# Cardo AI - The operating system for Asset Based Finance > Generated by All in One SEO Pro v5.0.2, this is an llms-full.txt file, used by LLMs to index the site. Cardo AI provides the core operating system for Asset Based Finance, delivering critical infrastructure for data ingestion, risk management, and automated reporting for financial institutions. ## Pages ### [Home](https://cardoai.com/) **Published:** August 24, 2021 **Author:** michele **Content:** POWERING THE WORLD’S MOST SOPHISTICATED INVESTORS AND LENDERS # The operating system for asset-based finance Cardo AI replaces the spreadsheets, manual workflows, and disconnected tools slowing down ABF teams, with one platform that handles data, waterfalls, cash flows, and reporting across every structure you run. [Book a demo](https://cardoai.com/book-a-demo/) ![The operating system for asset-based finance](https://cardoai.com/wp-content/uploads/2026/07/home-3.png) ## What makes us different ### End-to-end solution From deal close to investor report on one platform. Your front and middle office share a single source of truth. No reconciliation, no version conflicts, no data gaps between teams. ### Broadest coverage 15+ structure types, 30+ asset classes, 600+ automated controls. Whether you’re running warehouse facilities, ABS, or forward flows, it’s already built in. ### Highest interoperability Plugs into the tools you already use. Sync with your core systems and push analytics-ready data directly into Snowflake or Databricks, no professional services required. Trusted by ![Trusted by Blackstone](https://cardoai.com/wp-content/uploads/2026/05/trusted-by-blackstone-1.svg) ![Azimut](https://cardoai.com/wp-content/uploads/2026/02/Azimut.svg) ![Citi](https://cardoai.com/wp-content/uploads/2026/02/Citi.svg) ![Fasanara](https://cardoai.com/wp-content/uploads/2026/02/Fasanara.svg) ![Encina Lender Finance](https://cardoai.com/wp-content/uploads/2026/05/trusted-by-encina.svg) ![JTC](https://cardoai.com/wp-content/uploads/2026/02/JTC-1.svg) ![Multitude Bank](https://cardoai.com/wp-content/uploads/2026/05/trusted-by-multitude-bank.svg) ![Auto1](https://cardoai.com/wp-content/uploads/2026/05/trusted-by-auto1.svg) ## Built for complex credit structures ### Structure types [Capital Markets](#capital-markets) Private ABS • Public ABS • CLOs • ABCP [Funding Facilities](#funding-facilities) Warehouse Facilities • Seasoning Facilities • Credit Facilities [Investment & Acquisition Structures](#investment-amp-acquisition-structures) Forward Flows • Alternative Investment Funds • Whole Loan Sales [Capital Relief & Risk Transfer](#capital-relief-amp-risk-transfer) CRTs • SRTs • Risk Transfer Transactions [Receivables Purchase Structures](#receivables-purchase-structures) RPPs • RPAs ### Asset classes [Consumer](#consumer) RMBS • Consumer Loans • Credit Cards • Student Loans • Auto Loans [Corporate](#corporate) CMBS • Trade Receivables • Leasing • Corporate Loans • SME Loans • Leveraged Loans [Financials](#financials) Subscription lines • NAV Financing [Alternative](#alternative) BNPL • Revenue-Based Finance • E-commerce Financing • Rent Tech Finance • Public Admin Receivables • Inventory Finance • Legal Claims [Non-performing](#non-performing) NPLs • UTPs • Opportunistic Credit ## Hear from our clients ![John Stecher](https://cardoai.com/wp-content/uploads/2026/03/Quote-John-Stecher-1.jpg "Quote-John-Stecher") At Blackstone, we have seen firsthand the rapid growth in alternative credit. To keep pace, the industry must move beyond spreadsheets and adopt scalable technology. Our Credit & Insurance team has been impressed by Cardo AI’s ability to manage complex portfolios. John Stecher Chief Technology Officer Blackstone ![Susan Yung](https://cardoai.com/wp-content/uploads/2026/03/Quote-Susan-Yung.jpg "Quote-Susan-Yung") When we set out to find a software solution that could handle the complexity of our illiquid credit portfolios, we struggled to find one that met our high standards for efficiency, scalability, and risk management — until we found Cardo Al. Susan Yung SMD Investment Operations Blackstone ![Alain Nydegger](https://cardoai.com/wp-content/uploads/2026/03/Quote-Alain-Nydegger-1.jpg "Quote-Alain-Nydegger") Consolidating our ABL activities onto a unified platform allows us to deliver faster responses with more flexible structures, while maintaining the transparency and audit-ready oversight that regulators and investors require. Alain Nydegger CEO of Wholesale Banking Multitude Group ![Manuela Franchi](https://cardoai.com/wp-content/uploads/2026/03/Quote-Manuela-Franchi-1.jpg "Quote-Manuela-Franchi") By combining do Value's expertise in credit management with Cardo Al's skills in developing financial artificial intelligence models, we can significantly improve asset quality and profitability in the banking system. Manuela Franchi CEO at doValue ![Alberto De Maggi](https://cardoai.com/wp-content/uploads/2026/03/Quote-Alberto-De-Maggi.jpg "Quote-Alberto-De-Maggi") Cardo Al helps us enhance our data processing capabilities, enabling the creation of advanced reports for securitization and other complex transactions. We are proud to have them as one of our partners. Alberto De Maggi Group COO and CEO Master Gardant ![Jeroen Sonsma](https://cardoai.com/wp-content/uploads/2026/03/Quote-Jeroen-Sonsma.jpg "Quote-Jeroen-Sonsma") Regulatory reporting is typically an obligation we would like to have outsourced to professionals, preventing risks of being non-compliant. Cardo Al's technology offers us the right value for money, making it easier for us to fully focus on our growth plans. Jeroen Sonsma CEO at Swishfund ![Geoff Beard](https://cardoai.com/wp-content/uploads/2026/03/Quote-Geoof-Beard-1.jpg "Quote-Geoof-Beard") Our partnership with Cardo AI represents a major step forward in the evolution of asset-based finance. By integrating technology-driven solutions into our investment and surveillance processes, we are accelerating transactions and enhancing overall market efficiency. Geoff Beard CEO and Chief Investment Officer at ELF ## Recognized by top-tier industry bodies [![](https://cardoai.com/wp-content/uploads/2026/07/19-buysiderisk50.svg "19-buysiderisk50")](https://cardoai.com/article/cardo-ai-ranked-in-chartis-buyside-risk50-2025/) [![](https://cardoai.com/wp-content/uploads/2026/07/25-quantitative-analytics50.svg "25-quantitative-analytics50")](https://cardoai.com/article/cardo-ai-ranked-in-chartis-buyside-risk50-2025/) [![Alternative Data Vendor of the year](https://cardoai.com/wp-content/uploads/2026/04/risk-markets-technology-awards.svg "risk markets technology awards")](https://cardoai.com/abf_desk/cardo-ai-named-alternative-data-vendor-of-the-year-by-the-risk-markets-technology-awards-2026-2/) [![Asset-based finance Service Provider of the Year](https://cardoai.com/wp-content/uploads/2026/04/sci.svg "sci")](https://cardoai.com/article/cardo-ai-named-service-provider-of-the-year/) [![Asset-based finance Data Management Insight Awards Europe](https://cardoai.com/wp-content/uploads/2026/04/data-management-insight-awards-europe.svg "data management insight awards europe")](https://cardoai.com/abf_desk/cardo-ai-named-best-data-management-solution-for-unstructured-data-by-at-the-data-management-insight-awards-europe-2025/) ## See how leading US credit investors use Cardo AI to scale their asset-based finance business faster [Book a demo](https://cardoai.com/book-a-demo/) --- ### [Book a demo](https://cardoai.com/book-a-demo/) **Published:** January 21, 2026 **Author:** sviluppatore **Content:** # Experience the power of Cardo AI See how Cardo AI mitigates risk, automates operations, and surfaces hidden vulnerabilities across your balance sheet and portfolios, on a demo built around your structures. ## How does it work? ![Discovery call (15 mins)](https://cardoai.com/wp-content/uploads/2026/03/Call.svg) ### Discovery call (10 mins) We learn about your business, your structures, and what you’re trying to solve, so the demo is relevant, not generic. ![Demo](https://cardoai.com/wp-content/uploads/2026/03/Demo.svg) ### Personalized demo (30 mins) A targeted walkthrough of exactly the products that fit your workflow: waterfalls, cash-flow modeling, reporting, and more. ## Book a demo Instagram This field is for validation purposes and should be left unchanged. 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For more information, check out our [Privacy Policy](https://cardoai.com/privacy-policy/).(Required) Book a demo Trusted by ![Auto1](https://cardoai.com/wp-content/uploads/2026/04/auto1-logo-grey.png) ![](https://cardoai.com/wp-content/uploads/2026/04/ap-logo-grey.png) ![](https://cardoai.com/wp-content/uploads/2026/04/gardant-logo-grey.png) ![JTC](https://cardoai.com/wp-content/uploads/2026/04/jtc-logo-grey.png) ![Trusted by Blackstone](https://cardoai.com/wp-content/uploads/2026/04/Blackstone-logo-grey-scaled.png) ![](https://cardoai.com/wp-content/uploads/2026/04/dovalue-logo-grey-scaled.png) ![](https://cardoai.com/wp-content/uploads/2026/04/roadsurfer-logo-grey-scaled.png) ![Azimut](https://cardoai.com/wp-content/uploads/2026/04/azimut-logo-grey.png) ![Encina Lender Finance](https://cardoai.com/wp-content/uploads/2026/04/encina-logo-grey-scaled.png) ![Multitude Bank](https://cardoai.com/wp-content/uploads/2026/04/multitude-bank-logo-grey.png) ![Citi](https://cardoai.com/wp-content/uploads/2026/04/citi-logo-grey.png) ![](https://cardoai.com/wp-content/uploads/2026/04/BancaValsabbina-logo-grey.png) ![Fasanara](https://cardoai.com/wp-content/uploads/2026/04/fasanara-logo-grey.png) ![](https://cardoai.com/wp-content/uploads/2026/04/all-reserved-logo-grey.png) 15+ Structure Types 30+ Asset Classes 600+ Automated Controls SOC2 Typed 2 Certified ![John Stecher](https://cardoai.com/wp-content/uploads/2026/03/Quote-John-Stecher-1.jpg "Quote-John-Stecher") At Blackstone, we have seen firsthand the rapid growth in alternative credit. To keep pace, the industry must move beyond spreadsheets and adopt scalable technology. Our Credit & Insurance team has been impressed by Cardo AI’s ability to manage complex portfolios. John Stecher Chief Technology Officer Blackstone ![Susan Yung](https://cardoai.com/wp-content/uploads/2026/03/Quote-Susan-Yung.jpg "Quote-Susan-Yung") When we set out to find a software solution that could handle the complexity of our illiquid credit portfolios, we struggled to find one that met our high standards for efficiency, scalability, and risk management — until we found Cardo Al. Susan Yung SMD Investment Operations Blackstone ![Alain Nydegger](https://cardoai.com/wp-content/uploads/2026/03/Quote-Alain-Nydegger-1.jpg "Quote-Alain-Nydegger") Consolidating our ABL activities onto a unified platform allows us to deliver faster responses with more flexible structures, while maintaining the transparency and audit-ready oversight that regulators and investors require. Alain Nydegger CEO of Wholesale Banking Multitude Group ![Manuela Franchi](https://cardoai.com/wp-content/uploads/2026/03/Quote-Manuela-Franchi-1.jpg "Quote-Manuela-Franchi") By combining do Value's expertise in credit management with Cardo Al's skills in developing financial artificial intelligence models, we can significantly improve asset quality and profitability in the banking system. Manuela Franchi CEO at doValue ![Alberto De Maggi](https://cardoai.com/wp-content/uploads/2026/03/Quote-Alberto-De-Maggi.jpg "Quote-Alberto-De-Maggi") Cardo Al helps us enhance our data processing capabilities, enabling the creation of advanced reports for securitization and other complex transactions. We are proud to have them as one of our partners. Alberto De Maggi Group COO and CEO Master Gardant ![Jeroen Sonsma](https://cardoai.com/wp-content/uploads/2026/03/Quote-Jeroen-Sonsma.jpg "Quote-Jeroen-Sonsma") Regulatory reporting is typically an obligation we would like to have outsourced to professionals, preventing risks of being non-compliant. Cardo Al's technology offers us the right value for money, making it easier for us to fully focus on our growth plans. Jeroen Sonsma CEO at Swishfund ![Geoff Beard](https://cardoai.com/wp-content/uploads/2026/03/Quote-Geoof-Beard-1.jpg "Quote-Geoof-Beard") Our partnership with Cardo AI represents a major step forward in the evolution of asset-based finance. By integrating technology-driven solutions into our investment and surveillance processes, we are accelerating transactions and enhancing overall market efficiency. Geoff Beard CEO and Chief Investment Officer at ELF REAL OUTCOMES ## What clients ship after onboarding 90% Reduction in due diligence time 60+ Jurisdictions automated for daily NAV Real-time Borrowing base validation ## Ready to test this on your own deal? Bring a live facility or portfolio. Leave with it actually modeled, ingested, and calculating in Cardo AI. [Schedule a Pilot](https://cardoai.com/deal-pilot/) --- ### [Waterfall management](https://cardoai.com/solutions/by-product/waterfall-management/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** WATERFALL MANAGEMENT # Execute payment waterfalls with contractual precision Turn collections into allocations across all interest, principal, and reserve triggers with versioned data ready for reporting. [Book a demo](https://cardoai.com/book-a-demo/) OUR SOLUTION ## Waterfalls that match the docs and explain themselves - [Bespoke customizable templates and structures](#bespoke-customizable-templates-and-structures) - [Deterministic waterfall attribution](#deterministic-waterfall-attribution) - [Trigger driven waterfall execution](#trigger-aware-execution) - [Full auditability, reconciliation tooling](#full-auditability-reconciliation-tooling) [Bespoke customizable templates and structures](#bespoke-customizable-templates-and-structures) ![Bespoke customizable templates and structures](https://cardoai.com/wp-content/uploads/2026/04/Waterfall-Management-our-solution-1.svg "Waterfall Management - our solution - 1") Waterfalls start from your structures to accelerate setup, while still allowing configuration for deal-specific mechanics like fees, caps, reserves, calendars, and priority order. This keeps implementation fast without forcing a “one-size-fits-all” model. Teams can standardize how they stand up waterfalls across deals, while still reflecting the exact structural details that matter for each transaction. [Deterministic waterfall attribution](#deterministic-waterfall-attribution) ![Deterministic waterfall attribution](https://cardoai.com/wp-content/uploads/2026/04/Waterfall-Management-our-solution-2.svg "Waterfall Management - our solution - 2") Cardo AI handles accruals and allocations across tranches/notes with full transparency. Interest, principal, shortfalls, and carry-forwards are explainable period by period. This replaces spreadsheet “mystery math” with repeatable logic stakeholders can review and rely on. [Trigger driven waterfall execution](#trigger-aware-execution) ![Trigger driven waterfall execution](https://cardoai.com/wp-content/uploads/2026/04/Waterfall-Management-our-solution-3.svg "Waterfall Management - our solution - 3") Our engine automates trigger driven behavior, including enforcement states, OC and IC logic, and structural switches. When conditions change, the system automatically shifts priorities and makes the cause of the change visible. Teams can see exactly why execution changed without manually reworking models mid cycle. [Full auditability, reconciliation tooling](#full-auditability-reconciliation-tooling) ![Full auditability, reconciliation tooling](https://cardoai.com/wp-content/uploads/2026/04/Waterfall-Management-our-solution-4.svg "Waterfall Management - our solution - 4") Runs are tracked with history and versioning, so results are auditable over time and differences between runs are explainable. When you need to compare against trustee or admin outputs, reconciliation tooling highlights differences quickly rather than forcing manual cell-by-cell comparisons. This supports independent validation while preserving a clean record of what was calculated, when, and under which waterfall configuration. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated USE CASES ## Operational waterfall execution, period after period 01 Monthly payment waterfall runs with full allocation detail and versioning. 02 Trustee and admin reconciliation to validate third-party calculations quickly. 03 Reserve funding logic and structural mechanics (fees, caps, step-ups). 04 Trigger-driven switches between waterfall modes based on deal performance. 05 Payment reports and notices generated from governed system outputs. ![Waterfall Management - Use Cases](https://cardoai.com/wp-content/uploads/2026/03/Waterfall-Management-Use-Cases.svg "Waterfall Management - Use Cases") ## Related resources Beyond Excel: Payment waterfall modeling simplified. [Read more](https://cardoai.com/what_s_new_in_produc/payment-waterfall-modeling-simplified/) Why Excel is the wrong home for a waterfall. Meet Waterfall Studio. [Watch video](https://cardoai.com/whats_new_in_product/why-excel-is-the-wrong-home-for-a-waterfall/) ABF cash flow modeling: Are there alternatives to Excel-and-Intex? [Read more](https://cardoai.com/reports/abf-cash-flow-modeling-are-there-alternatives-to-excel-and-intex/) ### Stop debating the waterfall. Make it a source of truth Book a demo to see contractual execution with full transparency. [Get started](https://cardoai.com/book-a-demo/) --- ### [Cash flow modeling](https://cardoai.com/solutions/by-product/cash-flow-modeling/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** Cash flow modeling # Underwrite, structure, and price deals with confidence Run scenario analysis across defaults, delinquencies, prepayments, recoveries, and reinvestment assumptions at asset level or by custom clusters. [Book a demo](https://cardoai.com/book-a-demo/) OUR SOLUTION ## A structuring and valuation engine for modern credit - [Asset cash flow generation](#asset-cash-flow-generation) - [Reinvestment and ramp-up modeling](#reinvestment-and-ramp-up-modeling) - [Liability structuring workbench](#liability-structuring-workbench) - [Tranche analytics with scenario comparisons](#tranche-analytics-with-scenario-comparisons) [Asset cash flow generation](#asset-cash-flow-generation) ![](https://cardoai.com/wp-content/uploads/2026/04/Cash-Flow-Modeling-our-solution-1.svg "Cash Flow Modeling - our solution - 1") Build custom asset pools using a library of specialized models. Generate cash flows across entire portfolios or specific clusters by layering in assumptions for defaults, delinquencies, prepayments, and recoveries. Our standardized projections eliminate the need for bespoke models, allowing your team to compare scenarios with total consistency. [Reinvestment and ramp-up modeling](#reinvestment-and-ramp-up-modeling) ![](https://cardoai.com/wp-content/uploads/2026/04/cash-flow-our-solution-2.svg "cash-flow-our-solution-2") Model reinvestment and ramp up using specialized replines and replenishment criteria. Cardo AI accounts for cash drag so projections reflect realistic deployment instead of idealized timing. This ensures every evaluation captures the true impact of portfolio build and reinvestment behavior on your bottom line. [Liability structuring workbench](#liability-structuring-workbench) ![](https://cardoai.com/wp-content/uploads/2026/04/Cash-Flow-Modeling-our-solution-3.svg "Cash Flow Modeling - our solution - 3") Leverage waterfall templates and parameter tuning to iterate quickly on tranche sizing, coupons, triggers, and credit enhancement. Adjust key parameters within a controlled workbench, keeping assumptions organized and reviewable. [Tranche analytics with scenario comparisons](#tranche-analytics-with-scenario-comparisons) ![](https://cardoai.com/wp-content/uploads/2026/04/Cash-Flow-Modeling-our-solution-4.svg "Cash Flow Modeling - our solution - 4") Outputs include tranche level analytics – IRR, MOIC, WAL, cash-on-cash, and fair value-style views, presented in a way that supports decision making. Scenario comparisons make tradeoffs clearer, so stakeholders can understand how risk/return shifts under different assumptions to support underwriting, structuring, and investment. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated USE CASES ## Built for underwriting, structuring, and pricing 01 Pre-deal tape cracking and valuation to underwrite new opportunities faster. 02 Sensitivity and scenario comparisons across multiple assumptions at once. 03 Tranche sizing and trigger tuning to see structural impact immediately. 04 Cluster-level modeling (e.g., geography, rating, borrower buckets) with differentiated assumptions. 05 Asset-to-waterfall integration to project cash flows and returns consistently. ![](https://cardoai.com/wp-content/uploads/2026/04/Cash-Flow-Modeling-Use-cases.svg "Cash Flow Modeling - Use cases") ## Related resources How cash flow simulation improves ABS portfolios. [Read more](https://cardoai.com/what_s_new_in_produc/how-cash-flow-simulation-improves-abs-portfolios/) ABF cash flow modeling: Are there alternatives to Excel-and-Intex? [Read more](https://cardoai.com/reports/abf-cash-flow-modeling-are-there-alternatives-to-excel-and-intex/) Cash flow modeling tool with live rate curves powered by Bloomberg Data [Read more](https://cardoai.com/announcements/cardo-ai-launches-cash-flow-modeling-tool-for-asset-based-finance-with-live-rate-curves-powered-by-bloomberg-data/) ### From tape to pricing without spreadsheet sprawl See how Cardo AI powers faster modeling with consistent assumptions and outputs. [Get started](https://cardoai.com/book-a-demo/) --- ### [Careers](https://cardoai.com/company/careers/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** CAREERS # Great challenges require new perspectives Join us in building intelligent technology for the asset-based finance market. [Explore roles](https://jobs.deel.com/cardo) ![](https://cardoai.com/wp-content/uploads/2026/04/career-1.png "career-1") ![](https://cardoai.com/wp-content/uploads/2026/04/career-2.png "career-2") WE ARE OUR CULTURE ## Discover our values ### Always improve We foster a culture of continuous learning and development. We provide the tools, mentorship, and guidance you need to reach your full potential. ### Caring for each other We are stronger together. We believe in the power of collaboration. Our supportive environment encourages teamwork, mentorship, and personal growth. ### Driven by excellence We hire the best talent and encourage innovation. We recognize and reward achievements to maintain a high-performing culture. ### Customer First We’re dedicated to delivering exceptional customer experiences. We actively seek feedback to improve our products, services, and interactions. ### Context over control We value transparency, trust, and clear goals. We incentivize each individual to take the lead on their activities and take ownership of their work. [![Lisa Wantig](https://cardoai.com/wp-content/uploads/2026/02/Lisa-Wantig.png)](#) READY TO JOIN US? ## Life at Cardo AI At any role and level, make your mark and achieve greater things as a team. Why we’re in Nova’s top 2% for talent density. [Read more →](https://cardoai.com/article/cardo-ai-ranks-in-the-top-2-for-talent-density/) ![](https://cardoai.com/wp-content/uploads/2026/03/Hybrid-work-model.svg) Hybrid work model Enjoy a flexible work-life balance with our hybrid model. Combine the productivity of the office with the comfort of home. ![](https://cardoai.com/wp-content/uploads/2026/03/Rewards-that-matter.svg) Rewards that matter Hard work is recognized and rewarded with competitive salaries, performance-based bonuses, and quarterly bonuses for top performers. ![](https://cardoai.com/wp-content/uploads/2026/03/Socialize-and-recharge.svg) Socialize and recharge Connect with colleagues at regular social events and enjoy amazing company retreats annually. ![](https://cardoai.com/wp-content/uploads/2026/03/Continuous-learning.svg) Continuous learning We foster a collaborative work environment through constructive feedback practices to develop your full potential. ![](https://cardoai.com/wp-content/uploads/2026/03/Share-the-success.svg) Share the success Participate in our stock option plan and share in our growth, directly benefiting from the company’s success. DIVERSITY AND INCLUSION ## We don’t just open doors to talents. We open them equally. Join a workplace that celebrates your skills and your story. Learn how we champion inclusivity through our [Gender Equality Plan.](https://cardoai.com/wp-content/uploads/2026/03/Gender-Equality-Plan-GEP-for-Cardo-AI-March-2024.pdf) ## Discover what we’re building, why it matters, and the people behind it. [See who we are](https://cardoai.com/about-us/) --- ### [Mobile App Privacy Policy](https://cardoai.com/mobile-app-privacy-policy/) **Published:** April 24, 2026 **Author:** RobertaCardo26 **Content:** # **Mobile App Privacy Policy** Version September 2026 ### **1. Introduction** This privacy notice explains how Cardo AI Corporation collects, uses, stores, and protects personal data when you use the Cardo AI Flow mobile application (“Flow” or “the app”). This notice is directed at registered users of the Flow app and applies wherever the app is available, including in the European Union (EU), the United Kingdom (UK), and the United States (US). Flow is intended for use by professionals in the asset-based finance (ABF) industry. The app is not directed at children under the age of 18. This notice was last updated in June 2026. We will notify you of any material changes before they take effect. ### **2. Controller Identity and Contact Details** Data Controller: Cardo AI Corporation Registered address: 4 World Trade Center, Suite 2909, New York, NY 10007, United States State of incorporation: Delaware, USA EU establishment: Via San Francesco d’Assisi 15, 20122 Milan, Italy (REA MI-2743017) Data Protection contact: dataprotection@cardoai.com Cardo AI Corporation is a US-based entity offering services to data subjects in the EU and UK. As such, EU and UK data protection law applies to the processing of your personal data by virtue of the extra-territorial scope of the GDPR (Article 3(2)) and the UK GDPR. ### **3. What Personal Data We Collect** 3.1 Account Registration Data** When you create an account, we collect: - Email address - First name, last name (optional, added via profile settings) - Company name (optional, added via profile settings) - Phone number (optional, added via profile settings) - Authentication method: email/password, or SSO via Google, Microsoft, or Apple Where you use Google, Microsoft, or Apple SSO to register or log in, we receive only your email address from those providers. We do not store tokens or other profile data from SSO providers beyond the session. **3.2 Onboarding Preferences** During onboarding, we ask you to provide investment preferences to personalise your experience: - Your role (buy-side or sell-side) - Target return appetite - Asset class preferences - Preferred deal size You can update or reset these preferences at any time in Settings > Update Preferences, or clear them entirely via Settings > Clear Preferences. **3.3 Usage and Behavioural Data** When you use the app, we automatically collect data about how you interact with it: - Articles read and time spent reading - Search queries entered - Deal filter selections - Feedback submitted on AI-generated summaries (thumbs up/down) - Push notification interactions - Session data (session duration, features accessed) This data is used to personalise your news feed and deal recommendations through our AI recommendation system (see Section 6). **3.4 “Get in Touch” Request Data** When you use the Get in Touch feature, we record your request alongside the profile data you have already provided (name, email, company, and phone number where supplied). No additional personal data is collected at the point of submission. **3.5 Technical and Device Data** We automatically collect standard technical data when you use the app, including: - IP address - Device type and operating system - App version - Session start and end times This data is used for security monitoring, fraud prevention, and app performance diagnostics. **3.6 Special Category Data** We do not knowingly collect any special category data (as defined under Article 9 GDPR) through the app. ### **4. How We Collect Your Data** **Collection method****Data collected**Directly from you at registrationName, email, company, phone number, authentication credentialsDirectly from you during onboardingRole, return appetite, asset class and deal size preferencesAutomatically during app useUsage data, behavioural data, technical/device dataVia SSO providers (Google, Microsoft, Apple)Email address onlyVia push notification service (Firebase)Push notification interaction data ### **5. Purposes and Legal Bases** **Purpose****Data categories used****Legal basis****Retention**Creating and managing your accountRegistration data, authentication dataArt. 6(1)(b) — contract performanceDuration of account + 6 monthsDelivering personalised news and deal contentOnboarding preferences, usage and behavioural dataArt. 6(1)(b) — contract performanceDuration of account + 6 monthsAI recommendation and personalisationRead history, feedback, onboarding preferencesArt. 6(1)(f) — legitimate interestsDuration of account + 6 monthsProcessing Get in Touch requestsProfile data, request recordArt. 6(1)(f) — legitimate interestsDuration of account + 6 monthsSecurity monitoring and fraud preventionTechnical data, IP address, session dataArt. 6(1)(f) — legitimate interests6 months from collectionApp performance and diagnosticsTechnical data, device dataArt. 6(1)(f) — legitimate interests6 months from collectionCompliance with legal obligationsAny data required by applicable lawArt. 6(1)(c) — legal obligationAs required by lawProduct analyticsUsage and interaction data, technical and device dataArt. 6(1)(a) — consent Duration of account + 6 months ### **6. Legitimate Interests** Where we rely on Article 6(1)(f) (legitimate interests) as our legal basis, we have carried out a balancing assessment for each purpose. A summary is set out below. **AI personalisation and recommendation** - Our legitimate interest is in providing a relevant, useful service to users by surfacing content matched to their professional interests. The processing uses behavioural data (read history, feedback) that users generate through ordinary use of the app. Users retain meaningful control: they can reset their preferences, clear their history, and delete their account at any time. The processing does not produce legal or similarly significant effects on users and is limited to content ordering within the app. **Get in Touch requests** - Our legitimate interest is in facilitating connections between ABF professionals and originators. The processing is limited to profile data the user has already provided and is directly aligned with the user’s intent in submitting the request. **Security monitoring and fraud prevention** - Our legitimate interest is in maintaining the security, integrity, and availability of the app and protecting our users from fraud and abuse. This processing involves technical data only and has minimal impact on users’ privacy. **App performance and diagnostics** - Our legitimate interest is in identifying and resolving technical issues to maintain a reliable service. Processing is limited to technical and device data and does not involve content the user has read or submitted. ### **7. AI Features** **7.1 Content Processing** Articles ingested via our news feeds are automatically processed by an AI pipeline that assigns categories, generates tags, and produces article summaries. This processing operates on article text only and does not involve your personal data. **7.2 Personalisation and Recommendations** Our recommendation system uses your onboarding preferences, read history, and feedback to rank and filter the content shown to you. This affects the order and selection of articles and deals displayed in your feed. This is not automated decision-making within the meaning of Article 22 GDPR, it does not produce legal effects or similarly significant effects on you. It is a content personalisation feature only. You can reset your personalisation at any time via Settings > Clear Preferences. You can also request full deletion of your account and associated data (see Section 11). **7.3 AI Infrastructure** AI features in the app are powered by AWS Bedrock (Claude Sonnet 4), operated by Amazon Web Services, Inc. AWS acts as a data processor on our behalf under a data processing agreement. Article text is processed via AWS Bedrock; personal data is not sent to the AI model. ### **8. Third-Party Processors and Recipients** We share data with the following processors, all of whom are bound by data processing agreements and are only permitted to process data in accordance with our instructions. **Processor****Purpose****Data shared****Location**Amazon Web Services (AWS)Cloud infrastructure hosting and AI processing (AWS Bedrock)All app data at rest and in transit; article text for AI processingUS and EU regionsGoogle (Firebase)Push notificationsPush notification tokens, interaction dataUnited StatesGoogleSSO authenticationEmail address (at login only)United StatesMicrosoftSSO authenticationEmail address (at login only)United StatesAppleSSO authenticationEmail address (at login only)United StatesPendo.io, Inc.Product analytics App usage and interaction events, visitor identifier, device and technical data (including approximate location derived from IP address) United StatesWe do not use paid third-party news data providers. News content is sourced via Google Alerts RSS feeds. RSS ingestion does not involve transmission of personal data to Google. ### **9. International Data Transfers** Cardo AI Corporation is based in the United States. Your personal data is processed on servers hosted by AWS in US and EU regions. Where data is transferred to or processed in the US from the EU or UK, the following transfer mechanisms apply: **Data subjects****Destination****Transfer mechanism**EU usersUnited States (AWS, Google, Microsoft, Apple, Pendo)Standard Contractual Clauses (SCCs) UK usersUnited States (AWS, Google, Microsoft, Apple, Pendo)Standard Contractual Clauses (SCCs) Our EU establishment in Milan, Italy is available as a point of contact for EU data subjects in relation to data protection matters. ### **10. How Long We Keep Your Data** **Data category****Retention period**Account and profile data (name, email, company, phone)Deleted on account deletion, or after 6 months of inactivityOnboarding preferencesDeleted with accountUsage and behavioural data (read history, feedback, searches)Deleted with accountProduct analytics data Duration of account + 6 months, or deleted on withdrawal of consentGet in Touch request recordsDeleted with account, or after 6 months of inactivityTechnical and device data6 months from collectionNews articles6 months from publication date (automated deletion)Anonymised anti-abuse record (hashed identifier only)2 years from account deletionData retained for legal complianceAs required by applicable lawWhere you request deletion of your account, we will complete the deletion process within 30 days of your request. An anonymised record (containing only a hashed identifier and a deletion timestamp, with no personal data) is retained for anti-abuse purposes to prevent re-registration abuse; this record cannot be used to identify you. ### **11. Your Rights** Depending on your location, you have the following rights in relation to your personal data: **Right****Description**Access (Art. 15)Request a copy of the personal data we hold about youRectification (Art. 16)Request correction of inaccurate or incomplete dataErasure (Art. 17)Request deletion of your personal data (see also Section 12)Restriction (Art. 18)Request that we limit how we process your data in certain circumstancesPortability (Art. 20)Receive your personal data in a structured, machine-readable formatObjection (Art. 21)Object to processing based on legitimate interestsRights re: automated decisions (Art. 22)We do not carry out automated decision-making with legal or significant effects. This right is not currently engaged.Withdraw consentYou can withdraw consent at any time via Settings > Privacy > Product Analytics. To exercise any of these rights, contact us at dataprotection@cardoai.com. We will respond within one calendar month of receiving your request. In complex cases we may extend this by a further two months, in which case we will notify you of the extension and the reason for it. We may ask you to verify your identity before processing your request. We will not charge a fee for reasonable requests. ### **12. Account Deletion** You can request deletion of your account and personal data at any time by: - Using the in-app deletion option: Profile > Account Settings > Delete Account - Emailing support@cardoai.com On deletion, your personal data (name, email, company, phone number, preferences, read history, and usage data) is permanently deleted. We retain an anonymised record for anti-abuse purposes only (see Section 10). Account deletion is a one-way process; if you wish to use the app again after deleting your account, you will need to create a new account. We will complete the deletion process within 30 days of your request. ### **13. Automated Decision-Making and Profiling** We do not carry out automated decision-making within the meaning of Article 22 GDPR that produces legal effects or similarly significant effects on you. Our AI personalisation features affect the ordering and selection of content displayed to you in the app. This does not constitute automated decision-making under Article 22 and has no effect beyond the content shown within the app itself. ### **14. Security** Cardo AI Corporation operates a security programme that includes: - SOC 2 Type II certification, audited annually - Encryption of data in transit (TLS) and at rest - Access controls and role-based permissions - Endpoint security monitoring - Annual penetration testing - Incident response procedures AWS infrastructure is hosted in ISO 27001-certified data centres. AWS Bedrock processes article text only and does not retain personal data beyond the processing request. ### **15. Cookies and Tracking** The Cardo AI Flow mobile app does not use browser cookies. We use Firebase (Google) to deliver push notifications, which involves the use of a push notification token associated with your device. You can disable push notifications at any time via your device settings. No advertising or retargeting technologies are used in the app. We use Pendo to understand how Flow is used so that we can improve it. Pendo acts as a data processor on our behalf under a data processing agreement. Pendo collects information about how you interact with the app, such as screens viewed, features used, and session information, together with technical information such as your device type, operating system, and approximate location derived from your IP address. Product analytics runs only if you consent. You can give or withdraw consent at any time in Settings > Privacy > Product Analytics. Withdrawing consent does not affect your access to Flow. Pendo’s privacy documentation is available at pendo.io/legal. ### **16. Changes to This Policy** We will notify you of material changes to this privacy notice via the app or by email before those changes take effect. Where changes affect how we process your personal data, we will take appropriate steps to ensure you are properly informed and, where legally required, obtain your consent or acknowledgement. The current version of this policy is always available within the app settings and at cardoai.com. ### **17. Supervisory Authorities and Complaints** You have the right to lodge a complaint about our processing of your personal data with a data protection supervisory authority. - **EU data subjects:** You may contact the supervisory authority in your EU member state of residence. A directory of EU supervisory authorities is available at [edpb.europa.eu](http://edpb.europa.eu). - **UK data subjects:** You may contact the Information Commissioner’s Office (ICO) at ico.org.uk or by post at: Information Commissioner’s Office, Wycliffe House, Water Lane, Wilmslow, Cheshire, SK9 5AF. - **US data subjects:** Please contact us directly at dataprotection@cardoai.com. Privacy rights under US state law vary by jurisdiction. We will respond to verifiable requests in accordance with applicable law. We would always encourage you to contact us directly in the first instance so we have the opportunity to address your concern before you escalate to a supervisory authority. ### **18. Contact Us** Cardo AI Corporation 4 World Trade Center, Suite 2909 New York, NY 10007 United States EU establishment: Via San Francesco d’Assisi 15, 20122 Milan, Italy Data protection enquiries: dataprotection@cardoai.com Account deletion / support: support@cardoai.com --- ### [Deal Data Audit Tool](https://cardoai.com/solutions/by-product/deal-data-audit-tool/) **Published:** September 8, 2026 **Author:** RobertaCardo26 **Content:** DEAL DATA AUDIT TOOL # Read the contract. Match the tape. Ship the deal Turn deal contracts and asset tapes into a source-cited, data-ready deal, in minutes. [Talk to us](https://cardoai.com/company/contact-us/) OUR SOLUTION ## Contract and tape, handled in one go - [Single-run ingestion](#single-run-ingestion) - [Deep extraction](#deep-extraction) - [Tape mapping](#tape-mapping) - [Source citation](#source-citation) [Single-run ingestion](#single-run-ingestion) ![](https://cardoai.com/wp-content/uploads/2026/09/Deal-data-solution-1.svg "Deal-data-solution-1") Reads the credit agreement or indenture once and returns the completed deal analysis template, instead of an analyst working through hundreds of pages by hand. What used to take days of manual entry happens in a single run. [Deep extraction](#deep-extraction) ![](https://cardoai.com/wp-content/uploads/2026/09/Deal-data-solution-2-2.svg "Deal-data-solution-2") Reconstructs complex covenants, eligibility rules, concentration caps, borrowing bases, and waterfall priorities. Even when conditions are split across ten different sections or buried in fine print, the platform captures every rule, state cap, and payment order in exact detail. [Tape mapping](#tape-mapping) ![](https://cardoai.com/wp-content/uploads/2026/09/Deal-data-solution-3-2.svg "Deal-data-solution-3") Normalizes disparate servicer files into your institutional data model automatically. Regardless of how individual servicers label their columns, loan-level balances, interest rates, scores, and delinquencies are instantly standardized and linked to your contract rules. [Source citation](#source-citation) ![](https://cardoai.com/wp-content/uploads/2026/09/Deal-data-solution-4.svg "Deal-data-solution-4") Anchors every extracted term, cap, and figure directly to its source text with page-level precision. Every entry in the deal template carries a clear paper trail, guaranteeing that no unverified data ever enters your investment models or decision-making. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated WHAT IT DELIVERS ## Complex deal terms, audit-ready data 01 **Accelerates deal execution Shrinks days of manual analyst review into a structured, data-ready model in minutes.** 02 **Verifies terms instantly** Click any extracted figure to view its source contract passage side by side, replacing tedious re-reading with instant proof. 03 **Connects contracts to live data** Applies extracted credit logic directly to normalized loan-level tape data across balances, rates, and delinquencies. 04 **Automates compliance trails** Generates a regulatory-grade audit trail automatically with every run, eliminating manual reporting overhead. ![](https://cardoai.com/wp-content/uploads/2026/09/deal-document-extraction-use-case-1.svg "deal-document-extraction-use-case") ## Related resources Are your ABF risk management analytics truly effective? [Read more](https://cardoai.com/reports/asset-backed-finance-risk-modeling/) Data lineage: full transparency on how your data becomes insight [Read more](https://cardoai.com/whats_new_in_product/data-lineage-full-transparency-on-how-your-data-becomes-insight/) Collateral integration agents: AI-powered loan tape onboarding [Read more](https://cardoai.com/whats_new_in_product/collateral-integration-agents/) ### Verify before you fund See how Cardo AI turns a stack of PDFs into a data-ready deal. [Get started](https://cardoai.com/book-a-demo/) --- ### [Blog](https://cardoai.com/blog/) **Published:** February 9, 2026 **Author:** sviluppatore **Content:** # Blog Cardo AI updates and emerging trends in ABF. Category All - [All](https://cardoai.com/blog/) - [Weekly Deals](https://cardoai.com/blog/weekly-deals/) - [Market](https://cardoai.com/blog/market/) - [Infrastructure](https://cardoai.com/blog/infrastructure/) - [Life at Cardo AI](https://cardoai.com/blog/life-at-cardo-ai/) [ ![](https://cardoai.com/wp-content/uploads/2026/09/weekly-roundup-19.png "weekly-roundup-19") - September 28, 2026 ### Weekly Deal Roundup | September 18–24, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-september-18-24-2026/) [ ![](https://cardoai.com/wp-content/uploads/2026/09/cardo-nova-talent.png "cardo-nova-talent") - September 22, 2026 ### Cardo AI is one of Italy’s ten most talent-dense scale-ups Read more](https://cardoai.com/article/cardo-ai-ranks-in-the-top-2-for-talent-density/) [ ![](https://cardoai.com/wp-content/uploads/2026/09/weekly-roundup-18.png "weekly-roundup-18") - September 21, 2026 ### Weekly Deal Roundup | September 14–18, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-september-14-18-2026/) [ ![](https://cardoai.com/wp-content/uploads/2026/09/weekly-roundup-17.png "weekly-roundup-17") - September 14, 2026 ### Weekly Deal Roundup | September 7–11, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-september-7-11-2026/) [ ![](https://cardoai.com/wp-content/uploads/2026/09/weekly-roundup-16.png "weekly-roundup-16") - September 7, 2026 ### Weekly Deal Roundup | September 1–4, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-september-1-4-2026/) [ ![](https://cardoai.com/wp-content/uploads/2026/08/weekly-roundup-15.png "weekly-roundup-15") - August 31, 2026 ### Weekly Deal Roundup | August 24–28, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-august-24-28-2026/) [ ![](https://cardoai.com/wp-content/uploads/2026/08/weekly-roundup-14.png "weekly-roundup-14") - August 24, 2026 ### Weekly Deal Roundup | August 17 – 21, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-august-17-21-2026/) [ ![Dates for an event: August 3–7, 2026, over a background of financial charts and data visualizations in grayscale.](https://cardoai.com/wp-content/uploads/2026/08/weekly-roundup-13.png "weekly-roundup-13") - August 10, 2026 ### Weekly Deal Roundup | August 3 – 7, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-august-3-7-2026/) [ ![Event banner showing dates July 27–31, 2026 over a dark financial chart background.](https://cardoai.com/wp-content/uploads/2026/08/weekly-roundup-12.png "weekly-roundup-12") - August 3, 2026 ### Weekly Deal Roundup | July 27–31, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-july-27-31-2026/) [ ![Event banner showing dates July 20–24, 2026 over a dark data/finance chart background](https://cardoai.com/wp-content/uploads/2026/07/weekly-roundup-11.png "weekly-roundup-11") - July 27, 2026 ### Weekly Deal Roundup | July 20 –24, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-july-20-24-2026/) [ ![Brain atop a silicon chip with circuits radiating outward, symbolizing AI and neural networks.](https://cardoai.com/wp-content/uploads/2026/07/tabular-foundation-model.png "tabular foundation model") - July 22, 2026 ### Beyond the Data Wall: Redefining Credit Strategy with Foundation Models Read more](https://cardoai.com/article/landscape-analysis-of-tabular-foundation-models/) [ ![Banner showing stock market graphs with a rounded badge reading July 13–17, 2026 in the center.](https://cardoai.com/wp-content/uploads/2026/07/weekly-roundup-10.png "weekly-roundup-10") - July 20, 2026 ### Weekly Deal Roundup | July 13 – 17, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-july-13-17-2026/) [ ![Weekly deal roundup](https://cardoai.com/wp-content/uploads/2026/07/weekly-roundup-9.png "weekly-roundup-9") - July 13, 2026 ### Weekly Deal Roundup | July 6 –10, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-july-6-10-2026/) [ ![Event banner showing dates June 29 – July 3, 2026 over a grayscale financial chart background.](https://cardoai.com/wp-content/uploads/2026/07/weekly-roundup-8.png "weekly-roundup-8") - July 6, 2026 ### Weekly Deal Roundup | June 29–July 3, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-june-29-july-3-2026/) [ ![Event dates June 22–25, 2026 displayed on a rounded pill over a dark data-visualization background.](https://cardoai.com/wp-content/uploads/2026/06/weekly-roundup-7.png "weekly-roundup-7") - June 29, 2026 ### Weekly Deal Roundup | June 22–25, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-june-22-25-2026/) [ ![Event banner displaying dates June 15–19, 2026 over a black-and-white data analytics background.](https://cardoai.com/wp-content/uploads/2026/06/weekly-roundup-6.png "weekly-roundup-6") - June 22, 2026 ### Weekly Deal Roundup | June 15–19, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-june-15-19-2026/) [ ![EU AI act](https://cardoai.com/wp-content/uploads/2026/06/EU-AI-act.png "EU AI act") - June 19, 2026 ### The EU AI Act now has teeth. Here’s what ABF teams should know Read more](https://cardoai.com/article/eu-ai-act-now-has-teeth/) [ ![Hero banner with a rounded, white-edged label reading 'AI MANIFESTO' over an abstract black-and-white circuit background.](https://cardoai.com/wp-content/uploads/2026/05/ai-manifesto.png "ai-manifesto") - June 17, 2026 ### AI Manifesto Read more](https://cardoai.com/article/ai-manifesto/) [ ![Stock market charts with a rounded banner reading June 8–11, 2026](https://cardoai.com/wp-content/uploads/2026/06/weekly-roundup-5.png "weekly-roundup-5") - June 15, 2026 ### Weekly Deal Roundup | June 8–11, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-june-8-11-2026/) [ ![Dates June 2–5, 2026 over a background of stock charts and financial data](https://cardoai.com/wp-content/uploads/2026/06/weekly-roundup-4.png "weekly-roundup-4") - June 9, 2026 ### Weekly Deal Roundup | June 2–5, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-june-2-5-2026/) [ ![Cardo AI Weekly Roundup 3](https://cardoai.com/wp-content/uploads/2026/06/weekly-roundup-3-1.png "weekly-roundup-3") - June 1, 2026 ### Weekly Deal Roundup | May 26–29, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-may-26-29-2026/) [ ![Dates May 18–21, 2026 shown on a rounded white pill overlay over a grayscale data/tech background.](https://cardoai.com/wp-content/uploads/2026/05/weekly-roundup-2.png "weekly-roundup-2") - May 25, 2026 ### Weekly Deal Roundup | May 18–21, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-may-18-21-2026/) [ ![Line-graph financial chart with fluctuating data and a rounded label reading May 11–15, 2026.](https://cardoai.com/wp-content/uploads/2026/05/weekly-roundup-1.png "weekly-roundup-1") - May 18, 2026 ### Weekly Deal Roundup | May 11–15, 2026 Read more](https://cardoai.com/article/weekly-deal-roundup-may-11-15-2026/) [ ![City skyline overlaid with stock market charts and candlestick lines suggesting financial data trends.](https://cardoai.com/wp-content/uploads/2026/05/buy-vs-build.png "buy-vs-build") - May 13, 2026 ### Buy vs. Build: why building ABF operating systems internally is harder than it looks Read more](https://cardoai.com/article/why-building-abf-operating-systems-internally-is-harder-than-it-looks/) [ ![](https://cardoai.com/wp-content/uploads/2026/04/building-trust-trough-transparency.png "building-trust-trough-transparency") - April 10, 2026 ### Building trust through transparency: introducing the Cardo AI Trust Centre Read more](https://cardoai.com/article/building-trust-through-transparency-introducing-the-cardo-ai-trust-centre/) [ ![Your data, your infrastructure](https://cardoai.com/wp-content/uploads/2026/03/your-data-your-infrastructure.png "your-data-your-infrastructure") - March 26, 2026 ### Building flexible data connectivity in asset-based finance Read more](https://cardoai.com/article/building-flexible-data-connectivity-in-asset-based-finance/) [ ![](https://cardoai.com/wp-content/uploads/2026/01/ABF-Desk-Echoes-of-the-past-1.png "ABF Desk-Echoes of the past") - January 26, 2026 ### Echoes of the past: FinTech lending, usury laws, and the shadow of the mortgage crisis Read more](https://cardoai.com/article/echoes-of-the-past-fintech-lending-usury-laws-and-the-shadow-of-the-mortgage-crisis/) [ ![risk markets technology awards](https://cardoai.com/wp-content/uploads/2025/11/alternative-data-vendor-1.png "alternative-data-vendor") - November 26, 2025 ### Cardo AI named “Alternative Data Vendor of the Year” Read more](https://cardoai.com/article/cardo-ai-named-alternative-data-vendor-of-the-year-by-the-risk-markets-technology-awards-2026-2/) [ ![](https://cardoai.com/wp-content/uploads/2025/11/best-data-management-solution-2.png "best-data-management-solution") - November 3, 2025 ### Cardo AI named “Best Data Management Solution for Unstructured Data” Read more](https://cardoai.com/article/cardo-ai-named-best-data-management-solution-for-unstructured-data-by-at-the-data-management-insight-awards-europe-2025/) [ ![](https://cardoai.com/wp-content/uploads/2025/10/sci-service-provider-of-the-year-1.png "sci-service-provider-of-the-year") - October 22, 2025 ### Cardo AI Named “Service Provider of the Year” Read more](https://cardoai.com/article/cardo-ai-named-service-provider-of-the-year/) [ ![](https://cardoai.com/wp-content/uploads/2025/08/ABF-Desk-Diversification-benefits-of-multi‑sector-Direct-Lending-portfolios-1.png "ABF Desk-Diversification benefits of multi‑sector Direct Lending portfolios") - August 21, 2025 ### Diversification benefits of multi‑sector Direct Lending portfolios Read more](https://cardoai.com/article/diversification-benefits-of-multi-sector-direct-lending-portfolios/) [ ![](https://cardoai.com/wp-content/uploads/2025/07/ABF-Desk-Underpriced-risk-oversized-returns-1.png "ABF Desk-Underpriced risk, oversized returns") - July 29, 2025 ### Underpriced risk, oversized returns: The investment case for emerging market consumer loans Read more](https://cardoai.com/article/underpriced-risk-oversized-returns-the-investment-case-for-emerging-market-consumer-loans/) [ ![](https://cardoai.com/wp-content/uploads/2025/07/ABF-Desk-How-is-Asset-Based-Finance-contributing-to-LP-portfolio-strategies-1.png "ABF Desk-How is Asset-Based Finance contributing to LP portfolio strategies") - July 9, 2025 ### How is Asset-Based Finance contributing to LP portfolio strategies? Read more](https://cardoai.com/article/how-is-asset-based-finance-contributing-to-lp-portfolio-strategies/) [ ![](https://cardoai.com/wp-content/uploads/2025/06/debt-portfolio-manager-1.png "debt-portfolio-manager") - June 16, 2025 ### Cardo AI Named “Debt Portfolio Management Technology of the Year” Read more](https://cardoai.com/article/cardo-ai-named-debt-portfolio-management-technology-of-the-year-by-the-drawdown-awards-2025/) [ ![Chartis BuySideRisk50 2025](https://cardoai.com/wp-content/uploads/2026/05/chartis-buysiderisk50.png "chartis-buysiderisk50") - May 6, 2025 ### Cardo AI ranked in Chartis BuySide Risk50 2025 Read more](https://cardoai.com/article/cardo-ai-ranked-in-chartis-buyside-risk50-2025/) [ ![](https://cardoai.com/wp-content/uploads/2025/03/ABF-Desk-Cardo-AI-is-DORA-compliant-2.png "ABF Desk-Cardo AI is DORA compliant") - March 25, 2025 ### Cardo AI is DORA compliant Read more](https://cardoai.com/article/cardo-ai-is-dora-compliant/) [ ![](https://cardoai.com/wp-content/uploads/2024/10/ABF-Desk-SRT-Tech-solutions-for-effective-risk-management-1.png "ABF Desk-SRT-Tech solutions for effective risk management") - October 29, 2024 ### SRT: Tech solutions for effective risk management Read more](https://cardoai.com/article/srt-tech-solutions-for-effective-risk-management/) [ ![](https://cardoai.com/wp-content/uploads/2024/06/ABF-Desk-Why-secure-infrastructures-matter-1.png "ABF Desk-Why secure infrastructures matter") - June 10, 2024 ### Why secure infrastructures matter: in-house vs outsourced solutions Read more](https://cardoai.com/article/why-secure-infrastructures-matter-in-house-vs-outsourced-solutions/) [ ![SOC2](https://cardoai.com/wp-content/uploads/2024/03/ABF-Desk-Cardo-AI-achieves-SOC2-Type-2-certification-2.png "ABF Desk-Cardo AI achieves SOC2 Type 2 certification") - March 19, 2024 ### Cardo AI achieves SOC2 Type 2 certification Read more](https://cardoai.com/article/cardo-ai-achieves-soc2-type-2-certification/) [ ![](https://cardoai.com/wp-content/uploads/2024/02/MSCA-1.png "MSCA") - February 19, 2024 ### Cardo AI proudly contributes to the MSCA Industrial Doctoral Network on Digital Finance Read more](https://cardoai.com/article/cardo-ai-proudly-contributes-to-the-msca-industrial-doctoral-network-on-digital-finance/) [ ![](https://cardoai.com/wp-content/uploads/2023/05/ABF-Desk-A-new-era-of-performance-Django-1.png "ABF Desk-A new era of performance-Django") - May 17, 2023 ### A new era of performance: Exploring asynchronous web development in Django Read more ](https://cardoai.com/article/a-new-era-of-performance-exploring-asynchronous-web-development-in-django-new/) --- ### [Cookie policy](https://cardoai.com/cookie-policy/) **Published:** March 27, 2026 **Author:** RobertaCardo26 **Content:** # **Cookie Policy** Version July 2026 ### **1. Introduction** This Cookie Policy explains how Cardo AI uses cookies, trackers and similar technologies, including browser local storage, across: - our website at cardoai.com; and - the Cardo AI platform, the authenticated application used by our clients. These are covered separately below, because they use different technologies for different purposes. This processing is carried out by the Cardo AI group as data controller. **What are cookies?** Cookies are small text files stored on your device by your browser, which may contain certain information, sometimes including personal data. Alongside cookies, applications may use other technologies that read or write information on your device, most commonly browser local storage, which serves a similar purpose but is not a cookie. Where this policy refers to “trackers”, it means cookies and these similar technologies together. ### **2. Types of trackers we use** We categorise trackers according to their purpose: - Necessary (technical and operational): required to operate and deliver the service and to keep you signed in securely. These are strictly necessary and cannot be switched off. - Preference: remember your settings, such as language preferences. - Statistical / analytical: help us understand how our website and platform are used so that we can improve them. In the platform tables below, these are identified by their consent group, either Analytics / Performance or Analytics / Functional. - Marketing: used in connection with our marketing activities. We also distinguish trackers we manage directly (first-party trackers) from those that enable services provided by a third party (third-party trackers). Unless otherwise stated in this policy, third-party providers may access the trackers they manage. Because the use of third-party trackers cannot be fully controlled by Cardo AI, any specific references to them should be considered indicative. For complete information, please consult the privacy policies of the third-party services named in this policy. ### **3. Cookies on our website (cardoai.com)** Our website uses necessary, preference, statistical and marketing cookies. A full and current list, showing each cookie’s name, category, purpose and duration, is available through the cookie preferences button in the bottom-left corner of any page on this website. You can grant or withdraw your consent for non-essential categories there at any time. ### **4. Cookies and similar technologies on the Cardo AI platform** The Cardo AI platform is the authenticated application used by our clients. 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Pendo stores information in your browser’s local storage rather than in cookies. **Name****Consent group****Purpose****Duration**\_pendo\_visitorIdAnalytics / PerformanceIdentifies you as a visitor so that usage activity can be attributed consistently across visits.Approximately 100 days\_pendo\_accountIdAnalytics / PerformanceAssociates usage events with your organisation’s account.Approximately 100 days\_pendo\_metaAnalytics / PerformanceStores a metadata hash used to detect changes to visitor and account information.Approximately 100 days\_pendo\_sessionIdAnalytics / PerformanceGroups usage events into a single analytics session.Session\_pendo\_utmAnalytics / PerformanceRecords the channel through which a visitor arrived, for attribution purposes.Persists with the visitor record\_pendo\_guides\_blockedAnalytics / FunctionalCaches whether an ad-blocker is preventing in-app guides from displaying correctly.Approximately 30 minutes, up to 4 hoursThe names above are shown in base form; as stored, each key also carries a suffix identifying our Pendo subscription. 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Pendo’s own cookie policy is available at www.pendo.io/legal/cookie-policy. **4.3 Error monitoring: Sentry We use Sentry for front-end error monitoring and performance reporting. The Sentry software does not store anything on your device: it sets no cookies and writes nothing to browser local storage or session storage. Session replay is not enabled. Error and performance events are transmitted to Sentry, which acts as a processor on our behalf. Sentry’s privacy policy is available at sentry.io/privacy.** **4.4 What we do not use** The Cardo AI platform does not use advertising cookies, marketing or tracking pixels, cross-site tracking, session-replay recording, or device fingerprinting. ### **5. How to manage your preferences** **Website** Use the cookie preferences button in the bottom-left corner of any page on cardoai.com to grant or withdraw consent for non-essential categories. **Cardo AI platform** Where you have not yet set a preference, a consent banner is shown within the platform, allowing you to accept only necessary cookies or to accept optional cookies as well. You can change your choice at any time from your account page at id..cardoaiapps.com, using the Consent Optional Cookies setting. You can also contact our Data Protection Officer at dpo@cardoai.com. **Browser and device settings** You can block or delete cookies, and clear local storage, through your browser or device settings. Please note that blocking necessary cookies will prevent you from signing in to the platform. You can find guidance on managing cookies for the most commonly used browsers at the following addresses: - Google Chrome - Mozilla Firefox - Apple Safari - Microsoft Edge - Brave - Opera You may also be able to manage certain categories of trackers through your device settings, for example the tracking or advertising settings on a mobile device. ### **6. Consent** Necessary trackers do not require your consent, as the service cannot be provided without them. Where consent is required, it can be freely withdrawn at any time using the methods described in section 5. You are free to decide whether or not to give consent. Where you do not, some features that rely on those trackers may be unavailable. Declining analytics trackers will not affect your access to the platform or the core functionality of the service. ### **7. Contact** This policy is issued by Cardo AI Corporation, 4 World Trade Center, Suite 2909, New York, NY 10007, United States, acting as data controller. If you have questions about this policy, or about how we use cookies and similar technologies, you can contact our Data Protection Officer at dpo@cardoai.com. ### **8. Change register** - 07/2026: Trackers used by the Cardo AI platform documented - 11/2025: Version issued. - 11/2024: Complete revision of the previous policy. **COOKIE LIST** **Necessary** Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. CookieDuration**Description**cookieyes-consent1 yearCookieYes sets this cookie to remember your consent preferences so they are respected on subsequent visits. It stores a consent identifier and your accept or reject choice for each cookie category on this site**Analytics** Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc. CookieDuration**Description**ajs\_anonymous\_id1 year 1 month 4 daysThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.ajs\_user\_idNever ExpiresThis cookie is set by Segment to help track visitor usage, events, target marketing, and also measure application performance and stability.**Uncategorised** Other uncategorised cookies are those that are being analysed and have not been classified into a category as yet. CookieDuration**Description**loom\_anon\_comment1 year 1 month 4 daysNo description available.loom\_referral\_videosessionDescription is currently not available.\_\_Host-psifi.analyticsTrace6 hoursDescription is currently not available.\_Host-psifi.analyticsTraceV26 hoursDescription is currently not available. --- ### [Portfolio Risk Evaluation](https://cardoai.com/solutions/by-product/portfolio-risk-evaluation/) **Published:** September 3, 2026 **Author:** GiadaCardo26 **Content:** PORTFOLIO RISK EVALUATION # Evaluate any portfolio, explain every result Stop waiting for defaults to confirm what the data already knew. See risk shift as it happens, with the reasons behind every score. [Book a demo](https://cardoai.com/book-a-demo/) OUR SOLUTION ## Default risk, scored and explained - [Alternative data scoring](#contractual-calculations) - [Explainable risk](#waterfall-execution-with-explainable-allocations) - [New asset class coverage](#trigger-and-covenant-monitoring) - [Live portfolio monitoring](#standardized-reporting-and-distribution) [Alternative data scoring](#contractual-calculations) ![](https://cardoai.com/wp-content/uploads/2026/09/portfolio-risk-evaluation-solution-1.svg "portfolio-risk-evaluation-solution-1") The tool combines each loan’s file with external series such as rates, inflation, energy consumption, and sector and regional data to produce a risk score. It handles the ingestion and transformation work needed to turn those public series into predictive signal, and determines which series are relevant for the specific portfolio being scored. [Explainable risk](#waterfall-execution-with-explainable-allocations) ![](https://cardoai.com/wp-content/uploads/2026/09/portfolio-risk-evaluation-solution-2.svg "portfolio-risk-evaluation-solution-2") Shows the factors pushing a loan’s risk up or down, what’s changed since the last look, and how it compares to similar loans in the same sector and region. A portfolio-wide view sits above the scores, queryable in plain language. [New asset class coverage](#trigger-and-covenant-monitoring) ![](https://cardoai.com/wp-content/uploads/2026/09/portfolio-risk-evaluation-solution-3.svg "portfolio-risk-evaluation-solution-3") Scores books with no performance history using a pretrained model that reads context instead of training on the portfolio’s own data. Tested on pilot books against a model built on full history, with results that held up. [Live portfolio monitoring](#standardized-reporting-and-distribution) ![](https://cardoai.com/wp-content/uploads/2026/09/portfolio-risk-evaluation-solution-4.svg "portfolio-risk-evaluation-solution-4") Tracks the portfolio and the market underneath it after a model goes live, flagging shifts in rates, sectors or regions as they happen rather than waiting for defaults to confirm them. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated USE CASES ## Loan-level risk, from score to portfolio view 01 **Score every loan in the portfolio** Generates a risk grade for every loan, combining the loan file with relevant alternative data, refreshed continuously. 02 **Break down what’s driving each score** Shows what’s pushing each score up or down, what’s changed, and how it compares to similar loans. 03 **Query the portfolio in plain language** Gives a single, queryable view across the book, so a portfolio manager gets answers without an analyst. 04 **Flag risk before defaults confirm it** Sends an alert when rates, sectors, or regions shift risk, while there’s still time to act. ![](https://cardoai.com/wp-content/uploads/2026/09/risk-portfolio-use-case.svg "risk-portfolio-use-case") ## Related resources doValue and Cardo AI together to innovate Stage 2 credit management [Read more](https://cardoai.com/announcements/ai-turns-sub-performing-loans-into-opportunities-dovalue-and-cardo-ai-together-to-innovate-stage-2-credit-management/) Are your ABF risk management analytics truly effective? [Read more](https://cardoai.com/reports/asset-backed-finance-risk-modeling/) Cardo AI named “Alternative Data Vendor of the Year” [Read more](https://cardoai.com/article/cardo-ai-named-alternative-data-vendor-of-the-year-by-the-risk-markets-technology-awards-2026-2/) ### Skip the repeat reviews, keep full coverage See how Cardo AI eliminates redundant reviews without letting a single default slip through. [Get started](https://cardoai.com/book-a-demo/) --- ### [Invoice Reconciliation](https://cardoai.com/solutions/by-product/invoice-reconciliation/) **Published:** September 1, 2026 **Author:** GiadaCardo26 **Content:** INVOICE RECONCILIATION # Reconcile every invoice to the tape, automatically Stop chasing discrepancies that don’t matter. Catch only the breaks that cost you money, in minutes. [Book a demo](https://cardoai.com/book-a-demo/) OUR SOLUTION ## Invoice reconciliation, made intelligent - [One engine, every invoice ingested](#configurable-connectors-and-pipelines) - [Six-tier precision, no manual coding](#simple-data-mapping) - [Templates that adapt automatically](#data-validation-at-scale) - [Human-in-the-loop, always](#document-extraction-with-traceability) [One engine, every invoice ingested](#configurable-connectors-and-pipelines) ![Any format in, one matched line out](https://cardoai.com/wp-content/uploads/2026/08/RECOX-1-one-engine.svg "RECOX-1-one-engine") Every invoice, in whatever format it arrives, flows through the same engine. Each one is matched to the tape line by line, with no manual re-keying. Analysts see a finished match, not a blank spreadsheet. [Six-tier precision, no manual coding](#simple-data-mapping) ![Every field knows how sure it is](https://cardoai.com/wp-content/uploads/2026/08/RECOX-2-sixtier.svg "RECOX-2-sixtier") Each field is scored across six tiers, from exact match to mismatch, so the system knows exactly how confident it is. Ambiguous fields get flagged for review; clear ones move straight through. No rules to write or code to maintain as edge cases appear. [Templates that adapt automatically](#data-validation-at-scale) ![The vendor changed the layout, nothing broke](https://cardoai.com/wp-content/uploads/2026/08/RECOX-3-templates.svg "RECOX-3-templates") Per-vendor templates absorb format changes the moment they happen. When a vendor updates its invoice layout, the system adjusts on the next batch. No engineering ticket, no broken pipeline, no lost week. [Human-in-the-loop, always](#document-extraction-with-traceability) ![The engine proposes. A person decides](https://cardoai.com/wp-content/uploads/2026/08/RECOX-4-human-on-the-loop.svg "RECOX-4-human-on-the-loop") The system proposes a match and explains its reasoning; a person makes the final call. Every approval and override is logged against the source document. Reviewers stay in control, and every decision feeds the next run. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated WHAT IT DELIVERS ## Every invoice, one system, no surprises 01 **From 200 to 1** Hours saved per week, 5K invoices . What used to take 200 analyst-hours a week chasing discrepancies now takes one. 02 **Enterprise-grade precision for 75% less spend** Instead of routing everything through one LLM, our multi-model pipeline uses fast models for easy data and heavy models only when needed. 03 **Prioritize by what actually moves the P&L** Every break is ranked by financial impact, not arrival order, ensuring your team catches what counts first. 04 **Defend any number, any time** Every approval and override is logged against the source, so you can pull up the exact evidence behind any decision. ![](https://cardoai.com/wp-content/uploads/2026/09/Invoice-reconciliation-use-case-1.svg "Invoice-reconciliation-use-case") ## Related resources Risk without accountability: The collapse of Tricolor and First Brands [Read more](https://cardoai.com/reports/navigating-redemption-pressure-in-private-credit-and-semi-liquid-funds/) Market Financial Solutions and the ABF ‘collateral-truth’ problem [Read more](https://cardoai.com/reports/market-financial-solutions-and-the-abf-collateral-truth-problem/) Data lineage: full transparency on how your data becomes insight [Read more](https://cardoai.com/whats_new_in_product/data-lineage-full-transparency-on-how-your-data-becomes-insight/) ### From invoice to signed-off decision, without the spreadsheet mess See how Cardo AI turns a week of transcription into an hour of review. [Get started](https://cardoai.com/book-a-demo/) --- ### [Multi-Agent Workflow Engine](https://cardoai.com/solutions/by-product/multi-agent-workflow-engine/) **Published:** July 23, 2026 **Author:** GiadaCardo26 **Content:** MULTI-AGENT WORKFLOW ENGINE # Ask like an analyst, answer like a quant Pairs natural language with Cardo AI’s quantitative engine, giving investment, risk, and portfolio teams instant access to advanced analytics. [Talk to us](https://cardoai.com/company/contact-us/) OUR SOLUTION ## Advanced ABF analytics, made conversational - [One interface, every tool orchestrated](#configurable-connectors-and-pipelines) - [Quantitative power, no code required](#simple-data-mapping) - [An adaptive workflow system](#data-validation-at-scale) - [Human-in-the-loop control](#document-extraction-with-traceability) [One interface, every tool orchestrated](#configurable-connectors-and-pipelines) ![](https://cardoai.com/wp-content/uploads/2026/07/SAM-1-multi-agent.svg "SAM-1-multi agent") It orchestrates specialized agents, models, and data pipelines behind a single conversational interface. Each part of a task goes to the right tool and the results come together into decision-ready output. One system, one question, every answer you need. [Quantitative power, no code required](#simple-data-mapping) ![](https://cardoai.com/wp-content/uploads/2026/07/SAM-2-quant-infrastructure.svg "SAM-2-quant infrastructure") Underneath the conversation sits Cardo AI’s quantitative engine. It includes stochastic models, machine-learning models, simulations, and scenario analysis. All of it works without writing a single line of code. Complex analytics, made conversational. [An adaptive workflow system](#data-validation-at-scale) ![](https://cardoai.com/wp-content/uploads/2026/07/SAM-3-Task-driven.svg "SAM-3-Task driven") Unlike rigid workflow systems, it doesn’t rely on a fixed sequence of steps. It interprets your objective first, then dynamically selects the right tools, agents, and workflows. This adaptability spans asset classes, structures, and transaction types. [Human-in-the-loop control](#document-extraction-with-traceability) ![](https://cardoai.com/wp-content/uploads/2026/07/SAM-4-human-in-the-loop.svg "SAM-4-human in the loop") This is a decision-support system, not a replacement for judgment. Human-in-the-loop review is embedded throughout. Teams validate assumptions, check outputs, and approve actions before decisions turn deal-critical. The result: automation and speed, paired with transparency and control. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated USE CASES ## One system. Every ABF challenge, solved 01 **Stress-test a pool before you commit** Run models, simulations, and stress scenarios to quantify outcomes and sensitivities before they materialize. 02 **Underwrite with covenant-level detail** Support underwriting, pricing, and covenant assessment from a single, consistent view of the deal. 03 **Catch risk with a recommendation engine** Detect anomalies and flag emerging risks, backed by stress-tested pricing and structure recommendations. 04 **Generate audit-ready reports on demand** Standardized, auditable outputs traceable to data, models, and assumptions that are consistent every time. ![](https://cardoai.com/wp-content/uploads/2026/07/sam-use-cases.svg "sam-use-cases") ## Related resources AI-Powered Document Reader for Credit Agreements [Read more](https://cardoai.com/whats_new_in_product/introducing-document-reader-for-credit-agreements/) Beyond the Data Wall: Redefining Credit Strategy with Foundation Models [Read more](https://cardoai.com/article/landscape-analysis-of-tabular-foundation-models/) Collateral integration agents: AI-powered loan tape onboarding [Read more](https://cardoai.com/whats_new_in_product/collateral-integration-agents/) [](#) ### A full quant team, at a fraction of the cost What once took many people and a stack of disconnected tools now runs in a single engine, cutting both cost and time. --- ### [Meet us](https://cardoai.com/resources/events/) **Published:** March 9, 2026 **Author:** sviluppatore **Content:** # Meet us Join us at an upcoming event to hear industry leaders share innovative insights and discuss the future of asset based finance. ## Podcast [ ![](https://cardoai.com/wp-content/uploads/2026/09/podcast-private-market.png "podcast-private-market") ### Get ABF Off Spreadsheets September 15, 2026 View](https://open.spotify.com/episode/1bZc25gOLBN0a3tQbEUvmX?si=946c33855aac403e&nd=1&dlsi=7426664886cd462d) [ ![](https://cardoai.com/wp-content/uploads/2026/06/podcast-dealcatalyst-cardo-ai.png "podcast-dealcatalyst-cardo-ai") ### Transparency, Technology, and the Next Frontier of Private Credit June 26, 2026 View](https://www.youtube.com/watch?v=6pcYC-rZgig) [ ![](https://cardoai.com/wp-content/uploads/2026/06/podcast-sfa-cardo-ai.png "podcast-sfa-cardo-ai") ### From Spreadsheets to AI: Modernizing Asset-Based Finance June 26, 2026 View](https://www.youtube.com/watch?v=V_b3d1ysNk8) ## Conferences and events [ ![TSI Congress Cardo AI](https://cardoai.com/wp-content/uploads/2025/10/Event-tsi.png "Event-tsi-congress-cardo-ai") ### TSI Congress 2026 October 1-2, 2026 Berlin, Germany](https://www.tsi-kongress.de/en/agenda) [ ![SCI Cardo AI](https://cardoai.com/wp-content/uploads/2025/10/Event-sci.png "Event-sci-cardo-ai") ### World Risk Sharing Week 2026 October 8, 2026 London, United Kingdom](https://www.events-sci.com/World-Risk-Sharing-Summit-2026) [ ![ABS East Cardo AI](https://cardoai.com/wp-content/uploads/2024/09/Event-abs-miami.png "Event-abs-miami") ### ABS East 2026 October 18-21, 2026 Fontainebleau, Miami](https://abseast.com) [ ![Opal Cardo AI](https://cardoai.com/wp-content/uploads/2024/12/Event-opal.png "Event-opal-group-cardo-ai") ### ABS & Fintech Specialty Finance Forum December 3-4, 2026 Dana Point, California](https://opalgroup.net/finance/abs-and-fintech-specialty-finance-forum-2026/) ## Let’s connect If you’re attending one the above conferences, we’ll be delighted to meet on the ground. Drop us a line at to set up a 1-on-1 meeting. --- ### [Transaction manager](https://cardoai.com/solutions/by-product/transaction-manager/) **Published:** January 21, 2026 **Author:** sviluppatore **Content:** TRANSACTION MANAGEMENT # Borrowing base automation, end-to-end Model transaction workflows from the legal docs, automate checks and calculations, and coordinate approvals across parties in one controlled system. [Book a demo](https://cardoai.com/book-a-demo/) OUR SOLUTION ## A unified engine for funding operations - [Configurable transaction model](#configurable-transaction-model) - [Eligibility and concentration testing](#eligibility-and-concentration-testing) - [Multi-step funding workflows](#multi-step-funding-workflows) - [Automated outputs](#auto-generated-outputs) [Configurable transaction model](#configurable-transaction-model) ![Deal model alignment](https://cardoai.com/wp-content/uploads/2026/04/Transaction-Manager-our-solution-1-2.svg "Transaction Manager - our solution - 1") Mirror your deals in a single, flexible system. Capture contractual provisions, deal mechanics, and key parameters without translating legal terms into spreadsheets. Versioning and effective dates keep the model aligned with real-time agreements, while updates flow automatically, no workflow rebuilds required. [Eligibility and concentration testing](#eligibility-and-concentration-testing) ![Borrowing base calculation](https://cardoai.com/wp-content/uploads/2026/04/Transaction-Manager-our-solution-2-1.svg "Transaction Manager - our solution - 2") Run eligibility checks and concentration rules at both loan and pool levels, including advance rates, exclusions, and haircuts. The resulting borrowing base is fully traceable to its inputs and the rules applied. This reduces the back-and-forth that happens when each stakeholder calculates “their version” of availability. [Multi-step funding workflows](#multi-step-funding-workflows) ![Controlled funding workflows](https://cardoai.com/wp-content/uploads/2026/04/Transaction-Manager-our-solution-3-1.svg "Transaction Manager - our solution - 3") Run funding events through controlled workflows with maker-checker controls, approvals, and audit logs. Funding events move through structured workflows, so approvals don’t get lost in email threads. Every step is logged, creating a clear audit trail that supports repeatable drawdowns. [Automated outputs](#auto-generated-outputs) ![Transaction Management - Automated output](https://cardoai.com/wp-content/uploads/2026/04/Transaction-Manager-our-solution-4-1.svg "Transaction Manager - our solution - 4") Cardo AI produces standardized outputs, borrowing base certificates, eligibility schedules, and reporting packages, ready in PDF, Excel, or via API. Every output is tied to its underlying calculations and rules, preserving lineage so reviewers can validate both the results and how they were generated, without manual reformatting each cycle. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated USE CASES ## Complete control over every funding event 01 Borrowing base automation: eligibility, concentrations, advance rates, availability/deficiency. 02 Drawdown request workflows with multi-party approvals and full traceability. 03 Forward flow purchase governance: automate inclusion/exclusion logic and maintain a single source of truth. 04 Third-party reconciliation: verify or reconcile external borrowing base and waterfall calculations. 05 Collaborative portal: notifications, document sharing, deadlines, and approvals in one place. ![](https://cardoai.com/wp-content/uploads/2026/03/Transaction-Manager-Use-Cases.svg "Transaction Manager - Use Cases") ## Related resources Trade faith with fact with our Transaction Manager. [Read more](https://cardoai.com/what_s_new_in_produc/trade-faith-with-fact-transaction-manager/) Maximize integrity in ABF transactions: explore our Data Validation. [Read more](https://cardoai.com/what_s_new_in_produc/maximize-integrity-in-abf-transactions-with-our-data-validation/) Speeding up due diligence for ABF investments. [Read more](https://cardoai.com/what_s_new_in_produc/speeding-up-due-diligence-for-abf-investments/) ### Make every funding event repeatable and auditable See how Cardo AI replaces spreadsheets with governed workflows your whole deal team can trust. [Get started](https://cardoai.com/book-a-demo/) --- ### [Asset managers](https://cardoai.com/case-studies/asset-managers/) **Published:** March 5, 2026 **Author:** sviluppatore **Content:** # Case Studies | Asset Managers How we help our clients grow their business Category Asset Managers - [Asset Managers](https://cardoai.com/case-studies/asset-managers/) - [Credit Originators](https://cardoai.com/case-studies/credit-originators/) - [Bank Lenders](https://cardoai.com/case-studies/bank-lenders/) - [Fund Administrators](< https://cardoai.com/case-studies/fund-administrators/>) ASSET MANAGER [Driving operational leverage in ABF operations](#driving-operational-leverage-in-abf-operations) [View Blackstone use case](https://share.hsforms.com/15IsV909NTw-cPsTNiIiD3w59ao2) **Challenge:** Managing complex portfolios across Direct Lending CLOs, ABF investments, and ABS tranches requires oversight of multiple SPVs, layered capital structures, and insurance and rating agency constraints. Multi-layer cash flow modelling for esoteric assets, fragmented data workflows, and manual reconciliations limited real-time visibility and slowed collaboration across teams. **Solution:** Cardo AI centralized all portfolio data across all SPVs into a unified, structure-aware model. The platform automated data ingestion from multiple sourcers, including issuers, trustees, and rating agencies, enabled seamless advance rate calculations across capital layers, and delivered real-time covenant and portfolio monitoring across strategies. **Outcome:** - 100BN+ loan cash flows onboarded, reconciled, and modeled - Automated advance rate calculations across capital pools - Real-time oversight across every SPV and strategy - Scalable infrastructure without operational strain [Managing diversified ABF funds across 120+ originators](#managing-diversified-abf-funds-across-120-originators) **Challenge:** Overseeing diversified ABF funds across more than 120 originators, asset classes, and geographies created limited transparency into over and deal level performance and risk. Loan-level data received in inconsistent formats, covenant monitoring was partially manual, and NAV calculations required significant operational effort as AUM scaled. **Solution:** Cardo AI centralized loan-level data across all originators into a standardized structure-aware model. The platform enabled real-time monitoring of fund structures and underlying collateral, automated covenant and limit tracking, and delivered daily shadow NAV calculations. Machine learning models enhanced risk forecasting through PD, LGD, EAD, and expected loss projections. **Outcome** - 2.5x assets managed with the same cost base - 4x more data leveraged for risk modeling - Fully automated LP reporting - Improved transparency and capital allocation decisions [Scaling credit facilities and forward flow management](#scaling-credit-facilities-and-forward-flow-management) **Challenge:** Managing high-frequency credit facilities and forward flow programs was an operational strain due to the constant churn of collateral. Reconciling borrower tapes, purchase schedules, and remittance files manually made it difficult to verify eligibility and borrowing base accuracy in real time. The team struggled to identify duplicates, reversals, or unauthorized substitutions, leading to increased concentration risk and funding delays as portfolio volumes scaled. **Solution** Cardo AI provided a dedicated facility management layer that linked raw collateral data directly to transaction-specific eligibility rules. The platform automated the calculation of advance rates and borrowing base availability while monitoring every asset addition and removal for consistency. By **r**econciling funding requests against expected portfolio movements, the system ensured that only eligible, validated assets were included in the financing pool. **Outcome** - Real-time validation of borrowing base and funding eligibility - Automated detection of duplicates, cancellations, and unusual movements - Significant acceleration of funding cycles and purchase schedules - Enhanced oversight of complex concentration limits and advance rates - Eliminated manual gaps between borrower reporting and lender requirements [CLO and structured finance regulatory compliance](#clo-and-structured-finance-regulatory-compliance) **Challenge:** ​​Evolving CLO and structured finance regulations required granular data access, ongoing monitoring, and scenario capabilities across multiple SPVs. Manual data retrieval and fragmented reporting processes slowed compliance workflows and limited the ability to test macro and micro stress scenarios efficiently. **Solution:** Cardo AI automated structure and loans data ingestion and standardized regulatory metrics across structures. The platform enabled real-time monitoring of performance and limits, configurable stress scenarios across economic and asset-level parameters, and pre-configured compliance dashboards aligned with regulatory frameworks. **Outcome:** - Full regulatory compliance automation - 10x increase in usable data - Zero manual time spent on data retrieval - New investment opportunities enabled without additional FTEs [High-volume BNPL loan reconciliation](#high-volume-bnpl-loan-reconciliation) **Challenge:** An asset manager purchasing US Buy-Now-Pay-Later loans direct from the originator needed to reconcile over 12MN loans daily cash flows while updating loan statuses based on complex contractual logic. Manual reconciliation processes created operational risk due to existing tools limitations, slowed reporting cycles, and limited scalability as transaction volumes increased. **Solution** Cardo AI implemented a fully automated reconciliation engine with AI-driven matching algorithms and rule-based loan classification logic. The system reconciled payments, outstanding balances, and contractual adjustments in real time, supported by dashboards and proactive alerts. **Outcome** - 100% accuracy in daily reconciliation - Faster processing times, from hours to minutes - Regulatory compliance with U.S. standards - Scalability as the business grows [Capital call and liquidity forecasting optimization](#capital-call-and-liquidity-forecasting-optimization) **Challenge:** A global asset manager faced inefficiencies in capital call execution and liquidity forecasting across private debt funds and securitization vehicles. Manual triggers and static forecasting models led to inaccurate cash flow projections and high administrative workload. **Solution** Cardo AI launched an automated capital call and liquidity management platform that scheduled executions dynamically and forecasted cash flow needs using predictive analytics. Liquidity buffers were optimized across multiple funding sources based on upcoming obligations and historical patterns. **Outcome** - 70% reduction in capital call processing time - Lower liquidity shortfalls and improved investor confidence - 30+ operational hours saved per week with optimized cash utilization [Automated portfolio oversight and collateral analytics](#automated-portfolio-oversight-and-collateral-analytics) **Challenge:** Managing diverse private credit strategies is hindered by fragmented data from multiple originators. Manual aggregation creates visibility gaps, making it difficult to monitor asset quality, detect performance drift, or ensure consistent compliance across complex collateral pools. **Solution** Cardo AI provided a dedicated collateral monitoring layer built to handle complex, large and esoteric asset populations. The platform ingested collateral data from multiple sources, standardized it into a governed structure, and applied automated monitoring logic to help teams maintain control over pool composition, quality, and integrity over time. **Outcome** - Rapid capital call execution via automated workflows - Strengthened fund stability through proactive liquidity and investor oversight - Significant time savings and improved resource allocation for strategic tasks [Independent trustee waterfall reconciliation](#independent-trustee-waterfall-reconciliation) **Challenge:** Ensuring the accuracy of complex waterfall distributions was a recurring bottleneck. Discrepancies between internal models and trustee reports arose from timing issues, data gaps, or differing structural interpretations. Small differences in principal, interest, or fees created operational delays and constant back-and-forth between stakeholders. It was difficult to quickly isolate breaks, identify the affected tranches, or determine if variances were data driven or structural. **Solution** Cardo AI provided an independent reconciliation layer that mirrored the transaction’s legal structure and cash flow logic. By utilizing underlying asset data and transaction terms, our platform calculated expected outcomes and compared them to trustee-reported results at a granular level. This allowed the team to move beyond high-level checks and precisely locate variances within interest, fees, reserves, or noteholder distributions. **Outcome** - Detected payment breaks immediately at the tranche and fee level - Reduced manual distribution check efforts drastically - Streamlined communication with trustees through quantified - variance reports - Established an independent, structure-aware shadow model for all distributions - Automated the identification of root-cause discrepancies between reporting cycles [Infrastructure and CMBS deal management](#infrastructure-and-cmbs-deal-management) **Challenge:** Managing infrastructure-backed transactions like data centers and cell towers was hindered by fragmented data from operators, trustees, and internal teams. The complexity of tracking long-duration contracted cash flows alongside debt service and reserves made it difficult to maintain a unified view of portfolio performance. Without a dedicated system, teams spent excessive time manually stitching together reporting packs to monitor covenants and utilization trends. **Solution** Cardo AI provided a dedicated operating layer for infrastructure-backed transactions, connecting the underlying asset data, the financing structure, and the recurring control workflows needed to manage the deal. By bringing together data ingestion, transaction monitoring, reconciliations, and analytics, our platform supported both daily operational oversight and broader portfolio management across infrastructure-style assets. **Outcome** - Unified view of asset-level performance and liability structures - Automated tracking of infrastructure covenants and reserve balances - Faster reconciliation of operator and trustee reporting cycles - Minimal errors by removing manual spreadsheet aggregation - Consistent reporting for lenders and investors from a single source [Pre-deal cash flow simulation](#pre-deal-cash-flow-simulation) **Challenge:** Our client’s pre-deal analysis was fragmented across disconnected spreadsheets, manual internal models, and static presentation materials. This lack of centralization made it difficult for their team to answer critical structuring questions quickly, such as how a deal would behave under severe downside scenarios or which specific assumptions had the greatest impact on noteholder returns. **Solution** Cardo AI provided the client with a unified cash flow simulation engine that combines granular collateral assumptions with complex structural terms in a purpose-built environment. Our platform enabled their team to simulate proposed waterfall structures, liabilities, and trigger packages before execution, ensuring all stakeholders worked from a single, consistent analytical foundation. **Outcome** - Enabled real-time simulation of base case, downside, and sensitivity scenarios - Reduced time-to-market by eliminating manual iteration and fragmented modeling workflows - Streamlined alignment between the client’s internal approvals, credit reviews, and investor discussions. [Document extraction and verification](#document-extraction-and-verification) **Challenge:** Our client struggled with the manual burden of parsing complex credit agreements and financing contracts. Extracting critical covenants, formulas, and reporting thresholds from hundreds of pages was slow and difficult to scale. This manual “search and find” process created significant bottlenecks during onboarding and increased the risk of missing key changes in amendments, leading to data silos between legal documentation and operational systems. **Solution** Cardo AI implemented an AI-assisted extraction and verification layer that converts dense legal text into structured, usable data. The platform automates the identification of eligibility rules and structural terms while maintaining a “human-in-the-loop” workflow for final validation. This ensures that only verified data feeds into the client’s downstream reporting and risk engines, bridging the gap between legal text and operational execution. **Outcome** - Drastically reduced the time required to map and structure new facility documentation - Automated the capture of complex dates, covenants, and financial formulas with high accuracy - Instantly identified changes and discrepancies across latest amendments and side letters - Enabled the team to manage a higher volume of documents without increasing manual headcount. See how our clients leverage Cardo AI to manage their most sophisticated asset-based structures at scale. [Explore our Deal Desk](https://cardoai.com/deal-desk/consumer-credit/) --- ### [Press](https://cardoai.com/resources/press/) **Published:** March 9, 2026 **Author:** sviluppatore **Content:** # Press and awards Everything you need to stay up to date with what’s going on at Cardo AI. [Talk to us](https://cardoai.com/company/contact-us/) ## Announcements [Cardo AI named Category Leader in Chartis 2026 RiskTech Quadrants® - July 27, 2026](https://cardoai.com/announcements/cardo-ai-named-among-category-leaders-in-chartis-2026-credit-risk-research/) [Cardo AI emerges as the defining ABF specialist in Chartis Research’s 2026 global rankings - July 1, 2026](https://cardoai.com/announcements/cardo-ai-emerges-as-the-defining-abf-specialist-in-chartis-researchs-2026-global-rankings/) [Cardo AI launches cash flow modeling tool for asset-based finance, with live rate curves powered by Bloomberg Data - June 8, 2026](https://cardoai.com/announcements/cardo-ai-launches-cash-flow-modeling-tool-for-asset-based-finance-with-live-rate-curves-powered-by-bloomberg-data/) [Cardo AI powers Multitude Bank’s asset-based lending - September 15, 2025](https://cardoai.com/announcements/cardo-ai-powers-multitude-banks-asset-based-lending/) [Cardo AI and Encina Lender Finance: Transforming Asset-Based Finance for Originators and Investors - February 12, 2025](https://cardoai.com/announcements/cardo-ai-encina-lender-finance-transforming-asset-based-finance-for-originators-and-investors/) [Load More](#) ## Press mentions [ ![](https://cardoai.com/wp-content/uploads/2026/03/press-abfjournal.png "press-abfjournal") Cardo AI Launches Cash Flow Modeling Tool for Asset-Based Finance](https://www.abfjournal.com/cardo-ai-launches-cash-flow-modeling-tool-for-asset-based-finance/) [ ![A-Team Insight logo with navy and orange triangles to the left and the text 'A‑Team' in orange with 'Insight' in navy blue.](https://cardoai.com/wp-content/uploads/2026/02/press-a-team-insight.png "press-a-team-insight") Cardo AI Q&A: Shining a Light on Private Markets](https://a-teaminsight.com/blog/cardo-ai-qa-shining-a-light-on-private-markets/?brand=ati) [ ![](https://cardoai.com/wp-content/uploads/2026/03/press-risk-net.png "press-risk-net") Markets Technology Awards 2026: March of the riskbots](https://www.risk.net/awards/7962650/markets-technology-awards-2026-march-of-the-riskbots) [ ![](https://cardoai.com/wp-content/uploads/2026/03/press-sci.png "press-sci") Established asset managers seek scale in European ABF](https://www.structuredcreditinvestor.com/talking-point/asset-backed-finance/84325/established-asset-managers-seek-scale-in-european-abf) [ ![](https://cardoai.com/wp-content/uploads/2026/03/press-sci.png "press-sci") ABF’s data dilemma: how tech talents are driving change into private credit](https://www.structuredcreditinvestor.com/news-analysis/asset-backed-finance/84240/sci-in-focus:-abfs-data-dilemma-part-2) [ ![](https://cardoai.com/wp-content/uploads/2026/03/press-fintech-innovation-lab.png "press-fintech-innovation-lab") FinTech Innovation Lab New York Announces 2025 Class](https://www.fintechinnovationlab.com/news/new-york/fintech-innovation-lab-new-york-announces-2025-class/) [Load More](#) ## Awards [![](https://cardoai.com/wp-content/uploads/2026/07/19-buysiderisk50.svg "19-buysiderisk50")](https://cardoai.com/announcements/cardo-ai-emerges-as-the-defining-abf-specialist-in-chartis-researchs-2026-global-rankings/) [![](https://cardoai.com/wp-content/uploads/2026/07/25-quantitative-analytics50.svg "25-quantitative-analytics50")](https://cardoai.com/announcements/cardo-ai-emerges-as-the-defining-abf-specialist-in-chartis-researchs-2026-global-rankings/) [![Alternative Data Vendor of the year](https://cardoai.com/wp-content/uploads/2026/04/risk-markets-technology-awards.svg "risk markets technology awards")](https://cardoai.com/abf_desk/cardo-ai-named-alternative-data-vendor-of-the-year-by-the-risk-markets-technology-awards-2026-2/) [![Asset-based finance Service Provider of the Year](https://cardoai.com/wp-content/uploads/2026/04/sci.svg "sci")](https://cardoai.com/article/cardo-ai-named-service-provider-of-the-year/) [![Asset-based finance Data Management Insight Awards Europe](https://cardoai.com/wp-content/uploads/2026/04/data-management-insight-awards-europe.svg "data management insight awards europe")](https://cardoai.com/abf_desk/cardo-ai-named-best-data-management-solution-for-unstructured-data-by-at-the-data-management-insight-awards-europe-2025/) ## Join our newsletter Get the latest asset-based finance trends and Cardo AI product updates straight to your inbox. By subscribing, you agree to our [Privacy Policy](https://cardoai.com/privacy-e-cookie-policy/) --- ### [About us](https://cardoai.com/company/about-us/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** WHAT WE STAND FOR # Built by credit experts. Trusted by the firms that set the standard. Cardo AI was founded on a simple observation: the most complex credit strategies in the world were still being managed in spreadsheets. We built the platform we couldn’t find – one that brings data, workflows, and decision-making into a single system, purpose-built for the operational demands of asset-based finance. [![Cardo AI - About us](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery.jpg)](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery.jpg) [![Cardo AI - About us](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-1.jpg)](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-1.jpg) [![Cardo AI - About us](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-2.jpg)](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-2.jpg) [![Cardo AI - About us](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-3.jpg)](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-3.jpg) [![Cardo AI - About us](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-4.jpg)](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-4.jpg) [![Cardo AI - About us](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-5.jpg)](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-5.jpg) ![Cardo AI - About us](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-349x349.jpg) ![Cardo AI - About us](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-2-349x349.jpg) ![Cardo AI - About us](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-3-349x349.jpg) ![Cardo AI - About us](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-1-349x347.jpg) ![Cardo AI - About us](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-5-349x349.jpg) ![Cardo AI - About us](https://cardoai.com/wp-content/uploads/2026/02/About-us-gallery-4-349x349.jpg) ONE TEAM, TWO SKILL SETS ## A unique blend of finance and tech expertise Our 130+ experts across five countries power the world’s most sophisticated investors and lenders. What makes us unique is how closely engineers and domain experts work together. Credit specialists shape the logic; engineers turn it into scalable, production-ready software. ![](https://cardoai.com/wp-content/uploads/2026/03/Engineering-34x34.png) Engineering Software, Data Engineering, Infrastructure, Data Science, DevOps/MLOps ![](https://cardoai.com/wp-content/uploads/2026/03/Domain-Experts-30x37.png) Domain Experts Credit Risk, Credit Pricing, ABS/Securitization, Direct Lending DRIVEN BY DECADES OF EXPERTISE ## Leadership team ![Altin Kadareja, Co-Founder and CEO Cardo ai](https://cardoai.com/wp-content/uploads/2026/03/leadership-group-altin.png "leadership-group-altin") [Altin Kadareja](https://www.linkedin.com/in/altinkadareja/) Co-Founder & CEO ![Euron Metaliaj, Cofounder and CTO Cardo AI](https://cardoai.com/wp-content/uploads/2026/03/leadership-group-euron.png "leadership-group-euron") [Euron Metaliaj](https://www.linkedin.com/in/euron-metaliaj-b745a761/) Co-Founder & CTO ![](https://cardoai.com/wp-content/uploads/2026/03/leadership-group-marco.png "leadership-group-marco") [Marco Masotto](https://www.linkedin.com/in/marco-masotto/) Head of Product ![Lisa Wantig - VP of Operations Cardo AI](https://cardoai.com/wp-content/uploads/2026/03/leadership-group-lisa.png "leadership-group-lisa") [Lisa Wäntig](https://www.linkedin.com/in/lisawaentig/) VP of Operations ## Powering the next era of ABF at scale Cardo AI is the core intelligence layer for asset-based finance, unifying deals, data, and reporting in one platform. $100B Volume Asset Under Technology 160+ Connectivity Active Integrations 30+ Collateral Asset Classes 6Tn+ Data Unique Data Points Trusted by ![Trusted by Blackstone](https://cardoai.com/wp-content/uploads/2026/05/trusted-by-blackstone-1.svg) ![Azimut](https://cardoai.com/wp-content/uploads/2026/02/Azimut.svg) ![Citi](https://cardoai.com/wp-content/uploads/2026/02/Citi.svg) ![Fasanara](https://cardoai.com/wp-content/uploads/2026/02/Fasanara.svg) ![Encina Lender Finance](https://cardoai.com/wp-content/uploads/2026/05/trusted-by-encina.svg) ![JTC](https://cardoai.com/wp-content/uploads/2026/02/JTC-1.svg) ![Multitude Bank](https://cardoai.com/wp-content/uploads/2026/05/trusted-by-multitude-bank.svg) ![Auto1](https://cardoai.com/wp-content/uploads/2026/05/trusted-by-auto1.svg) MAIN BENEFITS ## Think Asset-Based Finance, but… ![](https://cardoai.com/wp-content/uploads/2026/03/Faster.svg) Faster Spend less time on manual work and more time moving deals forward. Fewer bottlenecks, fewer mistakes. ![](https://cardoai.com/wp-content/uploads/2026/03/Simplified.svg) Simplified Bring everything into one place. Less back-and-forth, less complexity, and smoother day-to-day execution. ![](https://cardoai.com/wp-content/uploads/2026/03/Enhanced-by-AI.svg) Enhanced by AI More than a dashboard. Built-in analytics and AI help teams spot patterns and find new opportunities. RESEARCH [](https://cardoai.com/resources/research/) Our Blog [](https://cardoai.com/blog/) Careers [](https://cardoai.com/company/careers/) --- ### [Operations Teams](https://cardoai.com/solutions/by-role/operations-team/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** OPERATIONS TEAMS # From manual workflows to scalable operations Stop managing fragmented spreadsheets and start running your credit platform on a single, automated infrastructure. [Book a demo](https://cardoai.com/book-a-demo/) ![Operations Team](https://cardoai.com/wp-content/uploads/2026/04/Hero-Operations-Team.png "Hero-Operations Team") ## Create a controlled operating model across data, reporting, reconciliation, and multi-party workflows ![Operations Team](https://cardoai.com/wp-content/uploads/2026/04/Operations-team.svg "Operations-team") - Standardize the path from raw data to reporting: Bridge the gap between raw data and reporting to eliminate manual errors and ensure a single source of truth. - Automate multi-party workflows: Streamline communication between originators, services, and trustees to reduce friction within a single environment. - Centralize platform governance: Maintain control over user permissions, audit trails, and process compliance as your team and asset volumes grow. - Unify reporting and distribution: Generate consistent, investor-ready reports, removing the month-end reconciliation burden. ## Who we serve Cardo AI helps operations and finance teams move from fragmented reconciliations to a unified, controlled operating environment. Chief Operating Officer Head of Operations Operations Manager Reporting Manager Compliance Officer Fund Controller ## Core tools for seamless operational control [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/data-management/) Data Management Automate data ingestion, validation, and normalization from multiple sources. [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/loan-data-agent/) Loan Data Agent Consistent loan-level and performance data sourced directly from originators and servicers. [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/facility-management/) Facility Management Track or manage facilities, liabilities, draws, and repayments with consistent operational logic. [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/transaction-manager/) Transaction Management Run automated eligibility checks, concentrations, and availability calculations. [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/reg-investor-reporting/) Regulatory Reporting Generate audit-ready regulatory reports with full traceability back to source data. [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/reg-investor-reporting/) Investor Reporting Automate investor reporting with consistent, deal-ready outputs that reduce manual prep and follow-up. ## Eliminate operational drag Reduce manual work across reconciliations, reporting, subledger, and data workflows. [Get started](https://cardoai.com/book-a-demo/) --- ### [Asset manager](https://cardoai.com/solutions/by-business-type/asset-manager/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** ASSET MANAGERS (GPs), ASSET OWNERS (LPs) # Move faster on every deal. Build scale without adding headcount One platform to standardize data, streamline workflows, and digitize your operations. [Book a demo](https://cardoai.com/book-a-demo/) ## We help asset managers run complex and dynamic ABF strategies ![Portfolio dashboard for asset managers](https://cardoai.com/wp-content/uploads/2026/04/asset-manager-pic.svg "asset-manager-pic") ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Run multi-strategy books without multiplying ops complexity. Aggregate assets and structures to get a real-time cross-portfolio risk view, without building it manually. ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Stop taking originators’ data at face value. Automatically validate loan tapes against your benchmarks and flag discrepancies before they hit your models. ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Cut due diligence time by up to 90%. Standardize data ingestion, automate preliminary analysis, and generate investor-ready reports in hours, not weeks. ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Know your downside before the market tests it. Run advanced stress scenarios across your full book and quantify capital impact with precision. BUILT TO OUTPERFORM ## Hear from our clients ![](https://cardoai.com/wp-content/uploads/2026/03/Quote-John-Stecher.jpg) At Blackstone, we have seen firsthand the rapid growth in alternative credit. Our Credit & Insurance team has been impressed by Cardo AI’s ability to manage complex portfolios. John Stecher Chief Technology Officer at Blackstone ![](https://cardoai.com/wp-content/uploads/2026/03/Quote-Francesco-Filia.jpg) Global funding operations create real operational complexity. Cardo AI has been pivotal in turning large-scale data into actionable capital allocation decisions, helping us improve performance. Francesco Filia Founder & CEO at Fasanara Capital ![](https://cardoai.com/wp-content/uploads/2026/04/Quote-Geoof-Beard-150x150.png) Our partnership with Cardo AI represent a major step forward. By integrating technology-driven solutions into our investment and surveillance processes, we are accelerating transactions and enhancing overall market efficiency. Geoof Beard CEO & CIO at Encina Lender Finance OUR CAPABILITIES ## Explore the modern investment management stack [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/data-management/) Data Management Trust a single, validated data foundation across assets and deals. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/cash-flow-modeling/) Cash Flow Modeling Forecast cash flows to support pricing, allocation, and portfolio decisions. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/portfolio-management/) Portfolio Management Monitor performance, risk, and exposures across portfolios in real time. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/liquidity-ladder-management/) Liquidity Ladder Management Anticipate inflows and outflows to manage liquidity proactively. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/stress-testing/) Stress Testing Assess downside scenarios and capital resilience before risks materialize. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/waterfall-management/) Waterfall Management Model distributions accurately across tranches and scenarios. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/deal-surveillance/) Deal Surveillance Track covenant compliance and performance with early warning signals. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/insurance-reporting/) Insurance Reporting Produce insurance-ready reports aligned with regulatory and investor needs. WHY CARDO AI ## Purpose built for Asset Based Finance 1\. End-to-end solution Run the full investment lifecycle in one place - from onboarding and data checks to cash flows, allocations, monitoring, and investor-ready reporting - so your team spends less time reconciling and more time managing risk and returns. 2\. Broadest coverage Support a wide range of asset classes, strategies and investment structures (ABF, private credit, warehouses, forward flows, fund finance and more) without stitching together different tools for each deal type. 3\. Highest interoperability Connect cleanly to the data and systems you already use - trustees/agents, servicers, cash flow engines, portfolio accounting, and data providers - via API/SFTP, so you don’t redesign your operating stack. 4\. Secure and trusted Institutional-grade security with strong access controls and audit trails - built to stand up to investor scrutiny, internal controls, and regulatory expectations. ## Case studies Blackstone: Driving operational leverage in ABF operations. [Read more](https://cardoai.com/case-studies/asset-managers/) Managing diversified ABF funds across 120+ originators. [Read more](https://cardoai.com/case-studies/asset-managers) Scaling credit facilities and forward flow management. [Read more](https://cardoai.com/case-studies/asset-managers) ## Get in touch Let’s explore how we can support your portfolio management. [Get started](https://cardoai.com/book-a-demo/) --- ### [Cardo AI MCP Privacy Policy](https://cardoai.com/mcp-privacy-policy/) **Published:** July 1, 2026 **Author:** RobertaCardo26 **Content:** # **MCP Privacy Policy** Version July 2026 ### **1. Introduction** This Privacy Notice explains how Cardo AI processes personal data in connection with the Cardo AI MCP Connector (the “Connector”) — a read-only integration that enables users of the Anthropic Claude platform to query Cardo AI’s Prism and ABF/Equalizer services. This notice applies to authorised users of the Connector, typically employees of organisations that have subscribed to Cardo AI’s platform and enabled the Connector within their Claude environment. Cardo AI is committed to processing personal data in accordance with the EU General Data Protection Regulation (EU GDPR), UK GDPR, and the Data Protection Act 2018. ### **2. Controller Identity and Contact Details** **Legal Entity**Cardo AI Corporation, 4 World Trade Center, Suite 2909, New York, NY 10007, United States**Trading Name**Cardo AI**EU Representative (Art. 27 GDPR)**Cardo AI Corporation — Italy Branch, Via San Francesco d’Assisi 15, 20122 Milan, Italy (REA MI-2743017)**DPO Email**dpo@cardoai.com**DPO Postal Address**4 World Trade Center, Suite 2909, New York, NY 10007, United States### **3. What Personal Data We Collect** The Connector does not process the personal data of end users’ clients or counterparties. The data flows through the Connector are financial and operational in nature (deal data, portfolio data, document content) and do not constitute personal data. The only personal data processed in connection with the Connector is operational metadata generated automatically when an authorised user makes a request. This metadata is collected by Cardo AI’s own infrastructure and includes: - **Tool invocation data:** which Connector tool was called - **Request outcome:** whether the request succeeded or failed - **Performance data:** duration of the request in milliseconds - **Sanitised input parameters:** query parameters with sensitive values removed - **Error details:** error codes and messages where a request fails - **Progress messages:** basic status messages generated during processing These logs do not contain authentication credentials, but each request is authenticated and carries a tenant and correlation identifier that could be linked to an identifiable user through additional information held by Cardo AI. No special category data (as defined by Article 9 GDPR) is collected. No children’s data is collected. ### **4. How We Collect Your Data** Operational metadata is collected automatically by Cardo AI’s own infrastructure each time an authorised user makes a request through the Claude interface. No data is collected directly from users outside of this automated process. No cookies or browser-based tracking technologies are used. ### **5. Why We Use Your Data – Purposes and Legal Bases** **Purpose****Data Categories****Legal Basis****Retention**Service monitoring and reliability – to detect errors, monitor connector performance, and support debuggingTool invocation data, request outcome, performance data, error details, progress messagesArt. 6(1)(f) Legitimate interests – see Section 62 years### **6. Legitimate Interests Assessment** Where Cardo AI relies on legitimate interests (Article 6(1)(f) GDPR) as the legal basis for processing, the following assessment applies: **Purpose:** To monitor the operational health of the Connector, detect failures, and support debugging to maintain service quality for client organisations. **Necessity:** Operational logs are the minimum required to identify and resolve failures. Input parameters are sanitised before logging to remove sensitive content. No personal data from the underlying platform is retained. **Balancing:** The data subjects are authorised professional users acting in a business capacity, who have a reasonable expectation that their use of a B2B integration tool will be subject to operational monitoring. The volume and sensitivity of personal data processed is minimal. The impact on data subjects is low. The legitimate interest is not overridden by data subjects’ interests or fundamental rights. ### **7. Who We Share Your Data With** Cardo AI does not share personal data with any third-party processors in connection with the Connector. Operational logs are stored within Cardo AI’s own self-hosted infrastructure and do not leave Cardo AI’s systems. Cardo AI does not sell personal data. Personal data is not shared with Anthropic as part of the Connector’s operation — the data flows that pass through the Connector to Anthropic’s models do not contain personal data (see Section 3). ### **8. International Data Transfers** **Data Category****Retention Period****Basis for Period**Operational metadata (connector logs)2 yearsOperational necessity — minimum period required for real-time monitoring and debugging; automatically deleted thereafter### **9. How Long We Keep Your Data** **Data Category****Retention Period****Basis for Period**Operational metadata (connector logs)2 yearsOperational necessity — minimum period required for real-time monitoring and debugging; automatically deleted thereafter### **10. Your Rights** As a data subject, you have the following rights under EU GDPR / UK GDPR: **Right****Description**Access (Art. 15)Request a copy of the personal data we hold about youRectification (Art. 16)Request correction of inaccurate or incomplete dataErasure (Art. 17)Request deletion of your personal dataRestriction (Art. 18)Request that we limit how we process your data in certain circumstancesPortability (Art. 20)Receive your personal data in a structured, machine-readable format (not applicable to this processing activity )Objection (Art. 21)Object to processing based on legitimate interestsRights re: automated decisions (Art. 22)We do not carry out automated decision-making with legal or significant effects. This right is not currently engaged.Withdraw consentNot currently applicable — we do not rely on consent as a legal basis for any processing activityTo exercise any of these rights, contact our DPO at **dpo@cardoai.com**. We will respond within one calendar month of receipt of your request. This period may be extended by a further two months where requests are complex or numerous, with prior notification. ### **11. Automated Decision-Making and Profiling** Cardo AI does not carry out automated decision-making or profiling in connection with the Connector that produces legal or similarly significant effects on individuals. Article 22 GDPR does not apply to this processing activity. ### **12. Cookies and Tracking** The Connector does not use cookies or browser-based tracking technologies. The ePrivacy Directive does not apply to this processing activity. ### **13. Security** Cardo AI implements appropriate technical and organisational measures to protect personal data against unauthorised access, disclosure, alteration, or destruction. These include: - Encryption of data in transit (TLS 1.2+) and at rest - Role-based access controls and authentication via Keycloak SSO (OAuth 2.0) - Endpoint protection via CrowdStrike Falcon - Annual external penetration testing - Formal incident response and breach notification procedures - SOC 2 Type II attestation ### **14. Changes to This Policy** We will notify you of material changes to this privacy notice via email before those changes take effect. Where changes affect how we process your personal data, we will take appropriate steps to ensure you are properly informed and, where legally required, obtain your consent or acknowledgement. The current version of this notice is always available on the Cardo AI website. ### **15. Supervisory Authorities and Complaints** You have the right to lodge a complaint about our processing of your personal data with a data protection supervisory authority. - **EU data subjects:** You may contact the supervisory authority in your EU member state of residence. A directory of EU supervisory authorities is available at edpb.europa.eu. - **UK data subjects:** You may contact the Information Commissioner’s Office (ICO) at ico.org.uk or by post at: Information Commissioner’s Office, Wycliffe House, Water Lane, Wilmslow, Cheshire, SK9 5AF. - **US data subjects:** Please contact us directly at dpo@cardoai.com. Privacy rights under US state law vary by jurisdiction. We will respond to verifiable requests in accordance with applicable law. We would always encourage you to contact us directly in the first instance so we have the opportunity to address your concern before you escalate to a supervisory authority. ### **16. Contact Us** Cardo AI Corporation 4 World Trade Center, Suite 2909 New York, NY 10007 United States EU establishment: Via San Francesco d’Assisi 15, 20122 Milan, Italy Data protection enquiries: dpo@cardoai.com --- ### [Our AI](https://cardoai.com/company/our-ai/) **Published:** February 10, 2026 **Author:** sviluppatore **Content:** OUR AI # Make data your competitive edge with AI We embed AI across our platform, from data extraction to forecasting, and portfolio optimization, so you can move faster and drive more business. [Read our AI Manifesto](https://cardoai.com/article/ai-manifesto/) Elevating YOUR Performance ## From data to strategic insights ![](https://cardoai.com/wp-content/uploads/2026/03/Onboard-deals-faster-than-ever.svg "Onboard deals faster than ever") Onboard deals faster than ever AI helps turn loan tapes and deal documents into structured, searchable, decision-ready data. - **Document Intelligence:** Extracts covenants, triggers, and key terms from deal docs. - **Data Ingestion Copilot:** Maps, validates, and enriches deal data from multiple sources. ![](https://cardoai.com/wp-content/uploads/2026/03/Grow-your-portfolio-with-better-signals.svg "Grow your portfolio with better signals") Grow your portfolio with better signals AI helps connect patterns across deals, flags performance anomalies, and spots risks earlier. - **Automated loan bidding** based on risk/return and portfolio fit - **Cashflow forecasting** across different scenarios - **Default & prepayment prediction** using application and behavioural data. ![](https://cardoai.com/wp-content/uploads/2026/03/Stronger-risk-and-valuation-analytics.svg "Stronger risk and valuation analytics") Stronger risk and valuation analytics AI helps enhance risk models to price new collateral, detect early risk, and set more robust deal constraints. - **Shadow valuation** for emerging collateral - **Early risk signals** using macro indicators and alternative data - **Covenant & limit recommendations** from deal behavior and structure Built for trust, explainability, and control ## Industry-native AI, built for ABF [![Gennaro di brino](https://cardoai.com/wp-content/uploads/2026/02/Gennaro-di-brino.png)](#) Unlike generic AI layers, Cardo AI is native to asset based finance. It operates directly on deal data, documents, and structures, and embeds AI into the workflows you already use. For us, AI isn’t an add-on. It’s part of how our platform works. Every output is fully traceable, auditable, and configurable, so teams understand why a result was produced and remain in control of the decisions it informs. OUR AI APPROACH ## Experience the AI difference with us ![](https://cardoai.com/wp-content/uploads/2026/03/lente.svg "lente") Proprietary predictive models - Loan-level signals: default, prepayment, and delinquency risk, plus early-warning indicators and asset-level pricing - Portfolio analytics: aggregation, correlations, P&L distributions, VaR-style views, optimization, and stress scenarios - Market insight: clustering, market indicators, and spillover effects across sectors and regions ![](https://cardoai.com/wp-content/uploads/2026/03/science.svg "science") Data science, as a service When standard approaches fall short, our Data Science team builds models around your data and your objectives. We tailor the features, assumptions, and methodology to your assets and structure. ![](https://cardoai.com/wp-content/uploads/2026/03/Scalable.svg "Scalable") Scalable AI infrastructure Our AI runs on secure, scalable infrastructure designed for large data volumes, complex computations, and continuous monitoring. CARDO AI METHODOLOGY ## Our machine learning and quantitative finance methods Our machine learning and quantitative finance methods PROBABILISTIC classification survival analysis time series forecasting unsupervised machine learning monte carlo simulation stochastic processes ## Related resources Are your ABF risk management analytics truly effective? [Read more](https://cardoai.com/reports/asset-backed-finance-risk-modeling/) Leveraging Large Language Models at Cardo AI: a dual approach. [Read more](https://cardoai.com/reports/leveraging-language-models-at-cardo-ai-a-dual-approach/) AI powered document processing for Private Credit and ABF. [Read more](https://cardoai.com/what_s_new_in_produc/ai-powered-document-processing-for-private-credit-and-asset-based-finance/) ## Take a closer look at our tech solution [Book a demo](https://cardoai.com/book-a-demo/) --- ### [Account and data deletion](https://cardoai.com/account-and-data-deletion/) **Published:** April 24, 2026 **Author:** RobertaCardo26 **Content:** # **Account and data deletion** *This page explains how users of the Cardo AI Flow mobile application can delete their account or request deletion of their personal data, in accordance with our*[ *Privacy Policy*](https://cardoai.com/app-privacy-policy/Cardoai-flow-app) *and applicable data protection regulations.* ### **Option 1: Delete your account in-app** You can delete your account directly within the Cardo AI Flow app at any time. This is the fastest way to remove your account and all associated data. **Steps to delete your account in-app:** **Step 1:** Open the Cardo AI Flow application and log in to your account. **Step 2:** Navigate to Profile > Account Settings. **Step 3:** Select Delete Account and follow the on-screen confirmation prompts. **Step 4:** Once confirmed, your account deletion will be processed within 30 days. This action cannot be undone. Account deletion is permanent. Your profile, preferences, read history, and all associated personal data will be removed and cannot be recovered after deletion is confirmed. ### **Option 2: Request deletion via email** If you are unable to delete your account in-app, or if you wish to request deletion of specific personal data without deleting your full account, you can contact us by email. **Steps to request data deletion via email:** **Step 1:** Send an email to support@cardoai.com. **Step 2:** Use the subject line: *“Data Deletion Request – Cardo AI Flow”*. **Step 3:** Include your registered email address and a brief description of what you would like deleted (full account, specific data categories, etc.). **Step 4:** We will acknowledge your request and complete deletion within 30 days of receipt, in accordance with applicable data protection law. For broader data rights requests, including access, portability, restriction, or objection, contact our Data Protection Officer at **dpo@cardoai.com**. ### **What data is deleted?** Upon account deletion, the following personal data will be permanently removed: - Your user profile, login credentials, email address, and user ID - Optional profile details (name, company, phone number) if provided - Onboarding preferences (role, return appetite, asset class and deal size preferences) - Usage and behavioural data (read history, search queries, feedback, session data) - Get in Touch request records **Data retained after deletion:** A small amount of data is retained after account deletion for specific purposes: **Data****Retention period****Reason**Anonymised anti-abuse record (hashed identifier and deletion timestamp only, no personal data)2 years from deletionPreventing re-registration abuseTechnical and device dataUp to 6 months from collectionSecurity monitoring and diagnosticsData required by applicable lawAs required by lawLegal complianceThe anti-abuse record cannot be used to identify you, it contains only a hashed identifier and a deletion timestamp, with no personal data attached. ### **Your data rights** Under GDPR and UK GDPR, you have the following rights in relation to your personal data held by Cardo AI: - **Access**: request a copy of the data we hold about you - **Rectification**: request correction of inaccurate or incomplete data - **Erasure**: request deletion of your personal data - **Restriction**: request that we limit processing in certain circumstances - **Portability**: receive your data in a structured, machine-readable format - **Objection**: object to processing based on legitimate interests To exercise any of these rights, contact: **dpo@cardoai.com**. We will respond within one calendar month. ### **Further information** - **Flow App Privacy Policy:**[ cardoai.com/app-privacy-policy/Cardoai-flow-app](https://cardoai.com/mobile-app-privacy-policy/) - **Support:** support@cardoai.com - **Data Protection Officer:** dpo@cardoai.com --- ### [Bank lenders](https://cardoai.com/solutions/by-business-type/bank-lenders/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** BANK LENDERS # Power your lending with institutional-grade intelligence All the tools you need for faster deployment, real-time risk oversight, and automated facility management. [Book a demo](https://cardoai.com/book-a-demo/) BY YOUR SIDE, EVERY STEP OF THE WAY ## Confidence in every lending facility We help bank lenders make confident loan decisions by unifying borrower data, facility terms, and monitoring workflows in a single, auditable platform. ![](https://cardoai.com/wp-content/uploads/2026/04/Bank-lender-1.svg "Bank-lender") ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Turn fragmented borrower data into lender-grade intelligence Standardize, validate, and monitor borrower, collateral, and facility data to support consistent oversight across every line. ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Automate borrowing base and covenant compliance Track eligibility, concentration limits, advance rates, and covenant performance with timely monitoring and clear exception visibility on each borrower. ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Master complex portfolio oversight Manage warehouses, ABF facilities, and back-leveraged lines through a single control layer. No more manual reconciliation. ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Streamline audits with institutional-grade reporting Access granular, traceable data and automated audit trails to accelerate reporting and reduce operational risk. ![Alain Nydegger](https://cardoai.com/wp-content/uploads/2026/03/Quote-Alain-Nydegger.jpg "Quote-Alain-Nydegger") Consolidating our ABL activities onto a unified platform allows us to deliver faster responses with more flexible structures, while maintaining the transparency and audit-ready oversight that regulators and investors require. Alain Nydegger CEO of Wholesale Banking Multitude Group OUR CAPABILITIES ## The tech engine for modern bank lending [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/data-management/) Data Management Centralize and validate loan-level data across borrowers, facilities, and portfolios. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/loan-data-agent/) Loan Data Agent Automatically extract, reconcile, and enrich loan data from systems and documents. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/facility-management/) Facility Management Monitor facility availability, limits, and covenant compliance in real time. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/transaction-manager/) Transaction Management Track funding, repayments, and lifecycle events with full auditability. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/waterfall-management/) Waterfall Management Calculate interest, fees, and principal allocations in line with facility agreements. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/stress-testing/) Stress Testing Assess downside risk and capital resilience under forward-looking scenarios. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/deal-surveillance/) Deal Surveillance Monitor collateral, triggers, and covenant breaches in real time. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/reg-investor-reporting/) Regulatory Reporting Produce accurate, regulator-ready reports backed by auditable loan-level data WHY CARDO AI ## Purpose built for Asset Based Finance 1\. End-to-end solution Run the full investment lifecycle in one place - from onboarding and data checks to cash flows, allocations, monitoring, and investor-ready reporting - so your team spends less time reconciling and more time managing risk and returns. 2\. Broadest coverage Support a wide range of asset classes, strategies and investment structures (ABF, private credit, warehouses, forward flows, fund finance and more) without stitching together different tools for each deal type. 3\. Highest interoperability Connect cleanly to the data and systems you already use - trustees/agents, servicers, cash flow engines, portfolio accounting, and data providers - via API/SFTP, so you don’t redesign your operating stack. 4\. Secure and trusted Institutional-grade security with strong access controls and audit trails - built to stand up to investor scrutiny, internal controls, and regulatory expectations. ## Case studies Automating borrowing base verification and credit monitoring. [Read more](https://cardoai.com/case-studies/bank-lenders/) Accelerating capital call execution and cash flow accuracy. [Read more](https://cardoai.com/case-studies/bank-lenders/) Real-time risk monitoring and concentration control. [Read more](https://cardoai.com/case-studies/bank-lenders/) ## Get in touch Let’s explore how we can support your lending operations. [Get started](https://cardoai.com/book-a-demo/) --- ### [Credit originators](https://cardoai.com/solutions/by-business-type/credit-originators/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** CREDIT ORIGINATORS # Attract more funding and grow your origination All the tools you need for crafting deals faster, reaching more investors, and optimising workflows. [Book a demo](https://cardoai.com/book-a-demo/) [](#) BY YOUR SIDE, EVERY STEP OF THE WAY ## Confidence deal after deal We help originators execute faster by unifying loan-level data, deal terms, and funding workflows in a single operating platform. ![](https://cardoai.com/wp-content/uploads/2026/04/credit-originator-by-your-side-1.svg "credit-originator-by-your-side 1") ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Turn loan data into deal-ready tapes Standardize and validate once, reuse across facilities, lenders, and reporting cycles. ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Run borrowing base checks in real time Apply eligibility, concentrations, and advance rates continuously. ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Manage multiple funding lines with ease Track warehouses, ABF facilities, and private credit lines in parallel, no duplicate workflows. ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Accelerate diligence with investor-ready reporting Share consistent, traceable data in the exact formats investors expect to eliminate back-and-forth. OUR CAPABILITIES ## The tech engine for scalable credit origination [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/data-management/) Data Management Trust a single data foundation across assets and deals. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/loan-data-agent/) Loan Data Agent Extract, reconcile, and enrich loan data automatically. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/cash-flow-modeling/) Cash Flow Modeling Model deal cash flows with precision across scenarios and structures. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/facility-management/) Facility Management Automate facility funding, eligibility, and borrowing bases. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/transaction-manager/) Transaction Management Run automated eligibility checks, concentrations, and availability calculations. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/waterfall-management/) Waterfall Management Calculate interest, fees, and principal allocations in line with facility agreements. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/stress-testing/) Stress Testing Assess downside risk and capital resilience under forward-looking scenarios. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/reg-investor-reporting/) Investor Reporting Deliver accurate, investor-ready reports with auditable data. WHY CARDO AI ## Purpose built for Asset Based Finance 1\. End-to-end solution Run the full investment lifecycle in one place - from onboarding and data checks to cash flows, allocations, monitoring, and investor-ready reporting - so your team spends less time reconciling and more time managing risk and returns. 2\. Broadest coverage Support a wide range of asset classes, strategies and investment structures (ABF, private credit, warehouses, forward flows, fund finance and more) without stitching together different tools for each deal type. 3\. Highest interoperability Connect cleanly to the data and systems you already use - trustees/agents, servicers, cash flow engines, portfolio accounting, and data providers - via API/SFTP, so you don’t redesign your operating stack. 4\. Secure and trusted Institutional-grade security with strong access controls and audit trails - built to stand up to investor scrutiny, internal controls, and regulatory expectations. ## Case studies Streamlining due diligence to attract investors. [Read more](https://cardoai.com/case-studies/credit-originators/) Boosting transparency with automated investor reporting. [Read more](https://cardoai.com/case-studies/credit-originators/) Automating borrowing base calculations for faster borrower onboarding. [Read more](https://cardoai.com/case-studies/credit-originators/) ## Get in touch Let’s explore how we can streamline your loan origination. [Get started](https://cardoai.com/book-a-demo/) --- ### [Fund administrators](https://cardoai.com/solutions/by-business-type/fund-administrators/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** FUND ADMINISTRATORS, TRUSTEES, CREDIT SERVICERS # Grow beyond ops, modernize your services All the tools you need to scale through automation, streamline workflows, and deliver new services to your clients. [Book a demo](https://cardoai.com/book-a-demo/) BY YOUR SIDE, EVERY STEP OF THE WAY ## The monitoring, calculation, and reporting system for ABF Cardo AI helps fund administrators support ABF funds end to end by unifying loan-level data, deal terms, and accounting workflows in a single operating platform. ![](https://cardoai.com/wp-content/uploads/2026/04/Fund-administrator-by-your-side-1.svg "Fund-administrator-by-your-side 1") ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Portfolio visibility in one place Keep positions, cash flows, and performance at your fingertips, no more spreadsheets and inbox searches. ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Improve investor communication Give investors a shared hub for documents and self-serve reporting, fewer emails, smoother collaboration ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Get reporting right, every time Reduce costly errors with consistent data and accurate, ready-to-use reports. ![](https://cardoai.com/wp-content/uploads/2026/03/bullet.svg) Simplify end-to-end operations Run daily workflows and period-end cycles on one platform, across funds and structures. ![Manuela Franchi](https://cardoai.com/wp-content/uploads/2026/03/Quote-Manuela-Franchi.jpg "Quote-Manuela-Franchi") By combining do Value’s expertise in credit management with Cardo Al’s skills in developing financial artificial intelligence models, we can significantly improve asset quality and profitability in the banking system. Manuela Franchi CEO of doValue OUR CAPABILITIES ## The tech engine for scalable fund administration [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/data-management/) Data Management Trust a single, validated data foundation across assets and deals. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/portfolio-management/) Portfolio Management Monitor performance, exposures, and positions across portfolios in real time. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/waterfall-management/) Waterfall Management Automate distributions and allocations in line with fund and deal documentation. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/deal-surveillance/) Deal Surveillance Track covenant compliance, triggers, and performance with continuous controls. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/insurance-reporting/) Insurance Reporting Produce insurer-ready reports aligned with statutory and portfolio requirements. [![](https://cardoai.com/wp-content/uploads/2026/01/Freccia-Lilla.svg "Freccia Lilla")](https://cardoai.com/solutions/by-product/reg-investor-reporting/) Regulatory and Investor Reporting Deliver accurate, auditable reports that meet regulatory and investor standards. WHY CARDO AI ## Purpose built for Asset Based Finance 1\. End-to-end solution Run the full investment lifecycle in one place - from onboarding and data checks to cash flows, allocations, monitoring, and investor-ready reporting - so your team spends less time reconciling and more time managing risk and returns. 2\. Broadest coverage Support a wide range of asset classes, strategies and investment structures (ABF, private credit, warehouses, forward flows, fund finance and more) without stitching together different tools for each deal type. 3\. Highest interoperability Connect cleanly to the data and systems you already use - trustees/agents, servicers, cash flow engines, portfolio accounting, and data providers - via API/SFTP, so you don’t redesign your operating stack. 4\. Secure and trusted Institutional-grade security with strong access controls and audit trails - built to stand up to investor scrutiny, internal controls, and regulatory expectations. ## Case studies NAV calculation and regulatory reporting. [Read more](https://cardoai.com/case-studies/fund-administrators/) Managing esoteric private SPVs (Inventory Finance & Trade Receivables). [Read more](https://cardoai.com/case-studies/fund-administrators/) Advanced analytics & Stage 2 loan optimization. [Read more](https://cardoai.com/case-studies/fund-administrators/) ## Get in touch Let’s explore how we can support your fund operations. [Get started](https://cardoai.com/book-a-demo/) --- ### [Investment teams](https://cardoai.com/solutions/by-role/investment-team/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** INVESTMENT TEAMS # From raw loan tapes to real-time strategy Connect scattered collateral-level data to portfolio strategy, so investment teams can underwrite, monitor, and allocate capital with greater speed and confidence. [Book a demo](https://cardoai.com/book-a-demo/) ![](https://cardoai.com/wp-content/uploads/2026/03/Hero-Investment-Team-image.png "Hero-Investment Team-image") ## Scale with greater visibility and confidence across your credit platform ![Investment teams](https://cardoai.com/wp-content/uploads/2026/04/Investment-team.svg "Investment-team") - Unify bottom-up data with top-down strategy: Link assets to fund and portfolio views to ensure performance consistently supports your investment thesis. - Analyze exposures and concentrations at scale: Instantly visualize exposures across originators and asset classes, without manual spreadsheet reconciliation. - Model cash flow, runoff, and reinvestment: Model asset cash generation with precision to ensure liquidity always matches your strategic targets. - Execute consistent stress analysis: Quantify downside risk by seeing exactly how strategy-level shifts ripple through underlying collateral. ## Who we serve Cardo AI helps investment leaders move from manual data oversight to scalable, automated platform governance. Chief Investment Officer Investment Committee Head of Investments Investment Director Portfolio Manager Investment Associate Investment Analyst ## Advanced tools for active portfolio management [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/deal-surveillance/) Deal Surveillance Track post-close performance against underwriting assumptions and identify change early. [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/cash-flow-modeling/) Cash Flow Modeling Run pricing, structuring, base case, downside, and stress scenarios with greater consistency. [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/stress-testing/) Stress Testing Assess sensitivity to defaults, recoveries, prepayments, and other performance shocks. [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/liquidity-ladder-management/) Liquidity Ladder Management Support forward-looking liquidity planning and allocation decisions. [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/portfolio-management/) Portfolio Management Monitor exposures, returns, concentrations, and portfolio-level risk across transactions. [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/data-management/) Data Management Ensure that analysis is built on trusted, current, and standardised inputs. ## See the trade-offs clearly Compare base, downside, and stress cases with consistent inputs, so allocation decisions are faster and underwriting discipline is easier. [Get started](https://cardoai.com/book-a-demo/) --- ### [Risk team](https://cardoai.com/solutions/by-role/risk-team/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** RISK TEAMS # Turn deal complexity into operational clarity Monitor facilities, collateral, triggers, and exceptions in one environment built for structured credit and private asset portfolios. [Book a demo](https://cardoai.com/book-a-demo/) ![](https://cardoai.com/wp-content/uploads/2026/04/Hero-Risk-Team-1.png "Hero-Risk Team") ## Manage transaction complexity, monitor risk continuously, and stay ahead of exceptions ![Risk teams](https://cardoai.com/wp-content/uploads/2026/04/Risk-team.svg "Risk-team") - Accelerate transaction onboarding: Onboard facilities into a consistent control framework with structured data, eligibility logic, and automated monitoring. - Automate facility and covenant tracking: Maintain oversight of collateral, triggers, and performance with real-time monitoring of assets and liabilities. - Identify breaches and emerging issues early: Stay ahead of emerging risks with automated surveillance that flags outliers and breaches the moment they occur. - Standardize stress testing and analytics: Run complex scenario analyses and stress tests with institutional-grade consistency to quantify downside risk. ## Who we serve Cardo AI helps risk and control teams move from reactive monitoring to consistent, forward-looking oversight. Chief Risk Officer Head of Risk Risk Committee Risk Manager Risk Analyst Treasury Portfolio Oversight ## Core risk tools for continuous portfolio oversight [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/transaction-manager/) Transaction Management Run automated eligibility checks, concentrations, and availability calculations. [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/deal-surveillance/) Deal Surveillance Monitor collateral behaviour, covenants, triggers, and performance trends. [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/waterfall-management/) Waterfall Management Automate distributions and allocations in line with fund and deal documentation. [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/facility-management/) Facility Management Track covenants, limits, utilization, and reporting across facilities in real time. [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/stress-testing/) Stress Testing Quantify downside risks under diverse performance and forward-looking market scenarios. [![](https://cardoai.com/wp-content/uploads/2026/01/freccia-azzurra.svg)](https://cardoai.com/solutions/by-product/liquidity-ladder-management/) Liquidity Ladder Management Map inflows and outflows over time to identify liquidity pressure and funding mismatches. ## Keep risk signals clear and actionable Learn how Cardo AI helps Risk teams monitor limits, controls, and exceptions in one consistent view. [Get started](https://cardoai.com/book-a-demo/) --- ### [Stress testing](https://cardoai.com/solutions/by-product/stress-testing/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** Stress testing # Stress the deal, analyze the outcome Run repeatable performance and liquidity stresses using governed scenario libraries and waterfall driven results. [Book a demo](https://cardoai.com/book-a-demo/) OUR SOLUTION ## Consistent and explainable stress testing - [Unified assumption management](#unified-assumption-management) - [Apply scenarios by key segments or clusters](#apply-scenarios-by-key-segments-or-clusters) - [Waterfall integrated stress analysis](#waterfall-integrated-stress-analysis) - [Side by side scenario comparisons](#side-by-side-scenario-comparisons) [Unified assumption management](#unified-assumption-management) ![Unified assumption management](https://cardoai.com/wp-content/uploads/2026/04/Stress-Testing-our-solution-1-1.svg "Stress Testing - our solution - 1") Stress testing with centralized control of macro variables and asset performance drivers. Governance and versioning ensure every run is documented, repeatable, and comparable, creating a consistent framework that works across deals and stakeholders. [Apply scenarios by key segments or clusters](#apply-scenarios-by-key-segments-or-clusters) ![Stress Testing - Cardo AI](https://cardoai.com/wp-content/uploads/2026/04/Stress-Testing-our-solution-2-1.svg "Stress Testing - our solution - 2") Apply scenarios by rating, geography, sector, vintage, or custom clusters, ensuring shocks are targeted where they matter most. This avoids generic assumptions, uncovers concentration risks and provides more accurate downside analysis. [Waterfall integrated stress analysis](#waterfall-integrated-stress-analysis) ![Waterfall aware results](https://cardoai.com/wp-content/uploads/2026/04/Stress-Testing-our-solution-3-1.svg "Stress Testing - our solution - 3") Stress outputs are waterfall aware and account for triggers, switches, reserves, and structural protections. Results highlight loss magnitude and show how cash flow shortfalls affect the structure, revealing where protections hold or fail. [Side by side scenario comparisons](#side-by-side-scenario-comparisons) ![Scenario comparison](https://cardoai.com/wp-content/uploads/2026/04/Stress-Testing-our-solution-4-1.svg "Stress Testing - our solution - 4") Compare scenarios side by side with driver level explanations that link shifts in assumptions directly to tranche performance. Easily identify what changed and why, empowering your team to make faster, better informed decisions through clear and actionable insights. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated USE CASES ## Multiple scenarios you can repeat and manage 01 API-enabled stress testing and liability simulations for governed cash flow modelling at scale. 02 Structural breakpoints: when triggers switch and why returns change. 03 Liquidity stress based on expected vs. realized cash flows and obligations. 04 IC-ready outputs for risk discussions and decision logs. 05 Repeatable scenario libraries for consistent testing across deals. ![Stress testing - Use cases](https://cardoai.com/wp-content/uploads/2026/03/Stress-testing-Use-cases.svg "Stress testing - Use cases") ## Related resources How cash flow simulation improves ABS portfolios. [Read more](https://cardoai.com/what_s_new_in_produc/how-cash-flow-simulation-improves-abs-portfolios/) Beyond Excel: Payment waterfall modeling, simplified. [Read more](https://cardoai.com/what_s_new_in_produc/payment-waterfall-modeling-simplified/) Transforming RMBS securitisation with data intelligence. [Read more](https://cardoai.com/what_s_new_in_produc/transforming-rmbs-securitisation-with-data-intelligence/) ### Make stress testing a workflow, not a one-off model. Book a demo to see governed scenarios with transparent outputs. [Get started](https://cardoai.com/book-a-demo/) --- ### [Data management](https://cardoai.com/solutions/by-product/data-management/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** DATA MANAGEMENT # Data integration built for control. Verified and ready to use Connect to any source, ingest any format, and enforce data quality with built-in controls, so every downstream calculation and report starts with trusted data. [Book a demo](https://cardoai.com/book-a-demo/) OUR SOLUTION ## The control-led data layer for asset based finance - [Configurable connectors and pipelines](#configurable-connectors-and-pipelines) - [Simple data mapping](#simple-data-mapping) - [Data validation at scale](#data-validation-at-scale) - [Document extraction with traceability](#document-extraction-with-traceability) [Configurable connectors and pipelines](#configurable-connectors-and-pipelines) ![](https://cardoai.com/wp-content/uploads/2026/04/Data-Management-our-solution-1-1.svg "Data Management - our solution - 1") Link your APIs, cloud buckets, and files instantly. With built in scheduling and metadata capture, you can scale your data delivery without the need for custom engineering. [Simple data mapping](#simple-data-mapping) ![Data transformation](https://cardoai.com/wp-content/uploads/2026/04/data-management-our-solution-2-1.svg "data-management-our-solution-2") Configure mapping, formulas, and SQL logic without hard coding. Versioned changes allow your team to iterate and test rules instantly, removing the need for engineering cycles as portfolios evolve. [Data validation at scale](#data-validation-at-scale) ![Data transformation](https://cardoai.com/wp-content/uploads/2026/04/Data-Management-our-solution-3.svg "Data Management - our solution - 3") Controls run within files, across sources, and over time, surfacing issues as clear pass/fail results instead of hidden discrepancies. Standard and custom checks cover completeness, thresholds, and reconciliation, so downstream calculations start from verified inputs. [Document extraction with traceability](#document-extraction-with-traceability) ![](https://cardoai.com/wp-content/uploads/2026/04/data-management-our-solution-4.svg "data-management-our-solution-4") Cardo AI extracts key fields from statements, reports, and agreements to populate tapes faster and reduce manual keying. Each output stays linked to its source evidence, creating clear traceability from document to record and supporting diligence and reporting without reopening PDFs. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated USE CASES ## Trusted data workflows in practice 01 Collateral graph and uniqueness checks to catch duplicates and double-pledging risks beyond IDs. 02 Doc-to-tape AI extraction (OCR + field mappers) with source-to-record comparisons. 03 Exception management and governance so bad data can’t be “accepted” without approval. 04 Cashflow truth ingestion via bank/payment connectors and reconciliation to schedules. 05 Data delivery to your lakehouse (Snowflake/Databricks/ Redshift/Synapse) for instant querying. ![](https://cardoai.com/wp-content/uploads/2026/04/Data-Management-Use-Cases.svg "Data Management - Use Cases") ## Related resources Data Intelligence Engine: data profiling, with more power [Read more](https://cardoai.com/what_s_new_in_produc/data-profiling-and-ai-ensure-the-quality-of-your-data/) Data Profiling and AI: Ensure the quality of your data [Read more](https://cardoai.com/what_s_new_in_produc/data-profiling-and-ai-ensure-the-quality-of-your-data-2/) AI powered Document Processing for Private Credit and ABF [Read more](https://cardoai.com/what_s_new_in_produc/ai-powered-document-processing-for-private-credit-and-asset-based-finance/) ### Start with clean data, and everything gets easier See how Cardo AI ingest, validate, and deliver data for your deals in days, not months. [Get started](https://cardoai.com/book-a-demo/) --- ### [Reg & investor reporting](https://cardoai.com/solutions/by-product/reg-investor-reporting/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** REGULATORY AND INVESTOR REPORTING # LP-ready reporting: automated, consistent, and always on time. Generate templated and custom investor reports from governed data and calculations, with versioning, distribution, and a single source of truth. [Book a demo](https://cardoai.com/book-a-demo/) OUR SOLUTION ## From source data to investor-grade reporting - [A unified report library](#a-unified-report-library) - [Templates and custom workflows](#templates-and-custom-workflows) - [Embedded compliance controls](#embedded-compliance-controls) - [Cross-portfolio views](#cross-portfolio-views) [A unified report library](#a-unified-report-library) ![A unified report library](https://cardoai.com/wp-content/uploads/2026/04/REG-Investor-Reporting-Our-solution-1-1.svg "REG & Investor Reporting - Our solution - 1") Investor reporting is organized as a library of outputs: transaction, investor, and regulatory-style, so recurring packages and one-off requests are generated from the same governed source. This reduces the fragmentation that happens when each request becomes a new workbook. It also helps teams standardize what “the investor pack” means across funds and quarters. [Templates and custom workflows](#templates-and-custom-workflows) ![Templates and custom workflows](https://cardoai.com/wp-content/uploads/2026/04/REG-Investor-Reporting-Our-solution-2-1.svg "REG & Investor Reporting - Our solution - 2") Cardo AI supports multiple controlled output workflows, including an Excel add-in, system generated delivery, and dashboard to PDF flows. The goal is to preserve consistent formatting while still allowing flexible presentation where LPs require it. [Embedded compliance controls](#embedded-compliance-controls) ![Embedded compliance controls](https://cardoai.com/wp-content/uploads/2026/04/REG-Investor-Reporting-Our-solution-3-1.svg "REG & Investor Reporting - Our solution - 3") With versioned reports stored in a centralized repository, you can easily track what was sent, when, and the assumptions behind it. This simplifies approvals, audit trails, and diligence requests, allowing teams to quickly retrieve past deliveries. [Cross-portfolio views](#cross-portfolio-views) ![Cross-portfolio views](https://cardoai.com/wp-content/uploads/2026/04/REG-Investor-Reporting-Our-solution-4-1.svg "REG & Investor Reporting - Our solution - 4") Investor questions often span multiple deals; cross-portfolio views help answer them consistently without stitching together separate analyses. Drill-down connects top-line metrics to underlying drivers so answers aren’t just “because the number changed.” This shortens response cycles while improving confidence in what’s being communicated. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated USE CASES ## The new standard for LP transparency 01 Investor reports and factsheets generated from validated deal data. 02 Look-through reporting to collateral pools and key performance drivers. 03 Automated delivery via email with consistent timing and version control. 04 Managerial reporting across facilities and portfolios for investor Q&A. 05 Document hub for reports, agreements, and supporting materials. ![](https://cardoai.com/wp-content/uploads/2026/04/Reporting-Use-cases-1.svg "Reporting - Use cases") ## Related resources Maximize integrity in ABF transactions with our Data Validation. [Read more](https://cardoai.com/what_s_new_in_produc/maximize-integrity-in-abf-transactions-with-our-data-validation/) Swishfund partners with Cardo AI to automate regulatory reporting. [Read more](https://cardoai.com/announcements/swishfund-partners-with-cardo-ai-to-automate-esma-reporting-for-private-securitization-transaction/) Data Profiling and AI: ensure the quality of your data. [Read more](https://cardoai.com/what_s_new_in_produc/data-profiling-and-ai-ensure-the-quality-of-your-data-2/) ### Upgrade reporting without adding headcount See how Cardo AI automates investor reporting with full consistency and auditability. [Get started](https://cardoai.com/book-a-demo/) --- ### [Insurance reporting](https://cardoai.com/solutions/by-product/insurance-reporting/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** INSURANCE REPORTING # Insurer-ready transparency from verified, auditable data Standardize and deliver reporting packages that insurance LPs and internal risk teams can rely on, with consistent data lineage and controls. [Book a demo](https://cardoai.com/book-a-demo/) OUR SOLUTION ## Reporting designed for insurer stakeholders - [Look-through insurance reporting](#look-through-insurance-reporting) - [Customizable reporting frameworks](#customizable-reporting-frameworks) - [Full data lineage and auditability](#full-data-lineage-and-auditability) - [Direct delivery into client data environment](#direct-delivery-into-client-data-environment) [Look-through insurance reporting](#look-through-insurance-reporting) ![Look-through insurance reporting](https://cardoai.com/wp-content/uploads/2026/04/Insurance-Reporting-our-solution-1-2.svg "Insurance Reporting - our solution - 1") Insurance reporting provides full visibility from positions to underlying collateral and cash flows, offering clear transparency on exposure, performance, and structural behavior. The output supports detailed reviews, going beyond high-level summaries. [Customizable reporting frameworks](#customizable-reporting-frameworks) ![Customizable reporting frameworks](https://cardoai.com/wp-content/uploads/2026/04/Insurance-Reporting-our-solution-2-2.svg "Insurance Reporting - our solution - 2") Start with standardized templates for common insurance needs, then easily customize for each LP’s specific requirements. Avoid reinventing the wheel with every new request. The result is controlled flexibility: a consistent structure with the room to meet tailored demands. [Full data lineage and auditability](#full-data-lineage-and-auditability) ![Full data lineage and auditability](https://cardoai.com/wp-content/uploads/2026/04/Insurance-Reporting-our-solution-3-1.svg "Insurance Reporting - our solution - 3") Gain total transparency from raw input to final output. Our audit trails make it easy to explain performance and period-to-period changes, providing a clear map of how every figure was calculated and verified. [Direct delivery into client data environment](#direct-delivery-into-client-data-environment) ![Direct delivery into client data environment](https://cardoai.com/wp-content/uploads/2026/04/Insurance-Reporting-our-solution-4-2.svg "Insurance Reporting - our solution - 4") Outputs can be delivered directly to a client data environment or through governed exports, so internal analytics, governance, and audit teams reuse the same trusted reporting data. That reduces rework and eliminates mismatches between “reporting numbers” and “internal numbers.” It also supports downstream use without sacrificing control over what was delivered and when. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated USE CASES ## Insurance-grade reporting workflows in production 01 Solvency-style transparency packs (look-through and structured templates). 02 Regulatory and capital analytics powered by consistent underlying data. 03 Ongoing covenant and performance monitoring tailored for insurer stakeholders. 04 Exception reporting to surface data gaps, changes, and remediation actions. 05 Automated distribution and versioning so stakeholders always see the latest. ![](https://cardoai.com/wp-content/uploads/2026/04/Reporting-Use-cases-1.svg "Reporting - Use cases") ## Related resources Cardo AI for Asset Managers and Insurance LPs. [Read more](https://cardoai.com/what_s_new_in_produc/cardo-ai-for-asset-managers-and-insurance-lps/) You can’t clap with one hand: unlock the power of Insurers in Private Credit. [Read more](https://cardoai.com/what_s_new_in_produc/you-cant-clap-with-one-hand-unlock-the-power-of-insurers-in-private-credit/) State of Private Credit fund performance: Q4 2025. [Read more](https://cardoai.com/reports/state-of-private-credit-performance-q4-2025/) ### Make insurance reporting predictable and provable See how Cardo AI generates trusted packages with full lineage. [Get started](https://cardoai.com/book-a-demo/) --- ### [Portfolio management](https://cardoai.com/solutions/by-product/portfolio-management/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** PORTFOLIO MANAGEMENT # One standard look-through view across your entire portfolio Standardized monitoring and cross-portfolio analytics for modern credit teams, from position to underlying collateral. [Book a demo](https://cardoai.com/book-a-demo/) OUR SOLUTION ## Look-through portfolio intelligence, built for asset based finance - [Position booking with lifecycle updates](#position-booking-with-lifecycle-updates) - [Deep dive into ABF collateral](#deep-dive-into-abf-collateral) - [Embedded compliance controls](#embedded-compliance-controls) - [Liquidity and reporting outputs](#liquidity-and-reporting-outputs) [Position booking with lifecycle updates](#position-booking-with-lifecycle-updates) ![](https://cardoai.com/wp-content/uploads/2026/04/Portfolio-Management-our-solution-1-1.svg "Portfolio Management - our solution - 1") Book positions across strategies and funds with lifecycle updates for draws, paydowns, valuations, and fees. This ensures a consistent system of record, allowing teams to manage positions from a single, controlled view as they evolve. [Deep dive into ABF collateral](#deep-dive-into-abf-collateral) ![](https://cardoai.com/wp-content/uploads/2026/04/Portfolio-Management-our-solution-2-2.svg "Portfolio Management - our solution - 2") Portfolio views provide deep insights into ABF collateral pools and underlying performance, linking exposure to loan-level drivers of risk and return. This allows for deeper analysis without the need for manual data stitching each time questions arise. [Embedded compliance controls](#embedded-compliance-controls) ![Unified portfolio oversight](https://cardoai.com/wp-content/uploads/2026/04/Portfolio-Management-our-solution-3-1.svg "Portfolio Management - our solution - 3") Compliance controls for mandates, allocations, and monitoring are seamlessly integrated, with automated checks, alerts, and approval workflows. This reduces the need for manual policing, ensures consistency across portfolios and time, and provides clear accountability. [Liquidity and reporting outputs](#liquidity-and-reporting-outputs) ![Liquidity and reporting outputs](https://cardoai.com/wp-content/uploads/2026/04/Portfolio-Management-our-solution-4-1.svg "Portfolio Management - our solution - 4") Our Portfolio management solution delivers liquidity and reporting outputs that combine portfolio views with governed data and exports. This supports faster Q&A and decision cycles because teams can respond from a consistent source rather than rebuilding “one-off” analyses. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated USE CASES ## Full cycle portfolio intelligence 01 Portfolio state and NAV views with position timelines and cash flows. 02 Look-through stratifications across underlying collateral and transactions. 03 Mandate compliance monitoring with alerts and audit trails. 04 Liquidity forecasting across redemptions, borrowings, and scheduled cash flows. 05 Stakeholder reporting with templated and customizable outputs. ![Portfolio Management - Use cases](https://cardoai.com/wp-content/uploads/2026/04/Portfolio-Management-Use-cases.svg "Portfolio Management - Use cases") ## Related resources Trade Order Management system for portfolio managers. [Read more](https://cardoai.com/what_s_new_in_produc/pre-trade-compliance-technology-for-portfolio-managers/) Diversification benefits of multi-sector Direct Lending portfolios. [Read more](https://cardoai.com/abf_desk/diversification-benefits-of-multi-sector-direct-lending-portfolios/) Meet the Cardo AI Excel Add-in: your entire portfolio data, one click away. [Read more](https://cardoai.com/what_s_new_in_produc/meet-the-cardo-ai-excel-add-in-your-entire-portfolio-data-one-click-away/) ### Ready to unlock your portfolio’s full potential? See how Cardo AI helps you spot risk, optimize liquidity, and drive performance. [Get started](https://cardoai.com/book-a-demo/) --- ### [Deal surveillance](https://cardoai.com/solutions/by-product/deal-surveillance/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** DEAL SURVEILLANCE # Independent deal surveillance, in real time Monitor performance, detect drift early, and investigate changes with independent calculations, alerts, and time-travel views. [Book a demo](https://cardoai.com/book-a-demo/) OUR SOLUTION ## Continuous surveillance, with automated monitoring and alerts - [Asset and liability analytics](#asset-and-liability-analytics) - [Independent trigger and covenant calculations](#independent-trigger-and-covenant-calculations) - [Automated system alerts](#automated-system-alerts) - [Unified portfolio surveillance](#unified-portfolio-surveillance) [Asset and liability analytics](#asset-and-liability-analytics) ![Cardo AI performance analytics](https://cardoai.com/wp-content/uploads/2026/04/Deal-Surveillance-our-solution-1-1.svg "Deal Surveillance - our solution - 1") Analyze assets and liabilities with trends, distributions, and time series data, giving teams full context on performance changes. Drill down to link top line movements to underlying drivers, and use “time-travel” across reporting dates to compare periods and see what changed, when, and why. [Independent trigger and covenant calculations](#independent-trigger-and-covenant-calculations) ![Cardo AI covenant monitor](https://cardoai.com/wp-content/uploads/2026/04/Deal-Surveillance-our-solution-2-1.svg "Deal Surveillance - our solution - 2") Cardo AI runs independent trigger and covenant calculations, removing reliance on self-reported or disconnected numbers. Waiver and suspension workflows capture evidence, approvals, and outcomes, creating a clear, defensible record of what was measured, waived, and why. [Automated system alerts](#automated-system-alerts) ![Covenant notification template configuration](https://cardoai.com/wp-content/uploads/2026/04/Deal-Surveillance-our-solution-3-1.svg "Deal Surveillance - our solution - 3") Configurable thresholds and change detection generate early alerts when performance drifts or risk rises. Notifications reach the right owners, enabling proactive remediation and turning surveillance into continuous oversight. [Unified portfolio surveillance](#unified-portfolio-surveillance) ![Cross deal analytics](https://cardoai.com/wp-content/uploads/2026/04/Deal-Surveillance-our-solution-4-2.svg "Deal Surveillance - our solution - 4") Cardo AI aggregates exposures and concentrations across deals, by asset class, seller, obligor, or structure, enabling portfolio level surveillance. Teams can spot correlated risks, track where exposure is building, and see how concentration evolves across the full book. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated USE CASES ## Take control of your deal performance in real time 01 Trigger and covenant monitoring with early warning alerts and governance workflows. 02 Collections vs. expectations analysis at asset and pool level. 03 Seller and obligor controls for concentration, behavior changes, and exposure drift. 04 Map structural risks to identify conflicts, recourse, and pledge chains. 05 Enable seamless back-up-agent handoffs with clean, export ready data. ![Deal Surveillance - Use Cases](https://cardoai.com/wp-content/uploads/2026/03/Deal-Surveillance-Use-Cases.svg "Deal Surveillance - Use Cases") ## Related resources Automated financials and covenant monitoring [Read more](https://cardoai.com/what_s_new_in_produc/automated-financials-and-covenant-monitoring/) Stratification: the power of layered risk analysis [Read more](https://cardoai.com/what_s_new_in_produc/stratification-the-power-of-layered-risk-analysis/) Direct lending: automate, track and mitigate risks [Read more](https://cardoai.com/what_s_new_in_produc/direct-lending-solution-automate-track-and-mitigate-risks/) ### See issues early and prove it with audit trails Get a demo of real-time surveillance built for structured credit teams. [Get started](https://cardoai.com/book-a-demo/) --- ### [Liquidity ladder management](https://cardoai.com/solutions/by-product/liquidity-ladder-management/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** LIQUIDITY LADDER MANAGEMENT # Forecast daily liquidity across assets, liabilities, and facilities Roll up expected and realized cash flows across assets and liabilities to spot gaps early and plan funding with confidence. [Book a demo](https://cardoai.com/book-a-demo/) OUR SOLUTION ## Cash flow intelligence across every layer - [Expected vs. realized cash flow](#expected-vs-realized-cash-flow) - [Contract driven liquidity](#contract-driven-liquidity) - [Proactive liquidity intelligence](#proactive-liquidity-intelligence) - [Auditable reporting and exports](#auditable-reporting-and-exports) [Expected vs. realized cash flow](#expected-vs-realized-cash-flow) ![Expected vs realized cash flow](https://cardoai.com/wp-content/uploads/2026/04/Liquidity-Ladder-our-solution-1-1.svg "Liquidity Ladder - our solution - 1") Track expected vs. realized cash flows across assets, tranches, facilities, and portfolios in one unified ladder. Variance analysis highlights discrepancies, making it easier to reconcile data, spot emerging gaps, and avoid month-end surprises. [Contract driven liquidity](#contract-driven-liquidity) ![Visualize contractual liquidity](https://cardoai.com/wp-content/uploads/2026/04/Liquidity-Ladder-our-solution-2-1.svg "Liquidity Ladder - our solution - 2") Cardo AI maps granular cash flows to actual settlement dates and structural rules. By replacing generic assumptions with real-world calendars, we deliver a daily liquidity view that reflects how cash truly moves. Drive smarter treasury decisions with data that matches settlement reality. [Proactive liquidity intelligence](#proactive-liquidity-intelligence) ![Proactive liquidity intelligence](https://cardoai.com/wp-content/uploads/2026/04/Liquidity-Ladder-our-solution-3-2.svg "Liquidity Ladder - our solution - 3") Identify missed payments, timing shifts, and concentration risks before they escalate. Cardo AI surfaces early warning signals with instant drill-down to the root cause. Layer in stress scenarios to see how liquidity shifts under pressure, moving your team from reactive fixes to proactive, driver based planning. [Auditable reporting and exports](#auditable-reporting-and-exports) ![Auditable reporting and. exports](https://cardoai.com/wp-content/uploads/2026/04/Liquidity-Ladder-our-solution-4.svg "Liquidity Ladder - our solution - 4") Share liquidity insights instantly via dashboards, Excel, or API. Cardo AI ensures treasury planning and stakeholder reporting always use the same verified numbers. By providing auditable, reusable outputs instead of manual builds, we enable seamless communication across finance, investment, and risk teams. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated USE CASES ## Day-by-day liquidity planning across the capital structure 01 Treasury planning: detect mismatches before they become emergencies. 02 Cashflow rollups from asset-level payments to portfolio liquidity. 03 Funding obligation forecasting across upcoming facility events and deadlines. 04 Liquidity stress views tied to scenario analysis and structure behavior. 05 Portfolio-wide monitoring for liquidity concentration and timing risk. ![Liquidity Ladder - Use cases](https://cardoai.com/wp-content/uploads/2026/04/Liquidity-Ladder-Use-cases.svg "Liquidity Ladder - Use cases") ## Related resources How cash flow simulation improves ABS portfolios. [Read more](https://cardoai.com/what_s_new_in_produc/how-cash-flow-simulation-improves-abs-portfolios/) Meet the Cardo AI Excel Add-in: your entire portfolio data, one click away. [Read more](https://cardoai.com/what_s_new_in_produc/meet-the-cardo-ai-excel-add-in-your-entire-portfolio-data-one-click-away/) ABF cash flow modeling: Are there alternatives to Excel-and-Intex? [Read more](https://cardoai.com/what_s_new_in_produc/how-cash-flow-simulation-improves-abs-portfolios/) ### Turn cash flow complexity into a clear plan. See how Cardo maps liquidity across your full portfolio in real time. [Get started](https://cardoai.com/book-a-demo/) --- ### [Facility management](https://cardoai.com/solutions/by-product/facility-management/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** Facility management # Run facilities with operational and accounting control Automate schedules, track covenants, manage counterparties, and maintain an audit ready facility record. [Book a demo](https://cardoai.com/book-a-demo/) OUR SOLUTION ## Facility administration and subledger control - [Flexible facility configuration](#flexible-facility-configuration) - [Automated payment schedules](#automated-schedules-and-cash-flow) - [Deal-events monitoring](#deal-event-monitoring) - [GL-ready subledger control](#gl-ready-subledger-control) [Flexible facility configuration](#flexible-facility-configuration) ![Smart term automation](https://cardoai.com/wp-content/uploads/2026/04/Facility-Management-our-solution-1-1.svg "Facility Management - our solution - 1") Easily configure facilities with multiple coupon types, PIK, margin grids, hybrids, and calendars. Floors, caps, resets, and step-ups are automatically included, ensuring your terms stay accurate and adaptable without the need for manual updates. [Automated payment schedules](#automated-schedules-and-cash-flow) ![Cash flow booking & event handling](https://cardoai.com/wp-content/uploads/2026/04/Facility-Management-our-solution-2-1.svg "Facility Management - our solution - 2") Automated payment schedules and cash flow bookings, using business day conventions and event handling. Schedules update automatically with changes, ensuring accuracy and reducing manual work. This keeps your data current and error-free, with accounting-grade outputs. [Deal-events monitoring](#deal-event-monitoring) ![Covenant & counterparty monitoring](https://cardoai.com/wp-content/uploads/2026/04/Facility-Management-our-solution-3-1.svg "Facility Management - our solution - 3") Covenant and counterparty monitoring includes configurable notifications and workflows to help teams stay on top of deadlines, breaches, and communications. The process is governed with a clear approval trail, turning monitoring into a managed workflow instead of relying on ad-hoc reminders. [GL-ready subledger control](#gl-ready-subledger-control) ![Subledger support](https://cardoai.com/wp-content/uploads/2026/04/Facility-Management-our-solution-4-1.svg "Facility Management - our solution - 4") Subledger includes double-entry integrity, reconciliations, and GL-ready exports, ensuring finance teams can trust the postings. Outputs are designed for seamless integration with accounting systems, reducing rework at month end and ensuring smooth flow from operational changes to postings. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated USE CASES ## Facility management from inception to maturity 01 Automated payment schedules that update when margins/PIK terms change. 02 Covenant tracking and reminders with approval workflows and audit logs. 03 Drawdown-to-schedule sync connected to borrowing base approvals. 04 Cash flow booking and monitoring across interest, principal, fees, and adjustments. 05 GL integration outputs for downstream finance systems. ![Subledger](https://cardoai.com/wp-content/uploads/2026/04/Facility-Management-Use-cases.svg "Facility Management - Use cases") ## Related resources Streamline complex ABL transactions through our end-to-end solution. [Read more](https://cardoai.com/what_s_new_in_produc/navigate-abl-transactions-with-ease-through-our-new-tailored-solution/) Scale portfolios with automated financials and covenant monitoring. [Read more](https://cardoai.com/what_s_new_in_produc/automated-financials-and-covenant-monitoring/) Are your ABF risk management analytics truly effective? [Read more](https://cardoai.com/reports/asset-backed-finance-risk-modeling/) ### Replace manual schedule maintenance with controlled automation See how Cardo AI runs facilities with fewer errors and faster closes. [Get started](https://cardoai.com/book-a-demo/) --- ### [Loan data agent](https://cardoai.com/solutions/by-product/loan-data-agent/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** LOAN DATA AGENT # Governed calculations for credit operations Cardo AI can operate as your calculation agent, executing contractual computations with transparency, audit trails, and multi-party coordination. [Book a demo](https://cardoai.com/book-a-demo/) OUR SOLUTION ## Calculation-agent rigor, delivered as software (or service) - [Rule-based engine calculations](#contractual-calculations) - [Explainable waterfall allocations](#waterfall-execution-with-explainable-allocations) - [Trigger and covenant monitoring](#trigger-and-covenant-monitoring) - [Standardized reporting and distribution](#standardized-reporting-and-distribution) [Rule-based engine calculations](#contractual-calculations) ![](https://cardoai.com/wp-content/uploads/2026/04/Loan-Data-Agent-our-solution-1-1.svg "Loan Data Agent - our solution - 1") Contractual calculations aligned to legal documents, borrowing bases, eligibility, limits, and triggers, so outputs reflect the governing rule set, not informal spreadsheets. Versioning ensures you can trace which configuration was in force for any specific run. Results can be traced back to rules and inputs, reducing ambiguity between parties. [Explainable waterfall allocations](#waterfall-execution-with-explainable-allocations) ![](https://cardoai.com/wp-content/uploads/2026/04/Loan-Data-Agent-our-solution-2-1.svg "Loan Data Agent - our solution - 2") Interest, principal, reserves, expenses, and payment priorities are allocated period by period with clear logic. Each run creates a defensible record, supporting repeatable execution and reducing reliance on manual waterfall maintenance. [Trigger and covenant monitoring](#trigger-and-covenant-monitoring) ![](https://cardoai.com/wp-content/uploads/2026/04/Loan-Data-Agent-our-solution-3-1.svg "Loan Data Agent - our solution - 3") Trigger and covenant monitoring includes governed workflows for waivers/suspensions and supports independent oversight. Evidence, calculations, and decisions remain visible, and exceptions are handled through a standardized process so governance does not depend on who manages the deal. [Standardized reporting and distribution](#standardized-reporting-and-distribution) ![](https://cardoai.com/wp-content/uploads/2026/04/Loan-Data-Agent-our-solution-4-1.svg "Loan Data Agent - our solution - 4") Deliver consistent reports to borrowers, lenders, trustees, and investors through a controlled workflow. Manage exceptions within the process, and keep a clear record of what was delivered and why, so reporting stays repeatable and reliable. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated USE CASES ## Use cases for independent calc, governance, and outputs 01 Borrowing base certificates generated from system checks, not self-reported numbers. 02 Monthly servicer and payment reports with full versioning and audit trails. 03 Independent oversight to reduce disputes and operational risk. 04 Stakeholder workflows for approvals, clarifications, and controlled exceptions. 05 Single source of truth for data, calculations, and delivered outputs. ![](https://cardoai.com/wp-content/uploads/2026/04/Loan-Data-Agent-Use-Cases.svg "Loan Data Agent - Use Cases") ## Related resources Trade faith with fact with our Transaction Manager. [Read more](https://cardoai.com/what_s_new_in_produc/trade-faith-with-fact-transaction-manager/) Introducing Document Reader for credit agreements. [Read more](https://cardoai.com/what_s_new_in_produc/introducing-document-reader-for-credit-agreements/#introduction) Swishfund partners with Cardo AI to automate ESMA reporting. [Read more](https://cardoai.com/announcements/swishfund-partners-with-cardo-ai-to-automate-esma-reporting-for-private-securitization-transaction/) ### Make calculations a source of trust, not friction See how Cardo AI delivers agent grade data with software level controls. [Get started](https://cardoai.com/book-a-demo/) --- ### [Workflow Management](https://cardoai.com/resources/whats-new-in-product/workflow-management/) **Published:** April 9, 2026 **Author:** sviluppatore **Content:** # What’s new in product Latest product news and feature releases, highlighting innovations across the Cardo AI platform. Category All - [All](https://cardoai.com/resources/whats-new-in-product/) - [Data Management](https://cardoai.com/resources/whats-new-in-product/data-management/) - [Workflow Management](https://cardoai.com/resources/whats-new-in-product/workflow-management/) - [Portfolio Analytics](https://cardoai.com/resources/whats-new-in-product/portfolio-analytics/) - [Asset & Structure Types](https://cardoai.com/resources/whats-new-in-product/asset-structure-types/) - [Deal Modeling](https://cardoai.com/resources/whats-new-in-product/deal-modeling/) [ ![](https://cardoai.com/wp-content/uploads/2024/12/Trade-Order-Management-System-for-portfolio-managers.png "Trade Order Management System for portfolio managers") - December 11, 2024 ### Trade Order Management System for portfolio managers Traditional credit management tools often impede portfolio managers with rigid processes, limited transaction capabilities, and inadequate compliance controls. OurTrade Order Management System transforms portfolio management by enabling seamless rebalancing, ensuring compliance, Read more](https://cardoai.com/whats_new_in_product/pre-trade-compliance-technology-for-portfolio-managers/) [ ![](https://cardoai.com/wp-content/uploads/2024/11/Dynamic-portfolio-views_-see-your-investments-your-way.png "Dynamic portfolio views_ see your investments, your way") - November 22, 2024 ### Dynamic portfolio views: see your investments, your way Traditional portfolio management tools often fall short, hindering users with rigid interfaces, time-consuming tasks, and limited analytical capabilities. This can compromise efficient decision-making and ultimately impact portfolio performance. Our platform‘s Dynamic Portfolio Views Read more](https://cardoai.com/whats_new_in_product/dynamic-portfolio-views-see-your-investments-your-way/) [ ![](https://cardoai.com/wp-content/uploads/2024/11/Simplify-collateral-management-with-technology.png "Simplify collateral management with technology") - November 6, 2024 ### Simplify collateral management with technology Our technology simplifies collateral management with rapid onboarding and efficient eligibility checks for enhanced risk management. Read more](https://cardoai.com/whats_new_in_product/simplify-collateral-management-with-technology/) [ ![](https://cardoai.com/wp-content/uploads/2024/09/Automated-calendar_-dates-done-right-every-time.png "Automated calendar_ dates done right, every time") - September 9, 2024 ### Automated calendar: dates done right, every time Introducing the Automated Calendar Module, a powerful tool specifically designed to enhance the management of key dates and events within private Asset-Backed Financing (ABF) deals. This innovative tool enables users to automate and customize the scheduling of essential agreement dates, including intere Read more](https://cardoai.com/whats_new_in_product/automated-calendar-dates-done-right-every-time/) [ ![](https://cardoai.com/wp-content/uploads/2024/05/Trade-Faith-with-Fact_-Transaction-Manager.png "Trade Faith with Fact_ Transaction Manager") - May 30, 2024 ### Trade faith with fact: Transaction Manager Asset-based transactions require complex calculations to determine the borrowing base, which affects the amount that can be drawn from existing facilities. In a standard scenario, a borrower must adhere to various asset-level eligibility criteria, enforce concentration limits (often around 50 for a typical US consumer loan transaction) on an aggregated level, and dynamically adjust advance rates based on asset characteristics. Read more ](https://cardoai.com/whats_new_in_product/trade-faith-with-fact-transaction-manager/) --- ### [SME Platform II](https://cardoai.com/transactions/sme-platform-ii/) **Published:** March 30, 2026 **Author:** lorenzo **Content:** # **SME Platform II** ## Attachment: [Prospectus](https://cardoai.com/wp-content/uploads/2026/03/VALSA_SME_PLATFORM_II_-_Prospectus-1.pdf) ## Additional Information: Originator: Banca Valsabbina S.C.p.A. Servicer: Banca Valsabbina S.C.p.A. Sub Servicer: NSA S.p.A. Back-up Servicer: Casa di Risparmio Asti S.p.A. Back-up Sub-Servicer: Società Cooperativa di Garanzia Collettiva dei Fidi tra Piccole e Medie Imprese- Garanzia Etica Representative of Noteholders: Centotrenta Servicing S.p.A. Computation Agent: Banca Finanziaria Internazionale S.p.A. Cash Manager: Finanziaria Internazionale Investments SGR S.p.A. Account Bank: Bank of New York Mellon SA/NV – Milan Branch Corporate Servicer: Banca Finanziaria Internazionale S.p.A. Arranger: Banca Valsabbina S.C.p.A. Arranger: Banca Finanziaria Internazionale S.p.A. Paying Agent: Bank of New York Mellon SA/NV – Milan Branch Data Agent: Cardo AI --- ### [Specialty finance/NPL](https://cardoai.com/deal-desk/specialty-finance-npl/) **Published:** March 18, 2026 **Author:** sviluppatore **Content:** # Deal Desk Explore our featured deals to see how our clients leverage Cardo AI to navigate their most complex asset-based structures. Category Specialty Finance/NPL - [Consumer Credit](https://cardoai.com/deal-desk/consumer-credit/) - [Corporate/Private credit](https://cardoai.com/deal-desk/corporate-private-credit/) - [Infrastructure/Real Assets](https://cardoai.com/deal-desk/infrastructure-real-assets/) - [Real Estate](https://cardoai.com/real-estate/) - [Specialty Finance/NPL](https://cardoai.com/deal-desk/specialty-finance-npl/) Specialty Finance/NPL [Multi-Asset Bank Facility](#multi-asset-bank-facility) ****Asset class:** Consumer Loans • Real Estate • Vessels** **Context**: European bank managing multiple asset types required a secure, interoperable platform able to manage various facilities and structures, to scale the lending activity. **Scale** Over 10k assets across deals $100M AUM **Funding structure** Credit Line • Bonds • Senior Notes **Data flow** Daily **Connectivity** Mixed (manual uploads and automated exports) **Live Cardo AI modules** Data Standardization, Validation, and Transformation • Direct Lending Engine • Transaction Management • Collateral Management • Analytics • Connectivity with Salesforce CRM • Connectivity with TUUM **Client voice** *“we are caring your platform as master system for all organization data (facility and assets) and loan”* (Bank CTO) [Non-Performing Loans](#non-performing-loans) ****Asset class:** Non Performing Loans Context**: A European Asset Manager with multiple asset types required a secure, interoperable platform to manage complex non performing loan deals **Scale** Over 500 assets €20M AUM **Funding structure** Fixed Rate Term Loan **Data flow** Monthly **Connectivity** Manual Uploads **Live Cardo AI modules** Data Standardization, Validation, and Transformation • Transaction Management • Analytics • Reporting ## Case studies from our clients Learn how our clients used Cardo AI to grow their business. [Read](https://cardoai.com/use-cases/asset-managers/) --- ### [Real estate](https://cardoai.com/deal-desk/real-estate/) **Published:** March 18, 2026 **Author:** sviluppatore **Content:** # Deal Desk Explore our featured deals to see how our clients leverage Cardo AI to navigate their most complex asset-based structures. Category Real Estate - [Consumer Credit](https://cardoai.com/deal-desk/consumer-credit/) - [Corporate/Private credit](https://cardoai.com/deal-desk/corporate-private-credit/) - [Infrastructure/Real Assets](https://cardoai.com/deal-desk/infrastructure-real-assets/) - [Real Estate](https://cardoai.com/real-estate/) - [Specialty Finance/NPL](https://cardoai.com/deal-desk/specialty-finance-npl/) Real ESTATE [Commercial Real Estate](#commercial-real-estate) ****Asset class:** Commercial Real Estate** **Context**: A US Insurance company managing multiple asset types required a secure, interoperable platform to manage various Commercial Real Estate investments. **Scale** Over 1k assets across deals $500M AUM **Funding Structure** Term Loans **Data Flow** Monthly **Connectivity** Manual uploads **Live Cardo AI Modules** Data Standardization, Validation, and Transformation • Transaction Management • Analytics • Reporting **Client voice** *“we are caring your platform as master system for all organization data (facility and assets) and loan”* (Bank CTO) [Residential Mortgages](#residential-mortgages) **Asset class:** Residential Mortgages **Context**: A US Insurance company managing multiple asset types required a secure, interoperable platform to manage various Residential investments. **Scale** Thousands of Assets $1Bn AUM **Funding structure** Fixed Rate Term Loan **Data flow** Weekly **Connectivity** Manual FE uploads **Live Cardo AI modules** Data Standardization, Validation, and Transformation • Transaction Management • Analytics • Reporting ## Case studies from our clients Learn how our clients used Cardo AI to grow their business. [Read](https://cardoai.com/use-cases/asset-managers/) --- ### [Infrastructure/real assets](https://cardoai.com/deal-desk/infrastructure-real-assets/) **Published:** March 18, 2026 **Author:** sviluppatore **Content:** # Deal Desk Explore our featured deals to see how our clients leverage Cardo AI to navigate their most complex asset-based structures. Category Infrastructure/Real Assets - [Consumer Credit](https://cardoai.com/deal-desk/consumer-credit/) - [Corporate/Private credit](https://cardoai.com/deal-desk/corporate-private-credit/) - [Infrastructure/Real Assets](https://cardoai.com/deal-desk/infrastructure-real-assets/) - [Real Estate](https://cardoai.com/real-estate/) - [Specialty Finance/NPL](https://cardoai.com/deal-desk/specialty-finance-npl/) Infrastructure/Real Assets [Digital Infrastructure 1](#digital-infrastructure-1) ****Asset class:** Cell towers Context**: A US global investment firm managing a variety of assets including cell towers required a secure, interoperable platform to manage multiple cell-tower loans – securing the acquisition of cell towers across the United States, with cash flows directly generated from tenant leases. **Scale** Over 5000 assets across over 10 deals, with over $2B outstanding in loans **Funding structure** Term Loans **Data flow** Monthly **Connectivity** Manual Uploads – Geneva + Chatham + Remit reports **Live Cardo AI modules** Data Standardization, Validation, and Transformation • GEOMAP • Custom Analytics • Custom Reporting • Direct Lending Engine [Digital Infrastructure 2](#digital-infrastructure-2) ****Asset class:** Data Centers** **Context**: A US global investment firm managing a variety of assets including cell towers required a secure, interoperable platform to manage multiple data center loans – securing the design, engineering, construction, completion and operation of data centers across the United States, with cash flows directly generated from tenant leases. **Scale** Over 20 assets across over 7 deals, with over $10B outstanding in loans **Funding structure** **Data flow** Monthly **Connectivity** Manual Uploads – Geneva + Chatham + Remit reports **Live Cardo AI modules** Data Standardization, Validation, and Transformation • GEOMAP • Custom Analytics • Custom Reporting • Direct Lending Engine [Energy and Infrastructure 1](#energy-and-infrastructure) **Asset class: Solar Loans** **Context:** A US Insurance company managing multiple asset types required a secure, interoperable platform to manage various Solar Loans investments. **Scale** Over 20 assets across over 7 deals, with over $10B outstanding in loans **Funding structure** **Data flow** Monthly **Connectivity** Manual Uploads – Geneva + Chatham + Remit reports **Live Cardo AI modules** Data Standardization, Validation, and Transformation • GEOMAP • Custom Analytics • Custom Reporting • Direct Lending Engine [Energy and Infrastructure 2](#energy-and-infrastructure) **Asset class**: **Telecommunication Infrastructure (Fiber Optics)** **Context**: A US Insurance company managing multiple asset types required a secure, interoperable platform to manage various Infrastructure investments. **Scale** Over 1,000 asset $690M AUM **Funding structure** Delayed Draw Down Term Loans **Data flow** Weekly **Connectivity** Manual FE uploads **Live Cardo AI modules** Data Standardization, Validation, and Transformation • Transaction Management • Analytics • Reporting ## Case studies from our clients Learn how our clients used Cardo AI to grow their business. [Read](https://cardoai.com/use-cases/asset-managers/) --- ### [Corporte/private credit](https://cardoai.com/deal-desk/corporate-private-credit/) **Published:** March 18, 2026 **Author:** sviluppatore **Content:** # Deal Desk Explore our featured deals to see how our clients leverage Cardo AI to navigate their most complex asset-based structures. Category Corporate/Private credit - [Consumer Credit](https://cardoai.com/deal-desk/consumer-credit/) - [Corporate/Private credit](https://cardoai.com/deal-desk/corporate-private-credit/) - [Infrastructure/Real Assets](https://cardoai.com/deal-desk/infrastructure-real-assets/) - [Real Estate](https://cardoai.com/real-estate/) - [Specialty Finance/NPL](https://cardoai.com/deal-desk/specialty-finance-npl/) Corporate/Private credit [Corporate Credit Cards](#corporate-credit-cards) **Asset Class:** Corporate Credit Cards **Context**: A US lender managing corporate credit cards required a secure, interoperable platform to manage a Senior Tranche. The deal is in a ramp-up phase with potential to scale to 110,000+ assets and $150M in AUMs. **Scale** 500 assets $10M outstanding and scaling **Funding structure** Senior Tranche, deal in ramp-up phase **Data flow** Daily, Funding request weekly, waterfall monthly **Connectivity** SFTP with GPG file encryption **Live Cardo AI modules** Data Standardization, Validation, and Transformation • Transaction Management • Waterfall Calculations • Cash Flow Simulation [Corporate / Leveraged Loans 1](#corporate-leveraged-loans-1) **Asset class:** Corporate Loans/Leveraged Loans **Context**: A US global investment firm managing a variety of assets including corporate loans and leveraged loans required a secure, interoperable platform to manage multiple insurance rated structured – SPVs with multiple classes of delayed drawdown loans and equity piece each structure. **Scale** $2.8B AUM **Funding structure** 6 SPVs with 7 classes of delayed drawdown loans and equity piece each **Data flow** Daily, including daily funding as needed **Connectivity** Axiom + Geneva, SFTP **Live Cardo AI modules** Data Standardization, Validation, and Transformation • Transaction Management • Waterfall Calculations [Corporate / Leveraged Loans 2](#corporate-leveraged-loans-2) ****Asset class:** Middle Market Leveraged Loans Context**: A US global investment firm managing a variety of assets including middle market leveraged loans required a secure, interoperable platform to manage multiple borrowers, SPVs and classes of notes and loans. **Scale** **Funding structure** 3 SPVs with 6 classes of loans **Data flow** Daily, including daily funding as needed **Connectivity** Axiom + Geneva, SFTP **Live Cardo AI modules** Data Standardization, Validation, and Transformation • Transaction Management • Waterfall Calculations **What’s special here** To optimize Funded Par Value calculation and ensure compliance, we’ve moved from a manual, spreadsheet-based process to an automated workflow in Cardo. This prevents exceeding capital call limits and reduces compliance risk—a key step for operational efficiency. Cardo applies the adjustment mechanism to the most senior DDTL (Class A–C), offsetting any “virtual” reductions that would have impacted junior classes (D–F). Adjustments occur only if the new DDTL target is lower than the prior draw amount. [Fund Finance 1](#fund-finance-1) ****Asset class:** Fund of funds Context**: A US global investment firm managing a variety of assets including managing funds of funds required a secure, interoperable platform to manage multiple borrowers and classes of notes and loans. **Scale** $500M **Funding structure** 1 borrower and 4 classes of loans **Data flow** Daily, including daily funding as needed **Connectivity** Axiom + Geneva, SFTP **Live Cardo AI modules** Data Standardization, Validation, and Transformation • Transaction Management • Waterfall Calculations [Fund Finance 2](#fund-finance-2) ****Asset class: Fund Finance** Context**: A US asset manager managing multiple asset types required a secure, interoperable platform to manage various Fund Finance investments. **Scale** $300M AUM **Funding structure** Term loan **Data flow** Weekly **Connectivity** Manual FE uploads **Live Cardo AI modules** Data Standardization, Validation, and Transformation • Transaction Management • Analytics • Reporting [Corporate Loans](#corporate-loans) ****Asset class****: ****Corporate Loan** Context**: A US Insurance company managing multiple asset types required a secure, interoperable platform to manage various Corporate Loans investments. **Scale** Various assets (deposit accounts, securities accounts, revenue agreements) backing $150M AUM **Funding structure** Delayed Draw Down Term Loans **Data flow** Weekly **Connectivity** Manual FE uploads **Live Cardo AI modules** Data Standardization, Validation, and Transformation • Transaction Management • Analytics • Reporting ## Case studies from our clients Learn how our clients used Cardo AI to grow their business. [Read](https://cardoai.com/use-cases/asset-managers/) --- ### [Consumer credit](https://cardoai.com/deal-desk/consumer-credit/) **Published:** March 18, 2026 **Author:** sviluppatore **Content:** # Deal Desk Explore our featured deals to see how our clients leverage Cardo AI to navigate their most complex asset-based structures. Category Consumer Credit - [Consumer Credit](https://cardoai.com/deal-desk/consumer-credit/) - [Corporate/Private credit](https://cardoai.com/deal-desk/corporate-private-credit/) - [Infrastructure/Real Assets](https://cardoai.com/deal-desk/infrastructure-real-assets/) - [Real Estate](https://cardoai.com/real-estate/) - [Specialty Finance/NPL](https://cardoai.com/deal-desk/specialty-finance-npl/) Consumer CREDIT [Revolving Credit Lines](#revolving-credit-lines) **Asset class**: Consumer Loans (revolving Credit Lines for vehicle purposes) **Context**: A US lender managing consumer loans (revolving credit lines for vehicle purposes) required a secure, interoperable platform to manage a Delayed Draw Term Loan with growth potential to 80,000+ assets and $150M in AUM. **Scale** 32k assets $40M outstanding at onboarding, supporting **Funding structure** Senior Delayed Draw Term Loan **Data flow** Weekly (daily enabled), BB-linked funding cadence **Connectivity** Snowflake + Parquet via Cardo’s Oxygen Platform **Live Cardo AI modules** Data Standardization, Validation & Transformation • Transaction Management • Waterfall Calculations • Cash Flow Simulation • Audit-ready reporting package • Portfolio and loan-level drill-downs **Client voice** *“Quick note from one principal to another to say that I am very excited to build my business on top of your platform. Throughout our recent POC, your team has been professional, thorough, and smart. You should be proud of them.”CEO and Chief Investment Officer, US Lender”* [Consumer Loans](#consumer-loans) **Asset Class:** Consumer Loans **Context**: A US lender managing consumer loans required a secure, interoperable platform to manage a Senior Tranche where the underlying portfolio is amortizing **Scale** 10k assets $54M outstanding at onboarding **Funding structure** Senior Tranche, underlying portfolio amortizing and not purchasing additional loans, expected to be fully repaid by the end of 2028. **Data flow** Monthly **Connectivity** Front End Data Upload **Live Cardo AI modules** Data Standardization, Validation, and Transformation • Transaction Management • Waterfall Calculations • Cash Flow Simulation • Audit-ready reporting package • Portfolio and loan-level drill-downs [Credit Card Receivables](#credit-card-receivables) ****Asset class:** Credit Card Receivables** **Context**: A US lender managing credit card receivables required a secure, interoperable platform to manage a Senior Tranche with over 400,000 assets and $125M in AUM. **Scale** 400K assets $100M outstanding **Funding structure** Senior Tranche, advance rate applied to eligible receivables **Data flow** Daily. Up to two fundings per week based on request including BB calculations **Connectivity** SFTP **Live Cardo AI modules** Data Standardization, Validation, and Transformation • Transaction Management • Waterfall Calculations • Cash Flow Simulation [Consumer Lease / Rent-to-Own 1](#consumer-lease-rent-to-own-1) **Asset class**: Consumer Lease, Rental Purchase Agreement, Rent-to-Own Contract **Context**: A US lender managing consumer lease, rental purchase agreement, rent-to-own contract required a secure, interoperable platform to manage a Senior Tranche with a variable advance rate **Scale** 36k assets $70M outstanding **Funding structure** Senior Tranche **Data flow** Weekly, Up to once per week based on request and BB calculations funding cadence **Connectivity** SFTP **Live Cardo AI modules** Data Standardization, Validation, and Transformation • Transaction Management • Waterfall Calculations • Cash Flow Simulation [Consumer Lease / Rent-to-Own 2](#consumer-lease-rent-to-own-2) **Asset Class:** Lease to Own – LTO **Context**: A US lender managing lease to own contracts required a secure, interoperable platform to manage a Senior Secured Delayed Draw Term Loan with dynamic advance rate tied to Net Book Value (NBV) of Eligible LTO Receivables. **Scale** 4k assets 27M outstanding **Funding Structure** Senior Secured Delayed Draw Term Loan **Data Flow** Daily, Up to 2x per week funding cadence **Connectivity** SFTP **Live Cardo AI Modules** Data Standardization, Validation, and Transformation • Transaction Management • Waterfall Calculations • Cash Flow Simulation • Direct Lending Engine [BNPL](#bnpl) ****Asset Class:** Consumer Loans – BNPL Context**: A US investment firm managing buy-no-pay-later loans required a secure, interoperable platform to manage a Term Loan structure with growth potential to 5 millions assets. **Scale** 3.1M number of assets $755M outstanding **Funding structure** Senior Secured Delayed Draw Term Loan **Data flow** Daily, Monthly funding cadence **Connectivity** SFTP **Live Cardo AI modules** Data Standardization, Validation, and Transformation • Transaction Management • Deal Monitoring • Custom Analytics ## Case studies from our clients Learn how our clients used Cardo AI to grow their business. [Read](https://cardoai.com/use-cases/asset-managers/) --- ### [US Auto](https://cardoai.com/resources/research/us-auto/) **Published:** March 4, 2026 **Author:** sviluppatore **Content:** # Research | US Auto Loan-level analytics tracking credit, funding, and structural dynamics across U.S. auto ABS. Category US Auto - [All](https://cardoai.com/resources/research/) - [Asset Based Finance](https://cardoai.com/resources/research/asset-based-finance/) - [State of Private Credit](https://cardoai.com/resources/research/state-of-private-credit/) - [US Auto](https://cardoai.com/resources/research/us-auto/) - [Asset Based Lending](https://cardoai.com/resources/research/asset-based-lending/) - [Applied Intelligence](https://cardoai.com/resources/research/applied-intelligence/) [- February 3, 2026 US Auto Loan Q4 2025](https://cardoai.com/reports/us-auto-loan-q4-2025/) [- August 23, 2025 US Auto Loan Q2 2025](https://cardoai.com/reports/us-auto-loan-q3-2025/) [- April 23, 2025 US Auto Loan Q1 2025](https://cardoai.com/reports/us-auto-loan-q1-2025/) [- January 23, 2025 US Auto Loan Q4 2024](https://cardoai.com/reports/us-auto-loan-q4-2024/) [- September 23, 2024 US Auto Loan Q2 2024](https://cardoai.com/reports/us-auto-loan-q2-2024/) ## Let’s connect If you’re attending one the above conferences, we’ll be delighted to meet on the ground. Drop us a line at to set up a private 1-on-1 meeting. --- ### [State of private credit](https://cardoai.com/resources/research/state-of-private-credit/) **Published:** March 4, 2026 **Author:** sviluppatore **Content:** # Research | State of Private Credit Fund performance, deployment strategies, and structural trends across private credit. Category State of Private Credit - [All](https://cardoai.com/resources/research/) - [Asset Based Finance](https://cardoai.com/resources/research/asset-based-finance/) - [State of Private Credit](https://cardoai.com/resources/research/state-of-private-credit/) - [US Auto](https://cardoai.com/resources/research/us-auto/) - [Asset Based Lending](https://cardoai.com/resources/research/asset-based-lending/) - [Applied Intelligence](https://cardoai.com/resources/research/applied-intelligence/) [- September 11, 2026 Hidden concentration in Private Credit: why ten managers can be one bet](https://cardoai.com/reports/hidden-concentration-in-private-credit/) [- August 26, 2026 State of Private Credit performance Q2 2026](https://cardoai.com/reports/state-of-private-credit-performance-q2-2026-2/) [- May 21, 2026 State of Private Credit performance Q1 2026](https://cardoai.com/reports/state-of-private-credit-performance-q1-2026/) [- March 20, 2026 State of Private Credit performance Q4 2025](https://cardoai.com/reports/state-of-private-credit-performance-q4-2025/) [- November 19, 2025 State of Private Credit performance Q3 2025](https://cardoai.com/reports/state-of-private-credit-performance-q3-2025/) [- August 20, 2025 State of Private Credit performance Q2 2025](https://cardoai.com/reports/state-of-private-credit-performance-q2-2025-2/) [- June 3, 2025 State of Private Credit performance Q1 2025](https://cardoai.com/reports/state-of-private-credit-performance-q2-2025/) [- March 27, 2025 State of Private Credit performance Q4 2024](https://cardoai.com/reports/state-of-private-credit-performance-q4-2024/) [- September 23, 2024 State of Private Credit performance Q2 2024](https://cardoai.com/reports/state-of-private-credit-performance-q2-2024/) ## Let’s connect If you’re attending one the above conferences, we’ll be delighted to meet on the ground. Drop us a line at to set up a private 1-on-1 meeting. --- ### [Asset based lending](https://cardoai.com/resources/research/asset-based-lending/) **Published:** March 4, 2026 **Author:** sviluppatore **Content:** # Research | Asset Based Lending Transaction-level analysis and forecasts on ABL structures, deal flow, and credit strategy. Category Asset Based Lending - [All](https://cardoai.com/resources/research/) - [Asset Based Finance](https://cardoai.com/resources/research/asset-based-finance/) - [State of Private Credit](https://cardoai.com/resources/research/state-of-private-credit/) - [US Auto](https://cardoai.com/resources/research/us-auto/) - [Asset Based Lending](https://cardoai.com/resources/research/asset-based-lending/) - [Applied Intelligence](https://cardoai.com/resources/research/applied-intelligence/) [- June 24, 2025 Asset-Based Lending Playbook Q2 Forecasts – Q3 Opportunities](https://cardoai.com/reports/asset-based-lending-playbook-q2-forecasts-q3-opportunities/) [- April 23, 2025 Asset-Based Lending Playbook Q1 Scorecard – Q2 Outlook](https://cardoai.com/reports/asset-based-lending-playbook-q1-scorecard-q2-outlook/) ## Let’s connect If you’re attending one the above conferences, we’ll be delighted to meet on the ground. Drop us a line at to set up a private 1-on-1 meeting. --- ### [Asset based finance](https://cardoai.com/resources/research/asset-based-finance/) **Published:** March 3, 2026 **Author:** sviluppatore **Content:** # Research | Asset Based Finance Cross-market analysis of risk, regulation, and strategy across the ABF ecosystem. Category Asset Based Finance - [All](https://cardoai.com/resources/research/) - [Asset Based Finance](https://cardoai.com/resources/research/asset-based-finance/) - [State of Private Credit](https://cardoai.com/resources/research/state-of-private-credit/) - [US Auto](https://cardoai.com/resources/research/us-auto/) - [Asset Based Lending](https://cardoai.com/resources/research/asset-based-lending/) - [Applied Intelligence](https://cardoai.com/resources/research/applied-intelligence/) [- October 13, 2026 Credit BDC Sector Monitor Q2 2026](https://cardoai.com/reports/credit-bdc-sector-monitor-q2-2026/) [- August 13, 2026 Earned or Financed? BDC Distribution Sustainability Monitor](https://cardoai.com/reports/yearned-or-financed-bdc-distribution-sustainability-monitor/) [- July 20, 2026 Credit BDC Sector Monitor Q1 2026](https://cardoai.com/reports/credit-bdc-sector-monitor/) [- May 12, 2026 Liquidity stress testing for ABF. How does a Liquidity structure get stressed and what are the implications?](https://cardoai.com/reports/liquidity-stress-testing-for-abf/) [- March 30, 2026 Market Financial Solutions and the ABF ‘collateral-truth’ problem](https://cardoai.com/reports/market-financial-solutions-and-the-abf-collateral-truth-problem/) [- March 20, 2026 The SaaS credit paradox: Navigating software lending stress in the age of AI](https://cardoai.com/reports/the-saas-credit-paradox-navigating-software-lending-stress-in-the-age-of-ai/) [- March 3, 2026 Navigating redemption pressure in Private Credit and semi-liquid fund](https://cardoai.com/reports/asset-based-lending-playbook-q2-2025/) [- November 6, 2025 Risk without accountability: The financial collapse of Tricolor, First Brands and Carriox Capital](https://cardoai.com/reports/navigating-redemption-pressure-in-private-credit-and-semi-liquid-funds/) [- November 5, 2025 How lenders can avoid the next Tricolor, First Brands and Carriox Capital](https://cardoai.com/reports/how-lenders-can-avoid-the-next-tricolor-first-brands-and-carriox-capital/) [Load More](#) ## Know where it’s heading. Get there faster. [See what’s new in our Product.](https://cardoai.com/resources/whats-new-in-product/) --- ### [Applied intelligence](https://cardoai.com/resources/research/applied-intelligence/) **Published:** March 4, 2026 **Author:** sviluppatore **Content:** # Research | Applied Intelligence AI frameworks reshaping analysis and decision-making in ABF. Category Applied Intelligence - [All](https://cardoai.com/resources/research/) - [Asset Based Finance](https://cardoai.com/resources/research/asset-based-finance/) - [State of Private Credit](https://cardoai.com/resources/research/state-of-private-credit/) - [US Auto](https://cardoai.com/resources/research/us-auto/) - [Asset Based Lending](https://cardoai.com/resources/research/asset-based-lending/) - [Applied Intelligence](https://cardoai.com/resources/research/applied-intelligence/) [- June 4, 2026 From Automation to Autonomous ABF operations](https://cardoai.com/reports/from-automation-to-autonomous-abf/) [- November 21, 2025 Are your ABF risk management analytics truly effective?](https://cardoai.com/reports/asset-backed-finance-risk-modeling/) [- February 27, 2025 Leveraging language models at Cardo AI. A dual approach](https://cardoai.com/reports/leveraging-language-models-at-cardo-ai-a-dual-approach/) [- February 19, 2025 Exploring the role and potential of Large Language Models in the Asset-Based Finance sector](https://cardoai.com/reports/exploring-the-role-and-potential-of-large-language-models-in-the-asset-based-finance-sector/) ## Let’s connect If you’re attending one the above conferences, we’ll be delighted to meet on the ground. Drop us a line at to set up a private 1-on-1 meeting. --- ### [Terms and Conditions](https://cardoai.com/terms-conditions/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** # Terms and conditions These terms and conditions outline the rules and regulations for the use of Cardo s.r.l.’s Website, located at www.cardoai.com. By accessing this website we assume you accept these terms and conditions. Do not continue to use Cardo AI if you do not agree to take all of the terms and conditions stated on this page. The following terminology applies to these Terms and Conditions, Privacy Statement and Disclaimer Notice and all Agreements: “Client”, “You” and “Your” refers to you, the person log on this website and compliant to the Company’s terms and conditions. “The Company”, “Ourselves”, “We”, “Our” and “Us”, refers to our Company. “Party”, “Parties”, or “Us”, refers to both the Client and ourselves. All terms refer to the offer, acceptance and consideration of payment necessary to undertake the process of our assistance to the Client in the most appropriate manner for the express purpose of meeting the Client’s needs in respect of provision of the Company’s stated services, in accordance with and subject to, prevailing law of Itay. Any use of the above terminology or other words in the singular, plural, capitalization and/or he/she or they, are taken as interchangeable and therefore as referring to same. **Cookies** We employ the use of cookies. 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We do not ensure that the information on this website is correct, we do not warrant its completeness or accuracy; nor do we promise to ensure that the website remains available or that the material on the website is kept up to date. **Disclaimer** To the maximum extent permitted by applicable law, we exclude all representations, warranties and conditions relating to our website and the use of this website. Nothing in this disclaimer will: - limit or exclude our or your liability for death or personal injury; - limit or exclude our or your liability for fraud or fraudulent misrepresentation; - limit any of our or your liabilities in any way that is not permitted under applicable law; or - exclude any of our or your liabilities that may not be excluded under applicable law. The limitations and prohibitions of liability set in this Section and elsewhere in this disclaimer: (a) are subject to the preceding paragraph; and (b) govern all liabilities arising under the disclaimer, including liabilities arising in contract, in tort and for breach of statutory duty. As long as the website and the information and services on the website are provided free of charge, we will not be liable for any loss or damage of any nature. --- ### [Portfolio analytics](https://cardoai.com/resources/whats-new-in-product/portfolio-analytics/) **Published:** April 9, 2026 **Author:** sviluppatore **Content:** # What’s new in product Latest product news and feature releases, highlighting innovations across the Cardo AI platform. Category All - [All](https://cardoai.com/resources/whats-new-in-product/) - [Data Management](https://cardoai.com/resources/whats-new-in-product/data-management/) - [Workflow Management](https://cardoai.com/resources/whats-new-in-product/workflow-management/) - [Portfolio Analytics](https://cardoai.com/resources/whats-new-in-product/portfolio-analytics/) - [Asset & Structure Types](https://cardoai.com/resources/whats-new-in-product/asset-structure-types/) - [Deal Modeling](https://cardoai.com/resources/whats-new-in-product/deal-modeling/) [ ![](https://cardoai.com/wp-content/uploads/2026/10/cover-dashboard-pdf-reports-scaled.png "cover-dashboard-pdf-reports") - October 7, 2026 ### From deal dashboard to board-ready report in one click Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. Read more](https://cardoai.com/whats_new_in_product/from-deal-dashboard-to-board-ready-report-in-one-click/) [ ![](https://cardoai.com/wp-content/uploads/2025/06/Speeding-up-due-diligence-for-ABF-investments-1.png "Speeding up due diligence for ABF investments") - June 3, 2025 ### Speeding up due diligence for ABF investments In today’s borrower-driven market, originators shuffle the cards, while investors fight for a seat at the table. Strong demand for high-performing assets and limited supply mean borrowers can choose their capital partners and speed up due diligence. Read more](https://cardoai.com/whats_new_in_product/speeding-up-due-diligence-for-abf-investments/) [ ![](https://cardoai.com/wp-content/uploads/2025/01/Climate-Risk-Assessment-with-Location-Data-Providers.png "Climate Risk Assessment with Location Data Providers") - January 21, 2025 ### Climate risk assessment with location data providers Recent events, including the wildfires in Los Angeles and the tornadoes in Florida, have underscored the critical importance of climate risk assessment for investors in asset-based finance and the power of location data providers. As covered in a recent issue Read more](https://cardoai.com/whats_new_in_product/climate-risk-assessment-with-location-data-providers/) [ ![](https://cardoai.com/wp-content/uploads/2024/07/Early-warnings_-real-time-visibility-proactive-control.png "Early warnings_ real-time visibility, proactive control") - July 11, 2024 ### Early warnings: real-time visibility, proactive control Early warning signals are essential for navigating the dynamic landscape of structured finance. By gaining a clear view of your structured finance mandates, you can identify potential risks early and take proactive measures to mitigate them. Our solution empowers you with real-time visibility and proactive control. Unify your view: Gain a comprehensive dashboard that seamlessly monitors covenants, triggers, and limits across all your structured finance m Read more](https://cardoai.com/whats_new_in_product/early-warnings-real-time-visibility-proactive-control/) [ ![](https://cardoai.com/wp-content/uploads/2024/06/Distressed-Assets_-sell-smarter-collect-more-invest-confidently.png "Distressed Assets_ sell smarter, collect more, invest confidently") - June 13, 2024 ### Distressed assets: sell smarter, collect more, invest confidently Since the financial crisis of 2007-2008, distressed assets have been one of the most attractive and highly remunerative asset classes. However, for sellers, servicers and investors navigating this asset class presents unique challenges due to its inherent complexity and unreliable data. Relying solely on aggregated data treats distressed assets as isolated entities, overlooking the interconnected factors influe Read more](https://cardoai.com/whats_new_in_product/distressed-assets-sell-smarter-collect-more-invest-confidently/) [ ![](https://cardoai.com/wp-content/uploads/2024/05/Beyond-static-metrics_-transition-matrix-and-vintage-default-analysis.png "Beyond static metrics_ transition matrix and vintage default analysis") - May 2, 2024 ### Beyond static metrics: transition matrix and vintage default analysis Asset-based finance thrives on data-driven decisions. Spotting trends requires dynamic, easily configurable metrics that can summarize vast datasets and streamline operations. At Cardo AI, we understand these challenges. That’s why we’ve pioneered advancements in transition matrix and vintage default analysis. Read more ](https://cardoai.com/whats_new_in_product/beyond-static-metrics-transition-matrix-and-vintage-default-analysis/) --- ### [Data Management](https://cardoai.com/resources/whats-new-in-product/data-management/) **Published:** April 9, 2026 **Author:** sviluppatore **Content:** # What’s new in product Latest product news and feature releases, highlighting innovations across the Cardo AI platform. Category All - [All](https://cardoai.com/resources/whats-new-in-product/) - [Data Management](https://cardoai.com/resources/whats-new-in-product/data-management/) - [Workflow Management](https://cardoai.com/resources/whats-new-in-product/workflow-management/) - [Portfolio Analytics](https://cardoai.com/resources/whats-new-in-product/portfolio-analytics/) - [Asset & Structure Types](https://cardoai.com/resources/whats-new-in-product/asset-structure-types/) - [Deal Modeling](https://cardoai.com/resources/whats-new-in-product/deal-modeling/) [ ![Collateral integration agents: the engine that onboards your collateral, so you don't have to](https://cardoai.com/wp-content/uploads/2026/07/Collateral-integration-agents.png "Collateral integration agents") - July 14, 2026 ### Onboarding loan tape collateral with AI agents Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. Read more](https://cardoai.com/whats_new_in_product/collateral-integration-agents/) [ ![Infographic: loan-level data to deal data via a central 'flow' circle, with a bottom benefits bar describing the advantages.](https://cardoai.com/wp-content/uploads/2026/05/Beyond-the-loan-tape.png "Beyond the loan tape") - June 4, 2026 ### Beyond the loan tape: how Cardo AI supports the full data landscape of a structured finance deal Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. Read more](https://cardoai.com/whats_new_in_product/beyond-the-loan-tape-how-cardo-ai-supports-the-full-data-landscape-of-a-structured-finance-deal/) [ ![Analytics dashboard UI with left navigation, top tabs, and a data table of asset identifiers and scores.](https://cardoai.com/wp-content/uploads/2026/05/Data-Lineage.png "Data Lineage") - May 20, 2026 ### Data lineage: full transparency on how your data becomes insight Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. Read more](https://cardoai.com/whats_new_in_product/data-lineage-full-transparency-on-how-your-data-becomes-insight/) [ ![](https://cardoai.com/wp-content/uploads/2026/03/AI-powered-document-processing-for-Private-Credit-and-Asset-Based-Finance.png "AI powered document processing for Private Credit and Asset Based Finance") - March 27, 2026 ### AI powered document processing for Private Credit and Asset Based Finance Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. Read more](https://cardoai.com/whats_new_in_product/ai-powered-document-processing-for-private-credit-and-asset-based-finance/) [ ![](https://cardoai.com/wp-content/uploads/2025/12/Data-Intelligence-Engine_-data-profiling-with-more-power.png "Data Intelligence Engine_ data profiling, with more power") - December 18, 2025 ### Data Intelligence Engine: data profiling, with more power Working with financial data in spreadsheets often means reconciling transactions, analyzing loan portfolios, and validating counterparty files under tight timelines. Understanding a loan tape received from an originator, or any dataset provided by a counterparty, can quickly become complex Read more](https://cardoai.com/whats_new_in_product/data-profiling-and-ai-ensure-the-quality-of-your-data/) [ ![](https://cardoai.com/wp-content/uploads/2025/10/Meet-the-Cardo-AI-Excel-Add-in_-your-entire-portfolio-data-one-click-away.png "Meet the Cardo AI Excel Add-in_ your entire portfolio data, one click away") - October 29, 2025 ### Meet the Cardo AI Excel Add-in: your entire portfolio data, one click away If you live in Excel, you shouldn’t have to jump between portals, exports, and manual cleanup just to get the data you need. Our Excel Add-in brings your entire portfolio data straight from our platform into the spreadsheet you already use – securely, consistently, and fast. Read more](https://cardoai.com/whats_new_in_product/meet-the-cardo-ai-excel-add-in-your-entire-portfolio-data-one-click-away/) [ ![](https://cardoai.com/wp-content/uploads/2025/04/Data-Profiling-and-AI_-Ensure-the-quality-of-your-data-1.png "Data Profiling and AI_ Ensure the quality of your data") - April 28, 2025 ### Data Profiling and AI: ensure the quality of your data Working with financial data in Excel presents challenges such as reconciling transactions, analyzing loan portfolios, and verifying bank statements. The process involves connecting different datasets (e.g., loans with cash flows or borrowers), calculating key metrics Read more](https://cardoai.com/whats_new_in_product/data-profiling-and-ai-ensure-the-quality-of-your-data-2/) [ ![](https://cardoai.com/wp-content/uploads/2025/04/Introducing-Document-Reader.png "Introducing Document Reader") - April 10, 2025 ### Introducing Document Reader for credit agreements Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. Read more](https://cardoai.com/whats_new_in_product/introducing-document-reader-for-credit-agreements/) [ ![](https://cardoai.com/wp-content/uploads/2024/04/Maximize-integrity-in-ABF-transactions-with-our-new-Data-Validation-1.png "Maximize integrity in ABF transactions with our new Data Validation") - April 8, 2024 ### Maximize integrity in ABF transactions with our Data Validation The lack of integrity in asset based finance transactions can have severe repercussions, from legal and regulatory issues to reputational damages and financial losses. Therefore, it is important to implement robust controls and governance mechanisms to mitigate these risks. This is where Cardo AI’s Data Validation comes in to help. Read more ](https://cardoai.com/whats_new_in_product/maximize-integrity-in-abf-transactions-with-our-data-validation/) --- ### [Deal modeling](https://cardoai.com/resources/whats-new-in-product/deal-modeling/) **Published:** April 9, 2026 **Author:** sviluppatore **Content:** # What’s new in product Latest product news and feature releases, highlighting innovations across the Cardo AI platform. Category All - [All](https://cardoai.com/resources/whats-new-in-product/) - [Data Management](https://cardoai.com/resources/whats-new-in-product/data-management/) - [Workflow Management](https://cardoai.com/resources/whats-new-in-product/workflow-management/) - [Portfolio Analytics](https://cardoai.com/resources/whats-new-in-product/portfolio-analytics/) - [Asset & Structure Types](https://cardoai.com/resources/whats-new-in-product/asset-structure-types/) - [Deal Modeling](https://cardoai.com/resources/whats-new-in-product/deal-modeling/) [ ![CLO cashflow waterfall model UI showing tranche structure, inputs, and code editor with charts](https://cardoai.com/wp-content/uploads/2026/07/Waterfall-Studio.png "Waterfall Studio") - October 8, 2026 ### Why Excel is the wrong home for a waterfall Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. Read more](https://cardoai.com/whats_new_in_product/why-excel-is-the-wrong-home-for-a-waterfall/) [ ![](https://cardoai.com/wp-content/uploads/2025/08/How-cash-flow-simulation-improves-ABS-portfolios.png "How cash flow simulation improves ABS portfolios") - August 20, 2025 ### How cash-flow simulation improves ABS portfolios Understanding the cash-flows generated by an Asset-Based Security (ABS) is paramount for investors seeking to assess its profitability and make informed decisions. However, predicting future cash-flows can pose a significant challenge, especially given the intricate nature of ABS transactions. Read more](https://cardoai.com/whats_new_in_product/how-cash-flow-simulation-improves-abs-portfolios/) [ ![](https://cardoai.com/wp-content/uploads/2025/03/Automated-financials-and-covenant-monitoring.png "Automated financials and covenant monitoring") - March 10, 2025 ### Automated financials and covenant monitoring Covenants are key conditions in loan agreements that borrowers must meet to maintain financing terms. Lenders and asset managers rely on covenant monitoring to assess financial health and ensure compliance, but manual tracking using spreadsheets is slow, error-prone, and inefficient. Read more](https://cardoai.com/whats_new_in_product/automated-financials-and-covenant-monitoring/) [ ![Payment Waterfall Modeling Simplified](https://cardoai.com/wp-content/uploads/2025/01/Payment-Waterfall-Modeling-Simplified.png "Payment Waterfall Modeling Simplified") - January 30, 2025 ### Payment waterfall modeling, simplified When a company takes on debt, it must pay interest and fees to its capital provider as specified in the credit agreement. These payments are essential to maintain access to funding. A payment waterfall defines the sequence in which lender of a ABF transactions are are paid, typically monthly or quarterly. Read more](https://cardoai.com/whats_new_in_product/payment-waterfall-modeling-simplified/) [ ![](https://cardoai.com/wp-content/uploads/2024/11/PIK-loans_-automating-interest-accruals-and-risk-monitoring.png "PIK loans_ automating interest accruals and risk monitoring") - November 22, 2024 ### PIK loans: automating interest accruals and risk monitoring At Cardo AI, we’ve built a dedicated solution for PIK loans, automating real-time interest accrual tracking and risk monitoring to ensure lenders maintain accurate, up-to-date exposure visibility. Payment-in-Kind (PIK) loans offer companies a way to ease cash flow constraints by deferring interest payments and addi Read more](https://cardoai.com/whats_new_in_product/pik-loans-automating-interest-accruals-and-risk-monitoring/) [ ![](https://cardoai.com/wp-content/uploads/2024/06/Stratification_-the-power-of-layered-risk-analysis.png "Stratification_ the power of layered risk analysis") - June 5, 2024 ### Stratification: the power of layered risk analysis Many originators currently take days to understand the risks associated with your transactions. This is due to their reliance on manual processes for analyzing asset data. These manual processes involve pulling information from internal management systems, classifying each asset or transaction individually, and then running calculations to assess risk. This time-consuming and tedious approach lim Read more](https://cardoai.com/whats_new_in_product/stratification-the-power-of-layered-risk-analysis/) [ ![](https://cardoai.com/wp-content/uploads/2024/05/Multicurrency_-managing-ABF-transactions-across-global-markets.png "Multicurrency_ managing ABF transactions across global markets") - May 15, 2024 ### Multicurrency: managing ABF transactions across global markets No more limits on where you invest globally. With our new Multicurrency feature, you can invest in every currency, and with a click of a button, integrate all your complex ABF transactions into the base currency of your fund. Implementing robust systems, processes, and risk management strategies is Read more ](https://cardoai.com/whats_new_in_product/multicurrency-managing-abf-transactions-across-global-markets/) --- ### [What’s new in product](https://cardoai.com/resources/whats-new-in-product/) **Published:** April 9, 2026 **Author:** michele **Content:** # What’s new in Product Latest feature releases and platform updates. Category All - [All](https://cardoai.com/resources/whats-new-in-product/) - [Data Management](https://cardoai.com/resources/whats-new-in-product/data-management/) - [Workflow Management](https://cardoai.com/resources/whats-new-in-product/workflow-management/) - [Portfolio Analytics](https://cardoai.com/resources/whats-new-in-product/portfolio-analytics/) - [Asset & Structure Types](https://cardoai.com/resources/whats-new-in-product/asset-structure-types/) - [Deal Modeling](https://cardoai.com/resources/whats-new-in-product/deal-modeling/) [ ![](https://cardoai.com/wp-content/uploads/2026/10/Optimal_reference_pool_cover-1.png "Optimal_reference_pool_cover (1)") - October 9, 2026 ### Select, constrain and optimize SRT portfolios on your terms Select the optimal SRT reference pool from thousands of loans in seconds. Set eligibility rules and limits, then optimize for capital relief. Read more](https://cardoai.com/whats_new_in_product/select-constrain-and-optimize-srt-portfolios-on-your-terms/) [ ![CLO cashflow waterfall model UI showing tranche structure, inputs, and code editor with charts](https://cardoai.com/wp-content/uploads/2026/07/Waterfall-Studio.png "Waterfall Studio") - October 8, 2026 ### Why Excel is the wrong home for a waterfall Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. Read more](https://cardoai.com/whats_new_in_product/why-excel-is-the-wrong-home-for-a-waterfall/) [ ![](https://cardoai.com/wp-content/uploads/2026/10/cover-dashboard-pdf-reports-scaled.png "cover-dashboard-pdf-reports") - October 7, 2026 ### From deal dashboard to board-ready report in one click Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. Read more](https://cardoai.com/whats_new_in_product/from-deal-dashboard-to-board-ready-report-in-one-click/) [ ![Collateral integration agents: the engine that onboards your collateral, so you don't have to](https://cardoai.com/wp-content/uploads/2026/07/Collateral-integration-agents.png "Collateral integration agents") - July 14, 2026 ### Onboarding loan tape collateral with AI agents Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. Read more](https://cardoai.com/whats_new_in_product/collateral-integration-agents/) [ ![Infographic: loan-level data to deal data via a central 'flow' circle, with a bottom benefits bar describing the advantages.](https://cardoai.com/wp-content/uploads/2026/05/Beyond-the-loan-tape.png "Beyond the loan tape") - June 4, 2026 ### Beyond the loan tape: how Cardo AI supports the full data landscape of a structured finance deal Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. Read more](https://cardoai.com/whats_new_in_product/beyond-the-loan-tape-how-cardo-ai-supports-the-full-data-landscape-of-a-structured-finance-deal/) [ ![Infographic with a central warning icon labeled 'Manual. Fragmented. Error-prone' connected to icons for notices, tests, and investors, leading to a right-side benefits panel.](https://cardoai.com/wp-content/uploads/2026/06/Why-Rated-Direct-Lending-CLOs-Break-Ops-Teams.png "Why Rated Direct Lending CLOs Break Ops Teams") - June 3, 2026 ### Why Rated Direct Lending CLOs break Ops teams Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. Read more](https://cardoai.com/whats_new_in_product/why-rated-direct-lending-clos-break-ops-teams/) [ ![Analytics dashboard UI with left navigation, top tabs, and a data table of asset identifiers and scores.](https://cardoai.com/wp-content/uploads/2026/05/Data-Lineage.png "Data Lineage") - May 20, 2026 ### Data lineage: full transparency on how your data becomes insight Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. Read more](https://cardoai.com/whats_new_in_product/data-lineage-full-transparency-on-how-your-data-becomes-insight/) [ ![](https://cardoai.com/wp-content/uploads/2026/03/AI-powered-document-processing-for-Private-Credit-and-Asset-Based-Finance.png "AI powered document processing for Private Credit and Asset Based Finance") - March 27, 2026 ### AI powered document processing for Private Credit and Asset Based Finance Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. Read more](https://cardoai.com/whats_new_in_product/ai-powered-document-processing-for-private-credit-and-asset-based-finance/) [ ![](https://cardoai.com/wp-content/uploads/2026/02/Cardo-AI-for-Asset-Managers-and-Insurance-LPs.png "Cardo AI for Asset Managers and Insurance LPs") - February 3, 2026 ### Cardo AI for Asset Managers and Insurance LPs Asset managers running complex private-credit and mortgage-style strategies (direct CRE mortgages, mezzanine, specialty collateral like telecom towers & data centers) face the same bottleneck: one underlying portfolio, many insurance LP “clients,” each with statutory-grade requirements. Read more](https://cardoai.com/whats_new_in_product/cardo-ai-for-asset-managers-and-insurance-lps/) [ ![](https://cardoai.com/wp-content/uploads/2025/12/Data-Intelligence-Engine_-data-profiling-with-more-power.png "Data Intelligence Engine_ data profiling, with more power") - December 18, 2025 ### Data Intelligence Engine: data profiling, with more power Working with financial data in spreadsheets often means reconciling transactions, analyzing loan portfolios, and validating counterparty files under tight timelines. Understanding a loan tape received from an originator, or any dataset provided by a counterparty, can quickly become complex Read more](https://cardoai.com/whats_new_in_product/data-profiling-and-ai-ensure-the-quality-of-your-data/) [ ![](https://cardoai.com/wp-content/uploads/2025/10/Meet-the-Cardo-AI-Excel-Add-in_-your-entire-portfolio-data-one-click-away.png "Meet the Cardo AI Excel Add-in_ your entire portfolio data, one click away") - October 29, 2025 ### Meet the Cardo AI Excel Add-in: your entire portfolio data, one click away If you live in Excel, you shouldn’t have to jump between portals, exports, and manual cleanup just to get the data you need. Our Excel Add-in brings your entire portfolio data straight from our platform into the spreadsheet you already use – securely, consistently, and fast. Read more](https://cardoai.com/whats_new_in_product/meet-the-cardo-ai-excel-add-in-your-entire-portfolio-data-one-click-away/) [ ![](https://cardoai.com/wp-content/uploads/2025/08/How-cash-flow-simulation-improves-ABS-portfolios.png "How cash flow simulation improves ABS portfolios") - August 20, 2025 ### How cash-flow simulation improves ABS portfolios Understanding the cash-flows generated by an Asset-Based Security (ABS) is paramount for investors seeking to assess its profitability and make informed decisions. However, predicting future cash-flows can pose a significant challenge, especially given the intricate nature of ABS transactions. Read more](https://cardoai.com/whats_new_in_product/how-cash-flow-simulation-improves-abs-portfolios/) [Load More](#) --- ### [SME Platform](https://cardoai.com/transactions/sme-platform/) **Published:** March 30, 2026 **Author:** lorenzo **Content:** # **SME Platform** ## Attachment: [Prospectus](https://cardoai.com/wp-content/uploads/2026/03/VALSA_PLATFORM_-_Prospectus-FINAL-VERSION.pdf) [Addendum to the Prospectus](https://cardoai.com/wp-content/uploads/2026/03/VALSA-PLATFORM-Size-Increase-Supplement-FINAL-VERSION-1.zip) [Financial Statement](https://cardoai.com/wp-content/uploads/2026/03/Financial_Statements_SME-1.zip) [Redazione sul bilancio](https://cardoai.com/wp-content/uploads/2026/03/Valsabbina-SME-Platform-SPV-relazione-e-bilancio-al-31-12-2021.pdf) ## Additional Information: Originator: Banca Valsabbina S.C.p.A. Servicer: Banca Valsabbina S.C.p.A. Sub Servicer: NSA S.r.l. Back-up Servicer: Casa di Risparmio Asti S.p.A. Back-up Sub-Servicer: Garanzia Etica Representative of Noteholders: Centotrenta Servicing S.p.A. Computation Agent: Centotrenta Servicing S.p.A. Cash Manager: Banca Valsabbina S.C.p.A. Account Bank: BNP Paribas Securities Services Corporate Servicer: Centotrenta Servicing S.p.A. Arranger: Phinance Partners S.p.A. Reporting Entity: Banca Valsabbina S.C.p.A. Paying Agent: BNP Paribas Securities Services Data Agent: Cardo AI --- ### [Skyline SPV](https://cardoai.com/transactions/skyline-spv/) **Published:** March 30, 2026 **Author:** lorenzo **Content:** # **Skyline SPV** ## Attachment: [Prospectus](https://cardoai.com/wp-content/uploads/2026/03/SKYLINE-SPV-Information-Memorandum-EXE.pdf) ## Additional Information: Paying Agent: CITIBANK, N.A., MILAN BRANCH Originator: AZIMUT INVESTMENTS S.A.. Calculation Agent: CITIBANK, N.A., MILAN BRANCH Underwriter AZ RAIF APA Arranger: BANCA IFIS S.P.A. Account Bank: CITIBANK, N.A., MILAN BRANCH Special Servicer: MO.NET S.P.A. Equity owner of conduit/SSPE FENICE TRUST COMPANY S.R.L Underwriter BANCO SANTANDER S.A. Account Bank BANCO SANTANDER S.A Servicer CENTOTRENTA Servicing Arranger BANCO SANTANDER S.A. Representative of Noteholders CENTOTRENTA SERVICING S.P.A. Special Servicer: CERVED GROUP S.p.A. Underwriter: AZ ELTIF DL Subordinated Loan Provider: BANCA IFIS S.P.A.. Underwriter: AZ RAIF SCB Backup Servicer: NSA S.p.A. Underwriter: BANCA IFIS S.P.A. Underwriter: AZ ELTIF DL II Issuer: SKYLINE SPV S.R.L. Administration Agent: CENTOTRENTA SERVICING S.P.A. --- ### [Kripton SPV](https://cardoai.com/transactions/kripton-spv/) **Published:** March 30, 2026 **Author:** lorenzo **Content:** # **Kripton SPV** ## Attachment: [Prospectus](https://cardoai.com/wp-content/uploads/2026/03/KRIPTON_-_Information_Memorandum_gmAXlZe-1.pdf) ## Additional Information: Issuer: Kripton SPE S.r.l. Originator: ART Sgr. S.p.A. Servicer: Centotrenta Servicing S.p.A. Sub Servicer: MO.NET S.p.A. Back-up Servicer: NSA S.p.A. Reporting Entity ART SGR S.p.A. Account Bank Banca Valsabbina S.C.p.A Representative of Noteholders: Banca Finanziaria Internazionale Cash Manager: Banca Valsabbina S.C.p.A. Corporate Servicer Centotrenta Servicing S.p.A. Paying Agent Banca Finanziaria Internazionale S.p.A. Computation Agent: Centotrenta Servicing S.p.A. Sole Quotaholder Fenice Trust Company S.r.l. Arranger Banca Valsabbina S.C.p.A Loan Provider Banca Valsabbina S.C.p.A Lead Manager Banca Valsabbina S.C.p.A Data Agent Cardo AI --- ### [Galadriel](https://cardoai.com/transactions/galadriel/) **Published:** March 30, 2026 **Author:** lorenzo **Content:** # **Galadriel** ## Attachment: [Prospectus](https://cardoai.com/wp-content/uploads/2026/03/galadriel.pdf) ## Additional Information: Originator: ART Sgr Servicer: Centotrenta Servicing S.p.A. Sub Servicer: MO.NET Back-up Servicer: N/A Back-up Sub-Servicer: N/A Representative of Noteholders: Banca Finanziaria Internazionale Computation Agent: Banca Finanziaria Internazionale Cash Manager: BNP Paribas Securities Services Account Bank: BNP Paribas Securities Services Corporate Servicer: Centotrenta Servicing S.p.A. Arranger: Intesa Sanpaolo, Banca IFIS Reporting Entity: ART Sgr Paying Agent: Banca Finanziaria Internazionale Data Agent: Cardo AI --- ### [Cloud SPV](https://cardoai.com/transactions/cloud-spv/) **Published:** March 30, 2026 **Author:** lorenzo **Content:** # **Cloud SPV** ## Attachment: [Prospectus ](https://cardoai.com/wp-content/uploads/2026/03/SLANCIO-IV-Information-Memorandum_signed.signed.pdf) ## Additional Information: Originator: Azimut ELTIF – Private Debt Digital Lending II Servicer: Centotrenta Servicing Sub-Servicer: Mo.Net Special Servicer: Cerved Back-up Servicer: N/A Back-up Sub-Servicer: NSA Representative of Noteholders: Centotrenta Servicing Computation Agent: Citibank N.A., London Branch Account Bank: Banco Santander S.A., Milan Branch Corporate Servicer: Centotrenta Servicing Arranger: Santander and Banca IFIS Reporting Entity: Skyline SPV Paying Agent: Citibank N.A., London Branch Payments Account Bank: Citibank N.A., Milan Branch --- ### [Auxilio SPV](https://cardoai.com/transactions/auxilio-spv/) **Published:** March 30, 2026 **Author:** lorenzo **Content:** # **Auxilio SPV** ## Attachment: [Prospectus](https://cardoai.com/wp-content/uploads/2026/03/Auxilio-Prospectus-2.pdf) [Relazione sul Bilancio al 31.12.2021](https://cardoai.com/wp-content/uploads/2026/03/Auxilio-SPV-Relazione-sul-bilancio-al-31.12.2021_RC109972021BD1230_PDFA.pdf) [Relazione sul Bilancio al 31.12.2022](https://cardoai.com/wp-content/uploads/2026/03/Auxilio-SPV-Relazione-sul-bilancio-al-31.12.2022-RC109972022BD1804-Fascicolo.pdf) ## Additional Information: Collection Account Bank Banca Valsabbina S.C.p.A. Originator: ART SGR S.P.A. Underwriter: ART SGR S.P.A. Issuer: AUXILIO SPV S.R.L. Underwriter: AZ RAIF I – STRUCTURED CREDIT BRIDGE Account Bank: BANCA VALSABBINA S.C.P.A. Backup Account Bank: BANCA VALSABBINA S.C.P.A.. Underwriter: BANCA FINANZIARIA INTERNAZIONALE S.P.A. Administration Agent CENTOTRENTA SERVICING S.P.A. Seller ART SGR S.P.A. Underwriter BANCA PATRIMONI SELLA & C. S.P.A. Arranger BANCA VALSABBINA S.C.P.A. Underwriter: BANCA VALSABBINA S.C.P.A. Representative of Noteholders: SECURITISATION SERVICES S.P.A. Paying Agent: BANCA FINANZIARIA INTERNAZIONALE S.P.A. Servicer: CENTOTRENTA SERVICING S.P.A. Underwriter: BANCA POPOLARE SANT’ANGELO S.C.P.A. Underwriter: FASANARA INVESTMENTS SA, SICAV – RAIF Securitisation Special Purpose Entity: AUXILIO SPV S.R.L. Calculation Agent CENTOTRENTA SERVICING S.P.A. Special Servicer MO.NET S.P.A. Underwriter BANCA DI CIVIDALE S.C.P.A. Equity owner of conduit/SSPE 130 TRUST COMPANY S.R.L. Cash Manager BANCA VALSABBINA S.C.P.A. --- ### [Cielo SPV s.r.l.](https://cardoai.com/transactions/cielo-spv-s-r-l/) **Published:** March 30, 2026 **Author:** lorenzo **Content:** # **Cielo SPV s.r.l.** ## Attachment: [Prospectus](https://cardoai.com/wp-content/uploads/2026/03/CIELO-Information-Memorandum.pdf) ## Additional Information: Originator: Azimut Investments Servicer: Centotrenta Servicing Sub Servicer: Mo.Net Special Servicer: Cerved Back-up Servicer: N/A Back-up Sub-Servicer: N/A Representative of Noteholders: Centotrenta Servicing Computation Agent: Centotrenta Servicing Cash Manager: BNP Paribas Account: Bank BNP Paribas Corporate Servicer: Centotrenta Servicing Arranger: Intesa Sanpaolo, Banca Ifis Reporting Entity: N/A Paying Agent: BNP Paribas Data Agent: Cardo AI --- ### [Anubi SPE](https://cardoai.com/transactions/anubi-spe/) **Published:** March 30, 2026 **Author:** lorenzo **Content:** # **Anubi SPE** ## Attachment: [ANUBI – Information Memorandum](https://cardoai.com/wp-content/uploads/2026/03/ANUBI-Information-Memorandum.pdf) ## Additional Information: Originator: Azimut ELTIF – Private Debt Digital Lending II Servicer: CENTOTRENTA SERVICING S.P.A Sub Servicer: MO.NET S.P.A. Special Servicer: CERVED GROUP S.P.A Back-up Servicer: N/A Back-up Sub-Servicer: NSA S.P.A Representative of Noteholders: CENTOTRENTA SERVICING S.P.A Computation Agent: CENTOTRENTA SERVICING S.P.A Account Bank BNP PARIBAS Corporate Servicer: CENTOTRENTA SERVICING S.P.A Arranger: NATIXIS S.A., ILLIMITY BANK S.P.A. Reporting Entity: ANUBI SPE Paying Agent: BNP PARIBAS Data Agent: CARDO AI --- ### [Transactions](https://cardoai.com/transactions/) **Published:** March 30, 2026 **Author:** lorenzo **Content:** # **Transactions** --- ### [Research](https://cardoai.com/resources/research/) **Published:** March 3, 2026 **Author:** sviluppatore **Content:** # Research Deep dives into the tech and capital trends reshaping the ABF market. Category All - [All](https://cardoai.com/resources/research/) - [Asset Based Finance](https://cardoai.com/resources/research/asset-based-finance/) - [State of Private Credit](https://cardoai.com/resources/research/state-of-private-credit/) - [US Auto](https://cardoai.com/resources/research/us-auto/) - [Asset Based Lending](https://cardoai.com/resources/research/asset-based-lending/) - [Applied Intelligence](https://cardoai.com/resources/research/applied-intelligence/) [- October 13, 2026 Credit BDC Sector Monitor Q2 2026](https://cardoai.com/reports/credit-bdc-sector-monitor-q2-2026/) [- September 11, 2026 Hidden concentration in Private Credit: why ten managers can be one bet](https://cardoai.com/reports/hidden-concentration-in-private-credit/) [- August 26, 2026 State of Private Credit performance Q2 2026](https://cardoai.com/reports/state-of-private-credit-performance-q2-2026-2/) [- August 13, 2026 Earned or Financed? BDC Distribution Sustainability Monitor](https://cardoai.com/reports/yearned-or-financed-bdc-distribution-sustainability-monitor/) [- July 20, 2026 Credit BDC Sector Monitor Q1 2026](https://cardoai.com/reports/credit-bdc-sector-monitor/) [- June 4, 2026 From Automation to Autonomous ABF operations](https://cardoai.com/reports/from-automation-to-autonomous-abf/) [- May 21, 2026 State of Private Credit performance Q1 2026](https://cardoai.com/reports/state-of-private-credit-performance-q1-2026/) [- May 12, 2026 Liquidity stress testing for ABF. How does a Liquidity structure get stressed and what are the implications?](https://cardoai.com/reports/liquidity-stress-testing-for-abf/) [- March 30, 2026 Market Financial Solutions and the ABF ‘collateral-truth’ problem](https://cardoai.com/reports/market-financial-solutions-and-the-abf-collateral-truth-problem/) [Load More](#) Our research maps where asset-based finance is heading. Our platform gives you the tools to get there. [See what’s new in our Product ](https://cardoai.com/resources/whats-new-in-product/) --- ### [Fund Administrators](https://cardoai.com/case-studies/fund-administrators/) **Published:** March 5, 2026 **Author:** sviluppatore **Content:** # Case Studies | Fund Administrators How we help our clients grow their business Category Asset Managers - [Asset Managers](https://cardoai.com/case-studies/asset-managers/) - [Credit Originators](https://cardoai.com/case-studies/credit-originators/) - [Bank Lenders](https://cardoai.com/case-studies/bank-lenders/) - [Fund Administrators](https://cardoai.com/case-studies/fund-administrators/) Credit Originators [NAV calculation and regulatory reporting](#nav-calculation-and-regulatory-reporting) **Challenge** Calculating NAV across 20+ funds and 60 countries required managing heterogeneous data formats while complying with multiple regulatory frameworks. Manual reconciliation processes slowed reporting cycles and increased operational risk. **Solution** Cardo AI implemented an automated, structure-aware valuation framework that centralized loan-level data into a standardized model across jurisdictions. Daily NAV calculations were executed through rule-based workflows incorporating waterfalls, accruals, FX adjustments, and eligibility logic. Real-time reconciliation ensured data integrity, while embedded regulatory templates enabled automated, audit-ready reporting. **Outcome** - 100% automated daily NAV calculation - Regulatory compliance across jurisdictions - Reporting cycles reduced from days to real time - Scalable framework supporting future fund expansion [Managing esoteric private SPVs (Inventory Finance and Trade Receivables)](#managing-esoteric-private-spvs-inventory-finance-trade-receivables) **Challenge** Managing private SPVs in inventory finance and trade receivables required tracking complex cash flow transformations across multiple asset types. Inventory is purchased, sold, converted into receivables, and repaid under different contractual structures – often across fragmented counterparties and reporting standards. **Solution** Cardo AI implemented a structure-aware cash flow tracking framework capable of following assets from inventory purchase through receivable conversion and final repayment. The platform automated reconciliation of realized cash flows against contractual repayment schedules and liability obligations, while embedding contract-driven compliance logic directly into workflows. Counterparty data was standardized into a single environment, enabling consistent reporting and centralized oversight across multiple private SPVs. **Outcome** - Full end-to-end visibility from inventory sale to receivable repayment - Automated reconciliation aligned with contractual waterfalls - Improved investor transparency with real-time reporting - Optimized liquidity management across SPVs - Scalable framework supporting additional esoteric structures [Streamlining trustee and transaction administration](#streamlining-trustee-and-transaction-administration) **Challenge** A leading corporate trustee managed rising deal volumes and increasingly complex structures that had become a significant operational bottleneck. The team depended on fragmented, inconsistent inputs from various issuers and servicers, which made it difficult to validate data quality before downstream processing. **Solution** Cardo AI supported the trustee with a modern data and transaction operations layer that standardized incoming information, automated core validations, and improved control over recurring deal workflows. This enabled the trustee team to reduce friction in high-volume recurring processes while improving transparency across the entire deal lifecycle **Outcome** - Strengthened stakeholder trust with audit-ready transparency - Shortened reporting timelines by reducing operational friction - Removed reconciliation gaps through standardized data - Handled higher deal volumes with greater precision and speed [Advanced analytics and Stage 2 loan optimization](#advanced-analytics-stage-2-loan-optimization) **Challenge** Identifying early deterioration signals and modeling loan evolution under varying economic scenarios required deeper data integration and predictive capability. **Solution** Cardo AI deployed machine learning models powered by servicer data, partner bank data, European securitization datasets, and alternative data sources. The platform analyzed historical payment behavior, borrower characteristics, and macroeconomic indicators to predict deterioration probabilities, simulate loan evolution, and evaluate management strategies such as restructurings, write-offs, or retention. Scenario engines enabled forward-looking return modeling under multiple economic conditions. **Outcome** - Proactive identification of at-risk loans before delinquency - Earlier mitigation actions reducing portfolio deterioration - Data-driven workout strategy evaluation - More accurate expected return forecasting under multiple scenarios - Enhanced portfolio health through predictive classification See how our clients leverage Cardo AI to manage their most sophisticated asset-based structures at scale. [Explore our Deal Desk](https://cardoai.com/company/contact-us/) --- ### [Credit Originators](https://cardoai.com/case-studies/credit-originators/) **Published:** March 5, 2026 **Author:** sviluppatore **Content:** # Case Studies | Credit Originators See how we help our clients grow their business Category Credit Originators - [Asset Managers](https://cardoai.com/case-studies/asset-managers/) - [Credit Originators](https://cardoai.com/case-studies/credit-originators/) - [Bank Lenders](https://cardoai.com/case-studies/bank-lenders/) - [Fund Administrators](https://cardoai.com/case-studies/fund-administrators/) Credit Originators [Streamlining due diligence to attract investors](#streamlining-due-diligence-to-attract-investors) **Challenge:** Manual due diligence processes slowed investor onboarding, limited transparency, and increased the cost of capital. Investor requests required repetitive data preparation, manual reconciliations, and static reporting formats. **Solution** Cardo AI deployed a fully digital, interactive due diligence environment featuring ready-to-use KPIs, stratifications, portfolio cash flow simulations, stress testing tools, and automated regulatory reporting. A digital Q&A workflow streamlined investor communication. **Outcome** - 15x originated volumes - €1BN+ notes issued across 10+ securitizations - 90% reduction in due diligence response time - 100% data reliability supported by 600+ automated checks [Boosting transparency with automated investor reporting](#boosting-transparency-with-automated-investor-reporting) **Challenge:** Managing 20+ investor reporting templates manually created delays, inconsistencies, and operational inefficiencies. Aggregating and reconciling loan-level data required significant time and coordination. **Solution** Cardo AI automated loan-level aggregation, analysis, and dashboard reporting within a cloud-based environment. Investors accessed customizable, real-time performance views aligned with their reporting requirements. **Outcome** - 100% real-time data delivery - 90% faster response times - Higher reporting accuracy through automation - Enhanced investor engagement through interactive dashboards [Automating borrowing base calculations for faster borrower onboarding](#automating-borrowing-base-calculations-for-faster-borrower-onboarding) **Challenge:** Manual borrowing base reporting and funding requests were time-consuming and prone to eligibility errors, particularly across warehouse and fund finance lines. **Solution:** Cardo AI automated data ingestion, eligibility and concentration checks, advance rate calculations, and payment allocations across facilities and tranches. All contractual provisions were executed through rule-based workflows. **Outcome:** - Daily full data control (including position reconciliation with cash flows) - Time saved due to automated borrowing base calculations and validation - Unlimited automated borrower checks - Faster onboarding of new borrowers [Advanced collateral monitoring and data governance](#advanced-collateral-monitoring-and-data-governance) **Challenge:** Scaling lending operations is hindered by fragmented internal data silos and manual reporting cycles. Managing high-volume, complex asset pools across various spreadsheets makes it difficult to maintain real-time data integrity, detect internal anomalies, or provide the transparency required by institutional funders. **Solution:** The platform centralizes internal data streams into a single, governed source of truth. It automates the “scrubbing” and standardization of loan-level data, applying rigorous validation rules and performance tracking (IRR, delinquency, and defaults) to ensure the portfolio is always audit-ready. **Outcome:** - End-to-end automation of investor-ready data preparation - 90% faster internal audit and reconciliation cycles - Minimal reporting errors through automated validation logic - 4x increase in capacity to manage new warehouse lines - Instant visibility into asset-level performance and concentration limits [Centralizing loan data management for seamless stakeholder alignment](#centralizing-loan-data-management-for-seamless-stakeholder-alignment) **Challenge: Maintaining a single “source of truth” across high-volume SME lending and structured facilities was a constant operational friction point. Disparate data formats and definitions between originators, warehouse lenders, and investors created significant manual reconciliation work. As transaction complexity scaled, it became difficult to ensure all stakeholders were aligned on the latest, most reliable data for monthly surveillance and compliance. **Solution:** Cardo AI acted as the loan data agent at the center of the transaction. Our platform received source files from the relevant parties, validated and standardized them, and produced clean downstream outputs tailored to reporting, surveillance, compliance, analytics, and investor use cases. Rather than leaving each participant to solve the same data problems independently, Cardo AI created a governed and auditable data backbone for the deal. **Outcome:** - Automated validation of high-volume SME loan data - Reduced friction in monthly reporting and surveillance cycles - Full historical traceability for audit and regulatory compliance - Standardized definitions across internal and external stakeholders See how our clients leverage Cardo AI to manage their most sophisticated asset-based structures at scale. [Explore our Deal Desk](https://cardoai.com/company/contact-us/) --- ### [Bank Lenders](https://cardoai.com/case-studies/bank-lenders/) **Published:** March 5, 2026 **Author:** sviluppatore **Content:** # Case Studies | Bank Lenders How we help our clients grow their business Category Bank Lenders - [Asset Managers](https://cardoai.com/case-studies/asset-managers/) - [Credit Originators](https://cardoai.com/case-studies/credit-originators/) - [Bank Lenders](https://cardoai.com/case-studies/bank-lenders/) - [Fund Administrators](https://cardoai.com/case-studies/fund-administrators/) Bank Lenders [Automating borrowing base verification and credit monitoring](#automating-borrowing-base-verification-and-credit-monitoring) **Challenge** Manual borrowing base verification delayed funding availability and increased the risk of eligibility and concentration errors. Monitoring limits and exceptions required significant manual review and internal reporting coordination. **Solution** Cardo AI deployed a rule-based borrowing base engine that modeled liabilities, advance rates, and waterfalls directly within the platform, comparing it to provided values. Loan-level data was automatically validated, eligibility and concentration checks were executed in real time, and risk-adjusted haircuts were applied dynamically. Exception alerts and automated internal reporting streamlined approval workflows. **Outcome** - Processing time reduced from 2–3 days to 4 hours - 35% decrease in eligibility calculation errors - Increased facility utilization - Faster borrower access to eligible funding [Accelerating capital call execution and cash flow accuracy](#accelerating-capital-call-execution-and-cash-flow-accuracy) **Challenge** Inefficient capital call execution and liquidity forecasting created inaccurate cash flow projections and increased administrative workload. Manual triggers, static forecasting models, and limited visibility across funding sources made it difficult to anticipate liquidity needs and optimize capital deployment. **Solution** Cardo AI automated capital call scheduling and execution, removing reliance on manual processes. The platform forecasted cash flow requirements based on historical patterns and upcoming fund obligations, while dynamically adjusting capital buffers across multiple funding sources to optimize liquidity allocation. **Outcome** - 70% reduction in capital call processing time - Lower liquidity shortfall incidents - Improved fund stability and investor confidence - Operational teams refocused on strategic cash management [Real-time risk monitoring and concentration control](#real-time-risk-monitoring-and-concentration-control) **Challenge** The absence of real-time risk monitoring led to delayed detection of concentration risk, increased exposure across asset managers and underlying debtors, and missed opportunities for early mitigation. Risk analysis relied heavily on manual reporting cycles, limiting proactive decision-making. **Solution** Cardo AI implemented automated concentration risk calculations across asset managers and underlying obligors, combined with risk-adjusted return analysis across investment lines. Real-time monitoring and automated alerts enabled continuous oversight and supported regulatory compliance and proactive capital allocation decisions. **Outcome** - 70% faster risk detection - 50% reduction in manual effort - Immediate notifications for risk threshold breaches - More accurate and optimized risk-adjusted returns [Optimizing direct lending and syndicated facilities](#optimizing-direct-lending-and-syndicated-facilities) **Challenge** A global lender managed highly bespoke bilateral and club deals that lacked market standardization. The team depended on spreadsheet-driven processes to track complex lender allocations and struggled to maintain consistent data as facilities underwent frequent amendments. This reliance on manual tracking limited operational scalability and made it difficult to audit cash movements and economics as the portfolio grew. **Solution** Cardo AI provided a purpose-built operating environment for direct lending facilities, allowing managers and lenders to track positions, deal terms, lender economics, and lifecycle events in a consistent structure-aware model. Our platform orchestrated the management of complex lifecycle events, including accruals and bespoke amendments, ensuring that origination, operations, and finance teams remained perfectly aligned. **Outcome** - Strengthened stakeholder trust with audit-ready transparency - Shortened reporting timelines by reducing operational friction - Removed reconciliation gaps through standardized data - Handled higher deal volumes with greater precision and speed [Automating ESMA reporting](#automating-esma-reporting) **Challenge** A leading specialty finance bank faced the intense operational demands of ESMA compliance for its structured finance and factoring portfolios. Producing accurate, timely templates required assembling massive datasets from fragmented loan systems and third-party servicers. **Solution** The bank deployed Cardo AI as its governed source of truth to automate the end-to-end ESMA reporting workflow. The platform integrated data directly from various transaction participants, applying automated completeness and consistency validations before mapping fields into the required regulatory templates. By establishing a repeatable production framework, the firm replaced manual data repair with a structured environment featuring version control, audit trails, and clear review checkpoints for every submission. **Outcome** - Strengthened stakeholder trust with audit-ready transparency - Ensured 100% compliance with complex regulatory mapping - Eliminated late-cycle data errors through automated validation - Reduced the manual reporting burden by standardizing source data See how our clients leverage Cardo AI to manage their most sophisticated asset-based structures at scale. [Explore our Deal Desk](https://cardoai.com/company/contact-us/) --- ### [Deal Desk](https://cardoai.com/deal-desk/) **Published:** January 20, 2026 **Author:** sviluppatore --- ### [Partners](https://cardoai.com/company/partners/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** LEADING THE WAY # Building business, together Our tech stack provides partners direct access to the most advanced solutions in the market. [Talk to us](https://cardoai.com/company/contact-us/) OUR PARTNERS ![wiserfunding](https://cardoai.com/wp-content/uploads/2026/02/wiserfunding.svg) ![prometeia](https://cardoai.com/wp-content/uploads/2026/02/logo_chiaro.svg) ![deloitte](https://cardoai.com/wp-content/uploads/2026/02/deloitte-logo-black-and-white.svg) ![pwc](https://cardoai.com/wp-content/uploads/2026/02/pwc_logo.svg) ![fis](https://cardoai.com/wp-content/uploads/2026/02/fis_logo-white.svg) ![arcesium](https://cardoai.com/wp-content/uploads/2026/02/arcesium.svg) ![credit-benchmark](https://cardoai.com/wp-content/uploads/2026/02/credit-benchmark.svg) ![rolf](https://cardoai.com/wp-content/uploads/2026/02/rolf.svg) ## Why partner with Cardo AI? We integrate our technology with the strategic expertise of global leaders to deliver end-to-end ABF solutions. ![](https://cardoai.com/wp-content/uploads/2026/04/parter-co-marketing.png "parter-co-marketing") Co-marketing Collaborate on joint events, thought leadership articles, use cases, and social media. ![](https://cardoai.com/wp-content/uploads/2026/04/partner-support.png "partner-support") Client support Dedicated support for smooth delivery and ongoing product enablement. ![](https://cardoai.com/wp-content/uploads/2026/04/partner-training.png "partner-training") Training Training sessions across the full product suite and constant updates as the platform features evolve. ![](https://cardoai.com/wp-content/uploads/2026/04/partner-Relationship-Manager.png "partner-Relationship Manager") Relationship Manager Dedicated Relationship Manager for joint go-to- market actions. $115B+ Assets Managed with our Technology 30+ Asset Classes 3000Tn+ Data Points Integrated ## Featured case studies How Blackstone uses Cardo AI to drive operational leverage in asset based finance operations. [Read more](https://cardoai.com/wp-content/uploads/2026/03/blackstone-use-case.pdf) How Swishfund automates regulatory reporting for private securitization transaction. [Read more](https://cardoai.com/announcements/swishfund-partners-with-cardo-ai-to-automate-esma-reporting-for-private-securitization-transaction/) How Multitude Bank scales SME asset-based lending by automating ABL operations. [Read more](https://cardoai.com/announcements/cardo-ai-powers-multitude-banks-asset-based-lending/) ### Ready to explore a partnership? Reach out to our partnerships team and see how you can join our network. [Talk to us](https://cardoai.com/company/contact-us/) --- ### [Faq](https://cardoai.com/company/faq/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** FAQ # Frequently Asked Questions Quick answers to common questions about Cardo AI, the platform, and how we work. General - [General](https://webincostruzione1.it/cardo_ai/company/faq/) - [Product](https://webincostruzione1.it/cardo_ai/company/faq/product/) - [Tech](https://webincostruzione1.it/cardo_ai/company/faq/tech/) - [Data](https://webincostruzione1.it/cardo_ai/company/faq/data/) [What does “Cardo AI” mean?](#what-does-cardo-ai-mean) Named after the Roman cardo, a main street that ran north-south in ancient cities, Cardo AI is the central hub of the structured credit industry. Just as the cardo was a bustling marketplace, we aim to connect financial institutions and streamline transactions. Leveraging artificial intelligence (AI), we employ advanced analytics to improve the structured credit market’s efficiency and connectivity. [Who founded Cardo AI and why?](#who-founded-cardo-ai-and-why) Founded in 2018 by Altin Kadareja (CEO) and Euron Metaliaj (CTO), Cardo AI is a global fintech company with offices in New York, London, and Milan, and a tech hub in Albania and Kosovo. We help banks, servicers, and asset managers streamline structured credit transactions by saving time, reducing errors, lowering costs, and improving operational efficiency. [What size company is Cardo AI suitable for?](#what-size-company-is-cardo-ai-suitable-for) Our technology is designed to be highly adaptable, capable of serving both individual transactions and the broader needs of large financial institutions. By focusing on the immediate needs of a single transaction, we can optimize processes and deliver results quickly. For major financial institutions, we can be seamlessly integrated into existing systems and workflows. As the institution’s needs grow, Cardo AI’s architecture can easily scale to accommodate increased workload and complexity. [Is Cardo AI a regulated company?](#is-cardo-ai-a-regulated-company) No, while we work closely with regulated entities, we do not engage in any activities that would require us to be regulated by financial authorities. This ensures that we maintain a clear distinction between our role as a technology provider and the regulated activities performed by our clients. [Why are you different from other firms in the space?](#why-are-you-different-from-other-firms-in-the-space) Our cutting-edge technology for structured credit sets us apart by delivering unmatched efficiency. With real-time data and insights, you can make informed decisions faster and more accurately. We seamlessly integrate diverse financial structures with any underlying asset class, whether traditional or innovative, ensuring swift and precise data handling to get our tech up and running in no time. Unlike others, our AI capabilities are embedded throughout the entire process—from data extraction and insight generation to forecasting and portfolio optimization—giving you a comprehensive advantage. ### Don’t see your question? Leave us a message. We’ll get back to you shortly. [Contact us](https://cardoai.com/company/contact-us/) --- ### [Security](https://cardoai.com/company/security/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** SECURE FROM EVERY ANGLE # Your data are in safe hands We run a security and compliance program to protect customer data and meet regulated institutions’ expectations. ## We don’t compromise on security Security at Cardo AI is governed through defined ownership, oversight, and regular review. Detailed security controls, certifications, and assurance materials are available via our [Trust Centre](#) under NDA. ## We don’t compromise on security We regularly review and enhance security measures to address emerging threats and changes in the wider threat landscape. ![penetration testing](https://cardoai.com/wp-content/uploads/2026/02/penetration-testing.svg) ### Penetration testing: We run internal and independent tests to to identify, and remediate vulnerabilities in line with business risk. ![Proactive measures](https://cardoai.com/wp-content/uploads/2026/02/Proactive-measures.svg) ### Proactive measures: We reduce risk using layered protections such as access controls, network security, and encryption. ![employee training](https://cardoai.com/wp-content/uploads/2026/02/employee-training.svg) ### Employee training: All employees undertake mandatory training covering cyber threats, phishing risks, and data handling responsibilities. ![24/7 monitoring](https://cardoai.com/wp-content/uploads/2026/02/monitoring.svg) ### 24/7 monitoring: We constantly monitor our systems to detect and respond to security events in line with regulatory expectations. ![SOC2](https://cardoai.com/wp-content/uploads/2026/03/SOC-1-150x150.png "SOC") Our security practices are SOC 2 Type II compliant, demonstrating the effectiveness of our controls over time and our commitment to protecting customer data and platform reliability. [Read more](https://cardoai.com/article/cardo-ai-achieves-soc2-type-2-certification/#second-par) ## We are resilient by design Seamless operations, secure foundation, reliable infrastructure. [![Security-1](https://cardoai.com/wp-content/uploads/2026/03/Security-1-550x200.png "Security-1")](https://cardoai.com/wp-content/uploads/2026/03/Security-1-1024x372.png) Your business, uninterrupted Even during cyber incidents, you keep operating. Our platform is built for resilience, so your workflows and data stay available and protected. [![Security-2](https://cardoai.com/wp-content/uploads/2026/03/Security-2-550x200.png "Security-2")](https://cardoai.com/wp-content/uploads/2026/03/Security-2-1024x372.png) Developed with security in mind Security is built in from day one at Cardo AI. Our development process includes in-depth testing of every line of code. [![Security-3](https://cardoai.com/wp-content/uploads/2026/03/Security-3-550x200.png "Security-3")](https://cardoai.com/wp-content/uploads/2026/03/Security-3-1024x372.png) An infrastructure you can trust We operate on a secure, cloud-native infrastructure designed to protect your data, minimise risk, and support compliance with industry standards ## Want to go deeper? Detailed security controls, certifications, and assurance materials are available under NDA. [Visit our Trust Centre](https://trust.cardoai.com/) --- ### [Contact us](https://cardoai.com/company/contact-us/) **Published:** January 20, 2026 **Author:** sviluppatore **Content:** CONTACT US # Speak to our team We’re always happy to connect. Whether you’re exploring Cardo AI, looking for support, interested in a partnership, or reaching out for press and events, send us a note here. We’ll route your message to the right team and follow up shortly with next steps. 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For more information, check out our [Privacy Policy.](https://cardoai.com/privacy-e-cookie-policy/)(Required) [LinkedIn](https://www.linkedin.com/company/cardoai/)[Instagram](https://www.instagram.com/cardoai_/)[Twitter](https://x.com/CARDO_AI) ## A global footprint connecting us to you ![Mappa](https://cardoai.com/wp-content/uploads/2026/03/Mappa.svg) OFFICE LOCATIONS ![](https://cardoai.com/wp-content/uploads/2026/02/pallino.svg) NEW YORK 4 World Trade Center, 2909, NY 10007 United States ![](https://cardoai.com/wp-content/uploads/2026/02/pallino.svg) LONDON 40 New Bond St London, W1S 2DE United Kingdom ![](https://cardoai.com/wp-content/uploads/2026/02/pallino.svg) MILAN Via S.Francesco d’Assisi 15, 20122 Italy TECH HUB ![](https://cardoai.com/wp-content/uploads/2026/02/pallino.svg) TIRANA Vaso Pasha Street, Rainbow center Albania ![](https://cardoai.com/wp-content/uploads/2026/02/pallino.svg) PRISHTINA Street Musa Tolaj, Lam A Kosovo ## Join our newsletter Get the latest asset-based finance trends and Cardo AI product updates straight to your inbox. By subscribing, you agree to our [Privacy Policy](https://cardoai.com/privacy-e-cookie-policy/) --- ### [Privacy policy](https://cardoai.com/privacy-e-cookie-policy/) **Published:** August 24, 2021 **Author:** michele **Content:** # **Privacy Policy** This Privacy Policy is applicable to the Cardo AI joint controllers, which collectively determine the purposes and means of processing personal data. The Joint Controllers are: - Cardo AI Corporation (US) - Cardo AI Corporation – Italy Branch, with its registered office at Francesco Assisi 15, CAP 20122, Milan (MI), Italy - Softics Data Science (Albania) - Cardo AI Ltd (UK) - Cardo ShPK (Kosovo) Together, these joint controllers are referred to as Cardo AI throughout this Privacy Policy. General contact information: support[@cardoai.com](mailto:hello@cardoai.com) Contact Information Data Protection and Privacy: ### **1) Whose personal data do we process?** Cardo AI processes Personal Data of different categories of data subjects, and possibly does so under different capacities. Cardo AI as the **Joint Data Controller** - Professional Contacts - B2B Marketing Contacts - Visitors of our Website - (Former) Employees Cardo AI as the **Data Processor** - Our End Users - Stakeholders from our Clients (employees, customers, etc.) This Privacy Policy is only applicable to End Users, Professional Contacts, B2B Marketing Contacts and Visitors of our Website. It is not applicable to Cardo AI’s staff. ### **2) Types of personal data we collect and process** Cardo AI processes the following categories of personal data: - **Identification Information**: e.g. First and Last Name, Date of Birth, Usernames, etc. - **Contact Information**: e.g. Email, Phone Number, Address, etc. - **Professional Information**: e.g. Organization, Job or Function, Professional Email, etc. - **Usage Data**: e.g. the way you use Cardo AI, the pages you visit on our website and the activity in your account, etc. - **Browser and Session Information**: e.g. Device Information, IP addresses, browser type, operating system, session statistics, etc. More details on each type of Personal Data collected are provided in the dedicated sections of this privacy policy or by specific explanation texts displayed prior to the Data collection. ### **3) For what purposes and on what legal basis do we process your personal data?** Cardo AI only collects and processes your personal data on the basis of legitimate legal grounds and for specified and documented purposes as described in this Privacy Policy. 3.1 Based on a **legal obligation**, Cardo AI is obligated to process your personal data in specific circumstances and for certain purposes in the context of our activities, such as: **Purposes****Processed Personal Data****Data Subjects**Obligation to respond to legal processes or enforceable requests from government authorities; - Identification Information - Contact Information - Professional Information - Usage Data - Browser and Session Information - Our End Users - Professional Contacts - B2B Marketing Contacts - Visitors of our Website - Data Subjects uploaded by our Clients To comply with administrative and accounting obligations;- Identification Information - Professional Information - Professional Contacts 3.2 Based on the **performance of an agreement** or **prior to the conclusion of an agreement** with Cardo AI, we can process: **Purposes****Processed Personal Data****Data Subjects**Create an **account** and set the correct levels of access; - Identification information - Email address - Professional information - Our End Users Provide our requested services; - Identification information - Contact information - Our End Users - Professional Contacts - Data Subjects uploaded by our Clients To ensure the security of our services;- Identification information - Our End Users - Professional Contacts To improve the service; - Identification information - Contact information - Our End Users - Professional Contacts To provide you with support when you encounter problems or have questions related to our services; - Identification information - Contact information - Any type of personal they provide in the ticket - Our End Users Processing invoices, payments and billings; - Identification information - Contact information - Professional information - Professional Contacts Account management and renewal notifications; - Identification information - Contact information - Professional information - Professional Contacts 3.3 On the basis of our **legitimate interest**, your personal information will be processed for: **Purposes****Processed Personal Data****Data Subjects**The technical and functional management of our website (cfr. the cookie policy) - Browser and Session information - Visitors of our Website Sending commercial information, invitations to events and webinars, invitations to orientation and demo sessions, and other marketing purpose events.- Identification Information - Contact Information - Professional Information - Browser and Session Information - Professional Contacts - B2B Marketing Contacts 3.4 Based on your **permission**, we process personal data for: **Purposes****Processed Personal Data****Data Subjects**Analyzing the behavior of website users through cookies (cfr. the cookie policy); - Browser and Session information - Visitors of our website ### **4) Who does Cardo share your personal data with?** **4.1 Data Processors** Cardo AI has carefully selected third parties and their services to help us provide, improve, protect and promote our Services. These trusted third parties will have access to (some of) your personal data. We will only share the strictly necessary personal information w We use the following categories of third parties: - IT services, - Software suppliers, - Advertising and communication services, - Accounting, tax or legal advisors, - Customer management software, - Compliance software. To ensure compliance throughout our entire supply chain, we will always conclude an agreement laying down rules and obligations related to the protection of personal data. **4.2 Authorities** We may share your personal data with relevant authorities, but only in response to official requests that are legally binding and backed by sufficient legal justification. We are committed to ensuring that any disclosure complies with applicable data protection laws and will only release the minimum information necessary to fulfill such requests. ### **5) Data Retention at Cardo AI** Personal data will be processed only for as long as necessary to fulfill the purposes for which it was collected, unless we are legally or judicially obliged to store your personal data for a longer period. In the latter case, your personal data will be deleted as soon as the legally determined period has passed. - Personal data collected for purposes related to our Services will be kept at least until those Services have been fully executed and ended. - For data used to send newsletters or for direct marketing, it will be kept as long as you remain subscribed. Once the retention period expires, personal data shall be deleted and/or destroyed within a reasonable period of time. ### **6) Security and safety of your personal data** To ensure the security and safety of your personal data, we adhere implemented the principles and measures as stated in SOC 2 and hold a **SOC 2 Certification**. This certification reflects our commitment to maintaining high levels of security, availability and privacy in managing your personal data. The SOC 2 compliance means we follow a robust set of technical and organizational controls and best practices, including regular security audits, vulnerability assessments and encryption protocols. For instance, we employ advance firewalls, implemented intrusion detection systems, etc. *SOC 2 is widely recognized as a leading standard for data security for service providers that manage and store personal data and information. It focuses on ensuring high levels of security, availability, processing integrity, confidentiality and privacy.* ### **7) What are your rights** You can always contact us to exercise the following rights: - Access to and inspection of your Personal Data and all information regarding their processing. - Deletion of your Personal Data. - Restriction of the processing of your Personal Data. - Object to all or certain purposes in order for us to stop processing for this purpose. - Withdrawal of your consent, however this does not affect the lawfulness of processing based on your consent before its withdrawal. - Transfer of your Personal Data. Cardo AI will always facilitate the exercise of your rights, taking into account the legal possibilities and limits in this regard. You will be informed within one month at the latest of what action will be taken on your request. In the case of complex or frequent requests, this period may be extended by a further two months, of which you will be notified. You can exercise your rights by sending a request via e-mail to . It is always possible to submit a complaint to your national data protection authority. ### **8) Adjustments to this Privacy Policy** Cardo AI reserves the right to review and adapt this Privacy Policy at any time in accordance with applicable laws and regulations. ### **9) Change Register** - 11/2024: complete revision of old Privacy Policy - 06/2025: revision of entity names --- ## Events and webinar ### [Get ABF Off Spreadsheets](https://cardoai.com/events_and_webinar/get-abf-off-spreadsheets/) **Published:** September 15, 2026 **Author:** GiadaCardo26 **Excerpt:** September 15, 2026 --- ### [Transparency, Technology, and the Next Frontier of Private Credit](https://cardoai.com/events_and_webinar/transparency-technology-and-the-next-frontier-of-private-credit/) **Published:** June 26, 2026 **Author:** GiadaCardo26 **Excerpt:** June 26, 2026 --- ### [From Spreadsheets to AI: Modernizing Asset-Based Finance](https://cardoai.com/events_and_webinar/from-spreadsheets-to-ai-modernizing-asset-based-finance/) **Published:** June 26, 2026 **Author:** GiadaCardo26 **Excerpt:** June 26, 2026 --- ## Press mentions ### [Encina Lender Finance, Cardo AI Partner on Asset-Based Finance](https://cardoai.com/press_mentions/encina-lender-finance-cardo-ai-partner-on-asset-based-finance/) **Published:** February 13, 2025 **Author:** GiadaCardo26 --- ### [Blackstone and FINTOP Capital lead $15m investment in FinTech startup Cardo AI](https://cardoai.com/press_mentions/blackstone-and-fintop-capital-lead-15m-investment-in-fintech-startup-cardo-ai/) **Published:** November 21, 2024 **Author:** GiadaCardo26 --- ### [Established asset managers seek scale in European ABF](https://cardoai.com/press_mentions/established-asset-managers-seek-scale-in-european-abf/) **Published:** October 23, 2025 **Author:** GiadaCardo26 --- ### [ABF's data dilemma: how tech talents are driving change into private credit](https://cardoai.com/press_mentions/abfs-data-dilemma-how-tech-talents-are-driving-change-into-private-credit/) **Published:** September 8, 2025 **Author:** sviluppatore --- ### [Markets Technology Awards 2026: March of the riskbots](https://cardoai.com/press_mentions/markets-technology-awards-2026-march-of-the-riskbots/) **Published:** November 25, 2025 **Author:** sviluppatore --- ### [Cardo AI Q&A: Shining a Light on Private Markets](https://cardoai.com/press_mentions/cardo-ai-qa-shining-a-light-on-private-markets/) **Published:** February 11, 2026 **Author:** GiadaCardo26 --- ### [Cardo AI Launches Cash Flow Modeling Tool for Asset-Based Finance](https://cardoai.com/press_mentions/encina-lender-finance-and-cardo-ai-partnership-aims-to-revolutionize-abf-market/) **Published:** June 10, 2026 **Author:** GiadaCardo26 --- ### [FinTech Innovation Lab New York Announces 2025 Class](https://cardoai.com/press_mentions/fintech-innovation-lab-new-york-announces-2025-class/) **Published:** April 23, 2025 **Author:** GiadaCardo26 --- ## Announcements ### [Cardo AI named Category Leader in Chartis 2026 RiskTech Quadrants®](https://cardoai.com/announcements/cardo-ai-named-among-category-leaders-in-chartis-2026-credit-risk-research/) **Published:** July 27, 2026 **Author:** GiadaCardo26 **Content:** LONDON/NEW YORK – JULY 27, 2026: Cardo AI has been recognized as a category leader in Chartis [Credit Risk Management Solutions](https://www.chartis-research.com/financial-risk/7947531/credit-risk-management-solutions-2026-quadrant-update) research, which assesses the global landscape for credit risk management (CRM) and credit portfolio management (CPM). The recognition spans three Chartis 2026 RiskTech Quadrants®: - **Traded Credit Solutions** - **Collateralized Loan Obligation (CLOs) Solutions** - **Alternative Credit Solutions** Chartis’ quadrant framework evaluates vendors across two dimensions: completeness of offering and market potential. Vendors classified as category leaders, according to Chartis, demonstrate strength across a broad set of technological capabilities alongside a strong overall market presence. The research looks at the software platforms and managed services helping financial institutions navigate a pivotal shift in how credit is managed, as the industry moves away from periodic, point-in-time risk assessments toward continuous credit intelligence, an approach where exposures are monitored, assessed, and optimized in real time through integrated data and advanced analytics. “*We built Cardo AI to bring the same transparency to illiquid, private markets that investors have long taken for granted in liquid ones.”* said **Altin Kadareja, Co-Founder & CEO of Cardo AI**. *“This recognition reflects our commitment to giving our clients true end-to-end, real-time command over their credit exposures.*” This recognition follows Cardo AI’s recent placement as a defining ABF specialist in [Chartis Research 2026 Global Rankings](https://www.chartis-research.com/financial-risk/7947531/credit-risk-management-solutions-2026-quadrant-update), where we ranked among the top platforms in both the BuySideRisk 50 and QuantTech 50. **FREQUENTLY ASKED QUESTIONS** 1\) **What is Chartis Credit Risk Management Solutions report**? It assesses vendors’ platform and analytical capabilities, product functionality and adoption of advanced technologies, and covers traded credit, market-linked credit, collateralized loan obligations, credit curves, retail credit, wholesale credit, alternative credit and credit portfolio management. 2\) **How are vendors categorized in the Chartis RiskTech Quadrants®?** Players are classified into four categories: Category Leaders, Best-of-Breed, Enterprise Solutions, and Point Solutions. This grid evaluates whether a vendor provides broad or specialized functionality, alongside their overall market adoption and commercial momentum. 3\) **Why is the** **Chartis Credit Risk Management Solutions report important?** This report matters because it validates a vendor’s technical capability and market reliability, helping financial institutions quickly identify proven platforms that can handle complex private credit and risk workflows. --- ### [Cardo AI empowers the inventory and trade receivables securitization structured by illimity in collaboration with Accounting Partners and Banca Sistema](https://cardoai.com/announcements/cardo-ai-empowers-the-inventory-and-trade-receivables-securitization-structured-by-illimity-in-collaboration-with-accounting-partners-and-banca-sistema/) **Published:** January 31, 2024 **Author:** RobertaCardo26 **Content:** MILAN – JANUARY 31, 2024: Cardo AI has recently participated as a tech enabler to +77M Euros securitization structured by illimity in collaboration with Banca Sistema and Accounting Partners in favor of Trasteel Trading Holding S.A., an industrial and trading group active in the steel, energy, and metal sectors. This transaction is exceptional in the European securitization landscape as it generates liquidity from the entire working capital by including trade receivables and inventory assets. Moreover, an innovative legal instrument is being used for the first time by adopting a non-possessory revolving pledge to secure the investment. This securitization is an excellent illustration of how technological advancements and enhanced data availability can facilitate the development of more dynamic and customized securitization models. This, in turn, can unlock new funding opportunities to support the real economy in today’s challenging times. To increase funding capacity, it is important for businesses to access new sources of finance and leverage technology to manage complex financial transactions. Our analytics & data management infrastructure, combined with our partners’ extensive experience in securitization structuring and management, played a crucial role in the success of this complex deal. Cardo AI’s technology enabled all parties involved in this securitization to make quick data-driven decisions to evaluate assets and invoices throughout the transaction’s lifecycle. “*This transaction marks the beginning of our partnership with Accounting Partners. Our goal is to introduce cutting-edge and innovative solutions for securitization management using technology and big data,”* said **Altin Kadareja, CEO and Co-Founder of Cardo AI**. *“By adopting this approach, we are confident that it will generate value for all market participants enabling them to manage their portfolios more efficiently”.* *“Accounting Partners has been active in the securitization market for over 15 years. We recognized the need to partner with a technology player that could help us complement our capabilities with artificial intelligence technologies. This will enable us to offer more comprehensive and reliable services to our clients”* said **Fabio Stupazzini, Director of Accounting Partner** Cardo AI facilitates transparency and sharing of all information in securitized portfolios. It enables the management of granular portfolios of any underlying credit, generating analytical databases that provide useful information for managing operations and monitoring performance indicators. --- ### [AI turns sub performing loans into opportunities: doValue and Cardo AI together to innovate Stage 2 credit management](https://cardoai.com/announcements/ai-turns-sub-performing-loans-into-opportunities-dovalue-and-cardo-ai-together-to-innovate-stage-2-credit-management/) **Published:** May 7, 2024 **Author:** RobertaCardo26 **Content:** ROME – MILAN MAY 7TH, 2024: According to a recent report by PwC1 on Non Performing Loans market in Italy, over the past five years the volume of Stage 2 loans2 , those still categorized as performing but with increasing riskiness, has grown by more than 25 percent, exceeding 200 billion euros in December 2023. While an evolved industry for the management of bank non-performing loans (Non-Performing Loans) has developed over the years, a leader of which is doValue, targeted strategies to reduce the risk arising from this segment in the credit merit for Stage 2 loans have not yet become widespread. Proactive management of Stage 2 loans, given their high volume, would bring several benefits to the banking system in terms of lower capital absorption and prudential provisions, with positive impact on the income statement as well, limiting and preventing their further deterioration into Stage 3 (Non Performing), a category that can have a significant impact on the lending bank, which finds itself setting aside a greater share of capital to cope with the risk of the borrower’s default. The best way to manage Stage 2 credit requires careful monitoring, analysis and the creation of an information flow to quickly pick up any signs of deterioration. Technology and experience are therefore essential to put in place a process based on the enhancement of early warning signals and the quick definition of corrective mechanisms. In this context, **doValue**, a company listed on the STAR segment and active in credit management services, and **Cardo AI**, a fintech specializing in the development of technologies for structured finance, announce a strategic partnership for effective and proactive Stage 2 management. The exclusive partnership focuses on specialized monitoring of Stage 2 loans for the purpose of assisting banks through proprietary models for dynamic risk management and default prediction. Data are integrated with traditional and alternative external sources for a comprehensive and up-to-date view of the financial health of households and businesses. This innovative approach enables early detection of emerging trends and more informed decision-making based on a wide range of unconventional indicators. From this data, predictive models are then developed to estimate and recalculate portfolio risk and cash-flow metrics, leveraging on artificial intelligence algorithms. Such models make it possible to guide the most appropriate management strategies in relation to the individual client type. In line with doValue’s 2024-2026 business plan, which aims for a more widespread and pervasive use of technology to increase process efficiency as well as a diversification of its business with respect to Non Performing Loans, the partnership with Cardo AI is part of a broader collaboration between the two players that currently has new products and services related to credit management under study. **Manuela Franchi,** **CEO of doValue**, comments: “*Our partnership with Cardo AI represents a crucial step in doValue’s strategic growth plan, geared toward greater diversification into credit segments beyond NPLs and the adoption of advanced IT solutions to optimize our processes. We firmly believe in the added value of a proactive and technologically advanced approach to Stage 2 credit management. We believe that by combining doValue’s expertise in credit management with Cardo AI’s skills in developing financial artificial intelligence models, we will be able to significantly improve asset quality and profitability in the banking system*” **Altin Kadareja**, **Founder and CEO of Cardo AI**, comments: “*We are proud to partner with a top player like doValue, a leader in the credit management industry, to put our expertise in the use of alternative data and our Artificial Intelligence solutions at the service of the real economy. This collaboration is an important step toward creating a more efficient and transparent credit management system*”. 1 Source: PwC, “The Italian NPE market. Chasing Tomorrow,” December 2023 2 Stage 2 status is given to exposures that have shown a significant increase in credit risk since initial recognition (unless they have low credit risk at the reporting date), but have no objective evidence of impairment **About doValue** The doValue Group is the leading operator in Southern Europe in credit and real estate management services. With an experience of more than 20 years and approximately €116 billion in assets under management (Gross Book Value) as at 31 December 2023 across Italy, Spain, Portugal, Greece and Cyprus, the doValue Group’s business contributes to the economic growth by fostering the sustainable development of the financial system. The doValue Group offers an integrated range of credit management services: servicing of Non-Performing Loans (NPL), Unlikely To Pay (UTP), Early Arrears, performing loans, Master Legal, Due Diligence, data processing and Master Servicing. The shares of doValue are listed on the STAR segment of Euronext Milan (EXM) and, in 2023, the Group reported Gross Revenue of €486 million and EBITDA excluding non-recurring items of €179 million.The doValue Group is the leading operator in Southern Europe in credit and real estate management services. With an experience of more than 20 years and approximately €116 billion in assets under management (Gross Book Value) as at 31 December 2023 across Italy, Spain, Portugal, Greece and Cyprus, the doValue Group’s business contributes to the economic growth by fostering the sustainable development of the financial system. The doValue Group offers an integrated range of credit management services: servicing of Non-Performing Loans (NPL), Unlikely To Pay (UTP), Early Arrears, performing loans, Master Legal, Due Diligence, data processing and Master Servicing. The shares of doValue are listed on the STAR segment of Euronext Milan (EXM) and, in 2023, the Group reported Gross Revenue of €486 million and EBITDA excluding non-recurring items of €179 million. --- ### [Swishfund partners with Cardo AI to automate ESMA reporting for private securitization transaction](https://cardoai.com/announcements/swishfund-partners-with-cardo-ai-to-automate-esma-reporting-for-private-securitization-transaction/) **Published:** October 4, 2024 **Author:** RobertaCardo26 **Content:** MILAN, OCTOBER 4, 2024: Swishfund, a leading non-bank lending platform serving small and medium-sized enterprises (SMEs) in the Netherlands and the UK, has partnered with Cardo AI to streamline compliance with the European Securities and Markets Authority ([ESMA](https://www.esma.europa.eu/)) reporting requirements. This collaboration is part of Swishfund’s recent refinancing through establishing a private securitisation. Since its inception in 2016, Swishfund has helped small businesses manage cash flow disruptions by offering flexible financing options, both in the Netherlands as in the UK. The securitization aligns with the lending platform’s broader strategy to scale its lending operations. By securing access to institutional capital, Swishfund is well-positioned to support a wider range of SMEs, helping them secure the funding necessary to drive growth and contribute to the development of local economies. To support their asset-backed securitization (ABS) transactions, Swishfund needed to comply with the recently enforced ESMA reporting guidelines. This is a complex and time-consuming process that demands precise data submission in standardized formats and within strict timelines. As regulatory oversight has increased, particularly with the European authorities tightening requirements around ABS transactions, Swishfund faced the challenge of adapting to the ESMA guidelines. Consequently, the reporting obligations often involve high costs, manual labor, and a heightened risk of errors, which can slow down operations and divert resources away from core business activities. In response to these challenges, Swishfund has integrated Cardo AI’s advanced reporting system, allowing for the seamless generation of ESMA-compliant reports with minimal manual intervention. This not only ensures accuracy and regulatory compliance but also frees up valuable internal resources, reduces operational costs, and significantly lowers the risk of errors. Cardo AI’s technology offers automated data validation, transformation, and submission to ESMA, enabling Swishfund to meet all reporting requirements efficiently. The established private securitisation structure enables Swishfund to improve access to the institutional debt capital market, thereby offering the potential of more economic funding costs, increased diversification, and enhanced flexibility. Furthermore, Cardo AI was happy to have worked alongside all involved transaction parties, including co-lead arranger ADCM (debt advisory), achieving a successful and timely completion. With the first tranches successfully placed the transaction marks a major milestone for Swishfund and confirms investor confidence in the future of the company. **Jeroen Sonsma, CEO at Swishfund**, added: *“Regulatory reporting is typically an obligation we would like to have outsourced to professionals, preventing risks of being non-compliant. Cardo AI’s technology and service offers us the right value for money, making it easier for us to fully focus on our growth plans.”* **Altin Kadareja, Founder and CEO of Cardo AI**, added: *“We’re happy to work with Swishfund, marking our first step into the Dutch market. Our reporting tool can help simplify the regulatory burden for financial institutions, and we are proud to support Swishfund in their journey towards more efficient operations.”* --- ***About Swishfund**: Swishfund is an alternative lender providing term loans to small businesses in the UK and in the Netherlands. For more info: [http://www.swishfund.co.uk,](http://www.swishfund.co.uk,/) [https://www.swishfund.nl](https://www.swishfund.nl/)* --- ### [Cardo AI emerges as the defining ABF specialist in Chartis Research's 2026 global rankings](https://cardoai.com/announcements/cardo-ai-emerges-as-the-defining-abf-specialist-in-chartis-researchs-2026-global-rankings/) **Published:** July 1, 2026 **Author:** GiadaCardo26 **Content:** LONDON/NEW YORK – JULY 1, 2026: Cardo AI, the technology platform purpose-built for asset-backed finance and private credit, today announced it has been recognised in two Chartis Research STORM reports, ranking 19th in [BuySideRisk50](https://www.chartis-research.com/financial-risk/7947513/storm-buysiderisk50-2026-ranking-and-award-winners) and 25th in [QuantTech50](https://www.chartis-research.com/financial-risk/7947511/storm-quantitative-analytics50-2026-ranking-and-award-winners), among the most authoritative independent assessments of risk technology vendors globally. In the **BuySideRisk50**, Cardo AI climbed 13 places from 32nd to 19th[,](https://cardoai.com/article/cardo-ai-ranked-in-chartis-buyside-risk50-2025/) one of the strongest year-on-year improvements in the ranking ([see how Cardo AI ranked in 2025](https://cardoai.com/article/cardo-ai-ranked-in-chartis-buyside-risk50-2025/)[)](https://cardoai.com/article/cardo-ai-ranked-in-chartis-buyside-risk50-2025/). It also received two awards highlighting our ability to handle complex asset classes like asset-backed finance and our modular and flexible approach to risk management: - Industry Category Award: Asset-Backed Finance - Solution Category Award: Risk Management as a Service model In the **QuantTech50**, Cardo AI won an award recognizing our coverage across consumer loans, CLOs, trade finance, and specialty finance, alongside our expansion into niche markets that generic tools cannot support: - Pricing, Valuation, and Asset-Class Specific Analytics Award: Asset-Backed Finance ![Table of 2026 BuySideRisk50 rankings; Cardo AI is rank 19 with scores: Overall 65.60, Functionality 66.00, Impact 57.00, Infra 68.00, Strategy 68.00, Innovation 69.00.](https://cardoai.com/wp-content/uploads/2026/07/BuySideRisk50-2026-Cardo-AI-Rankings-1.png) ![QuantTech50 2026 rankings table showing companies and scores; Cardo AI at rank 25 highlighted.](https://cardoai.com/wp-content/uploads/2026/07/Quantitative-Analytics50-2026-Cardo-AI-Rankings.png) Together, the recognitions establish Cardo AI as the risk and analytics platform of choice for institutional participants in private credit and ABF, a market that continues to grow in complexity as it moves into the mainstream. Asset managers, banks, and insurers increasingly require a single provider that spans portfolio risk, cash flow analytics, and reporting across illiquid asset classes. *“Asset-backed finance is one of the most analytically demanding corners of private markets, and most platforms were simply not built for it,” said **Altin Kadareja, Co-Founder & CEO of Cardo AI**. “These results confirm that we are the specialist in ABF across both analytics and risk management, and that focus is precisely what our clients rely on us for.”* **FREQUENTLY ASKED QUESTIONS** 1\) **What is the Chartis STORM series?** It is a research series examining how quantitative modeling, machine learning, optimization, and high-performance computing converge across financial services 2\) **What is the Chartis Quantitative Analytics50 2026 report?** Part of the STORM series, it is a global study and vendor ranking of the top 50 technology providers deploying quantitative analytics in capital markets and wholesale banking. 3\) **Why is the Quantitative Analytics50 ranking relevant?** It evaluates how effectively vendors integrate advanced quantitative modeling with their underlying computational infrastructure and real-world business operations 4\) **What is the Chartis Buyside Risk50 2026 report?** Part of the STORM series, it ranks the top 50 technology providers serving asset managers, hedge funds, private credit platforms, and institutional buy-side firms. 5\) **Why is the Buyside Risk50 ranking relevant?** It validates top providers handling complex private asset classes like Asset-Backed Finance through end-to-end data integration, workflow automation, and Risk Management as a Service. --- ### [Cardo AI and Encina Lender Finance: Transforming Asset-Based Finance for Originators and Investors](https://cardoai.com/announcements/cardo-ai-encina-lender-finance-transforming-asset-based-finance-for-originators-and-investors/) **Published:** February 12, 2025 **Author:** GiadaCardo26 **Content:** NEW YORK – FEBRUARY 12 2025: Cardo AI and Encina Lender Finance, LLC (“ELF”) have created a partnership to revolutionize the asset-based finance (ABF) market, delivering faster, more efficient funding solutions for originators and greater capital deployment opportunities for institutional investors (LPs). As the ABF market continues to grow, the collaboration between Cardo AI and ELF will set new industry standards, making structured credit more accessible, data-driven, and efficient. ELF brings cutting-edge structuring and credit risk management expertise to asset-backed credit facility and forward flow purchase program investments, while Cardo AI delivers advanced technology to streamline deal execution, enhance risk monitoring, and improve transparency for all stakeholders. *“Our partnership with Cardo AI represents a major step forward in the evolution of asset-based finance,”* said **Geoff Beard, CEO & Chief Investment Officer at ELF**. *“By integrating technology-driven solutions into our investment and surveillance processes, we are accelerating transactions and enhancing overall market efficiency.”* *“As we strengthen our presence in the U.S. market, we are excited to collaborate with ELF to redefine the way structured credit operates”* said **Altin Kadareja, Co-Founder and CEO at Cardo AI**. *“With our intelligent technology and the ELF team’s expertise in structuring asset-backed credit facilities and forward flow purchase programs, we are creating a more transparent, scalable, and investor-friendly ABF ecosystem.“* This partnership is just the beginning of a transformative shift, one that will empower originators, institutional investors, and the broader structured finance ecosystem to capitalize on the full potential of asset-based lending in the non-bank specialty finance sector. --- ***About Encina Lender Finance, LLC****: Operating from both coasts, ELF offers delayed draw term loans and forward flow purchase programs ranging in size from $50 – $150 million to emerging and established specialty finance companies with strong junior capital sponsorship. ELF targets ABF investments in granular pools of short-to-intermediate duration asset classes in the consumer and commercial finance sectors. Targeted consumer asset classes include unsecured installment and revolving loans (direct-to-consumer and point-of-sale), secured vehicle finance, home improvement loans, rent-to-own/lease-to-own, debt consolidation loans, and student lending (in-school vocational, refi). Targeted commercial asset classes include small balance SMB lending (both direct-to-business and embedded point-of-sale), merchant cash advance, small-ticket equipment finance, corporate charge card, earned wage access, and supply chain / trade finance. In addition, ELF provides streamlined “first-out” financing to other like-minded private credit investors in the specialty finance sector. ELF utilizes a combination of cutting-edge technologies and rigorous collateral-level analysis to generate differentiated investment insights and creative solutions to its counterparties’ credit portfolio financing needs. [lenderfinance.encinacapital.com](https://lenderfinance.encinacapital.com/)* --- ### [Cardo AI announces $15 million Series A Funding Co-Led by Blackstone, FINTOP Capital and JAM FINTOP](https://cardoai.com/announcements/cardo-ai-announces-15-million-series-a-funding-co-led-by-blackstone-fintop-capital-and-jam-fintop/) **Published:** November 20, 2024 **Author:** sviluppatore **Content:** NEW YORK – NOVEMBER 20, 2024: Cardo AI, a next generation asset-based finance and private credit technology platform, has completed its $15mn Series A round. The investment was co-led by [Blackstone Innovations Investments](https://www.blackstone.com/blackstone-innovations-investments/), Blackstone’s early-stage strategic investment arm, [FINTOP Capital](https://www.fintopcapital.com/) and[ JAM FINTOP](https://www.jamfintop.com/). Andy Horwitz and Kevin MacDonald, co-founders of Black Mountain Systems, also participated in the round. The $40 trillion asset-based finance and private credit market continues to operate with outdated systems, manual processes and fragmented data. As the market expands with increasingly complex investment strategies, Cardo AI is modernizing the sector’s operations with advanced portfolio modeling and collateral data management technology. The platform facilitates investment decision-making and portfolio monitoring of multi-strategy credit portfolios for industry stakeholders, from investors to banks and servicers, leveraging advanced software workflows, a powerful data engine and predictive AI algorithms. The platform also supports servicers, trustees and fund administrators in streamlining operations and reducing costs. Founded in 2018, Cardo AI rapidly expanded its presence throughout Europe, with early support from Fasanara Capital, before entering the U.S. market earlier this year. Over the past few months, Cardo has onboarded several of the most sophisticated private credit and asset-based finance investors in the U.S. In addition to co-leading the Series A round, Blackstone is leveraging Cardo AI’s technology in its Credit & Insurance operations to support direct lending and asset-based finance transactions. Co-leads FINTOP Capital and JAM FINTOP bring deep operating expertise and a strategic network of nearly 100 regional and community banks. Rick Kushel leads the investment for FINTOP and brings over three decades of experience building companies such as iLevel Solutions and DealCloud. **Altin Kadareja**, **Founder and CEO of Cardo AI**, said*: “Our expansion into the US market, supported by such prestigious institutions, highlights Cardo AI’s ability to deliver powerful technology for the alternative credit industry. We are excited to collaborate with new investors and are confident that our platform will play a significant role in scaling the market and enhancing operational excellence for our clients.”* **John Stecher**, **Chief Technology Officer at Blackstone,** said: *“At Blackstone we have seen firsthand the rapid growth in alternative credit. To keep pace, the industry needs to move away from manual, spreadsheet-based processes to embrace scalable technology solutions. Our Credit & Insurance team has been impressed by Cardo AI’s robust portfolio management capabilities for complex instruments.”* **Rick Kushel,** **Managing Partner at FINTOP Capital** said: *“We see enormous potential for Cardo AI to transform an industry that has been slow in adopting technological innovation. With decades of experience, their team brings the expertise needed to tackle the challenges this market faces. We are excited to support them in setting the standard in asset-based finance and private credit technology.”* --- ### [Cardo AI launches cash flow modeling tool for asset-based finance, with live rate curves powered by Bloomberg Data](https://cardoai.com/announcements/cardo-ai-launches-cash-flow-modeling-tool-for-asset-based-finance-with-live-rate-curves-powered-by-bloomberg-data/) **Published:** June 8, 2026 **Author:** GiadaCardo26 **Content:** NEW YORK – JUNE 8, 2026: Cardo AI today announced a new cash flow modeling tool for asset-based finance and specialty finance, built as a modern alternative to incumbent structured-finance cash flow engines such as Intex. The tool addresses a problem that has constrained analysts for years: engines designed for public ABS deals that do not fit today’s esoteric asset classes or the needs of insurance investors, who have become major holders of private credit and structured assets. “For years, analysts modeling esoteric deals had to either force them into templates that don’t fit or rebuild everything from scratch in a spreadsheet,” said **Altin Kadareja, Co-Founder and CEO of Cardo AI**. “The tools simply weren’t built to model a deal across its full lifecycle, through ramp-up and reinvestment, against live market data. That’s the problem we solved.” As private credit has expanded into data center financings, cell tower deals, buy-now-pay-later receivables, music and other royalty rights, and other esoteric assets, the tools used to model these structures have not kept pace. Analysts have been left to adapt legacy engines built for public ABS deals. The new tool brings structured-credit modeling rigor, waterfalls, tranching, eligibility tests, covenants, and cash flow simulation, to the asset-based and specialty finance collateral that legacy engines were never built for. Purpose-built for esoteric and non-standard asset classes, it pulls live market data directly into cash flow simulation, accrual, covenant monitoring, and stress testing, and lets teams model deal structures across their entire lifecycle, not just the steady state. During the ramp-up phase, analysts can model the structure as collateral is acquired toward target par, testing how partial portfolios, warehouse financing, and timing affect the waterfall before a deal is fully invested. Through the reinvestment period, the tool models the redeployment of principal proceeds into new collateral under the deal’s eligibility criteria and reinvestment rules, so projected cash flows reflect how the structure is actually managed over time, rather than assuming a static, fully-ramped pool. Cardo AI’s Reference Rates module supports SOFR, SONIA, EURIBOR, and custom adjusted indices such as “EURIBOR 3M + 20bps.” Powered by [Bloomberg data](https://professional.bloomberg.com/products/data/?utm_source=google&utm_medium=paid_search&utm_campaign=emea_techdata_bsss_2025_ao&utm_content=text_all-data-financial&tactic=948160&utm_term=bloomberg%20data&matchtype=e&device=c&gclsrc=aw.ds&gad_source=1&gad_campaignid=22137903346&gbraid=0AAAAADdH1c-TGLVIOgDpsRZTy405dy9-W&gclid=CjwKCAjwmJjSBhB-EiwAkZgxixwecnQe9tLOOO1TzCmsKzPffN3d12z9jpVf-gcWlsybQdJX-JpuEhoCjVMQAvD_BwE), the module replaces batch uploads with live forward-curve data pulled directly into cash flow simulations and daily accrual calculations, so pricing reflects current market conditions rather than the last manual refresh. The engine accepts forward curves, interest-rate stresses, and custom yield-curve inputs per index and tenor combination, allowing analysts to run portfolio stress tests against live market curves instead of static estimates. “Insurance investors must run independent scenario analyses to meet regulatory requirements, but today they only have access to static cash flows, which limits their ability to meet those requirements,” said **Marco Masotto, Head of Product of Cardo AI**. “Bringing live curves and full-lifecycle modeling into a single engine gives them the dynamic cash flows those analyses depend on.” That capability matters most for Private Credit managers and Insurers, where those cash flows feed a chain of regulatory and accounting processes that legacy engines leave analysts to bridge by hand. The tool is built to produce the quarterly cash flows insurers rely on for income recognition, to support allowance-for-credit-loss (ACL) provisioning under CECL and other-than-temporary impairment (OTTI) assessment, to underpin statutory reserving under NAIC frameworks such as VM-21 and VM-22. --- ### [Cardo AI powers Multitude Bank’s asset-based lending](https://cardoai.com/announcements/cardo-ai-powers-multitude-banks-asset-based-lending/) **Published:** September 15, 2025 **Author:** sviluppatore **Content:** NEW YORK – SEPTEMBER 15, 2025: Cardo AI, a leading technology platform for asset-based finance and private credit, today announced a strategic partnership with Multitude Bank p.l.c., a subsidiary of the listed Multitude AG. The collaboration will use Cardo AI’s platform to automate and streamline Multitude Bank’s asset-based lending (ABL) operations, enhancing efficiency and transparency in funding for small and medium-sized enterprises (SMEs). “*Technology is the key to accelerating growth in asset-based credit*,” said **Altin Kadareja**, **Co-Founder & CEO of Cardo AI**. “*By automating and streamlining critical operations, our platform helps institutions like Multitude enhance the efficiency of their ABL businesses and provide faster, more flexible funding solutions to SMEs*.” “*Our clients expect both speed and reliability”*, said **Alain Nydegger, CEO of Wholesale Banking at Multitude Group.** “*By working with innovative technology partners, we can scale our asset-based lending operations more efficiently. Consolidating our ABL activities onto a unified platform allows us to deliver faster responses with more flexible structures, while maintaining the transparency and audit-ready oversight that regulators and investors require.*” The Cardo AI platform will consolidate Multitude Bank’s asset-based lending operations by bringing loan, collateral, and counterparty data into one system. This provides real-time visibility into portfolio performance and covenant compliance, while also streamlining regulatory, accounting, and business reporting to improve efficiency and ensure accurate, audit-ready records. --- ***About Multitude**: Multitude Bank p.l.c. is a public limited company registered under the laws of Malta with registration number C56251. Its registered address is ST Business Centre, 120 The Strand, Gzira, GZR 1027, Malta. Multitude Bank p.l.c. is licensed as a credit institution by the Malta Financial Services Authority. For more info: * --- ## What's new in product ### [Select, constrain and optimize SRT portfolios on your terms](https://cardoai.com/whats_new_in_product/select-constrain-and-optimize-srt-portfolios-on-your-terms/) **Published:** October 9, 2026 **Author:** GiadaCardo26 **Excerpt:** Select the optimal SRT reference pool from thousands of loans in seconds. Set eligibility rules and limits, then optimize for capital relief. **Content:** Contents [Three steps to the optimal pool](#first-par) [Why switch from Excel](#second-par) [Built for every side of the trade](#third-par) Structuring a significant risk transfer starts with a simple challenge: selecting the right reference portfolio out of thousands of loans. A valid pool has to meet strict eligibility rules and complex concentration limits, while still hitting your financial targets. In Excel, that means testing combinations by hand, one after the other. It is slow, manual and repetitive. Worse, you rarely know if you found the best pool or just the first one that worked. And when a single limit changes, you start over. **Cardo AI Optimizer** replaces that guesswork with an instant, precise calculation. You set the rules and the objective, and it returns the pool that best satisfies them. From the full loan book to an optimized pool in 3 steps *1. Set your rules and objectives* - **Upload securely**: The Optimizer runs fully on anonymized data with zero persistent storage. Your data stays yours. - **Define your limits**: Layer on eligibility criteria, exposure caps, sector limits or rating thresholds. - **Set your target**: Choose your core objective, for example Maximise Capital Relief. *2. Run Cardo AI Optimizer* - **Scan every option**: The Optimizer evaluates millions of valid pool combinations in seconds. - **Find the best**: No more trial and error: it isolates the highest performing pool. - **Maximise your target**: You get the single pool that best satisfies your financial objective. *3. Export and evidence* - **Export the list** : Download your exact loan-level selection in Excel or PDF. - **Access analytics**: See charts on pool composition, limits and performance instantly. - **Provide proof**: Share executive-ready reports with credit committees, investors and regulators. ![](https://cardoai.com/wp-content/uploads/2026/10/cardo_abf_pool_optimiser_tablet_mockup-scaled.png) Why switch from Excel to Cardo AI Optimizer The optimal pool in your book is rarely the obvious one. A hand-built model can only test the few combinations someone had time to try. - **From manual to instant**: Manual Excel trial and error becomes an instant calculation. - **Uncover maximum value**: Evaluate millions of combinations, not just a few hand-built models, to squeeze more capital relief out of the exact same book. - **Adapt live in meetings**: Adjust constraints during deal discussions and see the new optimal pool in real time. - **Backed by proof**: Present clear, reproducible evidence to credit committees, investors and regulators. Tailored for every side of the trade Reference pool selection touches everyone in an SRT, and each side needs something different from it. - **For bank issuers**: Maximise capital relief on your own balance sheet without trial and error. - **For arrangers**: Speed up deal execution and adapt pools live during investor negotiations. - **For investors**: Re-optimize incoming portfolios against your internal risk rules before pricing. - **For insurers**: Stress-test coverage requests and understand concentration risks in seconds. The result: **no more guessing whether your pool is the best one**, and a selection you can defend in front of anyone who asks. The optimal pool in your book is rarely the obvious one. Cardo AI Optimizer finds it. [Book a demo →](https://cardoai.com/book-a-demo/) --- ### [Onboarding loan tape collateral with AI agents](https://cardoai.com/whats_new_in_product/collateral-integration-agents/) **Published:** July 14, 2026 **Author:** GiadaCardo26 **Excerpt:** Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. **Content:** Contents [How does it work?](#first-par) [What’s in it for you](#second-par) [Why does it matter?](#third-par) [Use case 1](#fourth-par) [Use case 2](#fifth-par) Onboarding a new deal has always been the most time-consuming, expert-dependent step in asset-based finance and collateral asset management. A loan tape lands, and someone who knows the asset class, the data model, and the quirks of that originator’s format sits down to map it, field by field, decision by decision. Every closing, default, prepayment, repurchase and write-off has to be reasoned through. It’s careful, valuable work but it doesn’t scale: one analyst, one integration, one tape at a time. A bottleneck that any collateral management software worth the name has to solve. Cardo AI changes the shape of that work. Instead of driving the integration by hand, you supervise an engine that already understands the terrain, proposing the mappings, declaring its assumptions and asking you to confirm before anything is applied. You stop doing the integration and start reviewing it. The core idea behind ai-powered collateral management done right. How does it work? The engine is usable via your AI interface with built-in expertise on three things that used to live only in analysts’ heads: 1. The asset classes-specific mechanisms; 2. The loan-tape logic; 3. The Cardo AI data model that turns raw collateral into analytics-ready data. A session moves relentlessly through the same stages a senior analyst would, forming a collateral management workflow that runs largely on its own: - **It establishes context.** The engine spins up a set of specialized agents, each taking care of a distinct piece of the groundwork. A Data Profiling Agent reads the incoming tape and characterizes its structure and contents; A Data Quality Agent surfaces gaps, anomalies, and inconsistencies across multiple reporting dates; A Scope Agent reads the output the deal has to produce and works out what’s in play (the asset classes, the identifiers, the input structure and the tables the deal has to build). Working in concert, they don’t just cover the standard case: they identify every use case and behaviour present in the data, across every reporting date. - **It proposes, you decide.** For each mapping and each lifecycle event (closing, default, prepayment, repurchase, write-off,…), the engine proposes an approach and states the assumption behind it. Nothing is written until you sign off – the human-in-the-loop checkpoint that separates a genuine collateral management platform from a black box. - **It reasons about consequences.** It thinks in edge cases: testing whether a join actually works, checking that a generated identifier is unique, and flagging when a change would break something. - **It builds and checks.** Once decisions are locked, it generates the data quality checks and transformation logics in the right dependency order, saves and runs each and helps surface data-quality issues in the output as it goes. And it remembers. All the reasoning is persisted, so you can start new sessions leveraging the business context behind earlier decisions, so you’re not re-explaining the deal every time you come back to it – the kind of institutional memory that automated collateral management is supposed to provide but rarely does. What’s in it for you **Expertise, built in.** The knowledge of data models, asset-class conventions and loan-tape structures that used to depend on a handful of specialists is now part of the engine. It has walked analysts through asset classes they’d never touched before, challenging assumptions and reasoning through the mapping, so unfamiliar collateral stops being a blocker. Key-person risk is solved because the history of decisions and assumptions is stored and shared among different users on the same deal – collateral intelligence that persists past any one analyst. **From driving to reviewing.** You no longer hand-build mappings. You confirm proposals, declare assumptions, and review decisions. Since it asks before it acts, you keep control of what actually gets written, the human-in-the-loop checkpoint analysts have specifically valued. **From one at a time to many in parallel.** Since your role is to review rather than to drive, you’re no longer the bottleneck. The engine works from a couple of lines of instruction and pings you only when it needs an input or an approval, which lets analysts run multiple integrations at once and keep other work moving in between. The team stops being capped by how fast any person can map a tape. **Complexity it can actually hold.** Eight input tables feeding a single output, joins that have to be tested, identifiers that have to be proven unique, this is where manual work gets fragile. The engine keeps track of it, tests as it goes and thinks through the edge cases with you, the way a properly built collateral management system should. **Problems found on the way in, not at the next review.** Analysts have used the engine to surface raw-data issues as they build, identifying the problem and working out the fix in the same moment. When a data anomaly turns into a finding you can hand to the client, that’s onboarding and diligence happening at once. ![Marketing hero: CARD AI logo on the left and bold text on white space; a dark laptop dashboard with a grid of tokens on the right.](https://cardoai.com/wp-content/uploads/2026/07/oxygen-harness-cover.jpg) Why does it matter? Integration is one of the most persistent collateral management challenges; it’s where deals get delayed and where key-person risk concentrates. When onboarding depends on a few experts working sequentially, every new mandate competes for the same scarce attention, and the pipeline moves at the speed of the analyst. Turning that work into something you supervise rather than perform breaks the constraint. The expertise no longer has to be re-summoned for every tape; it’s encoded in the engine and applied consistently, with the analyst as the reviewer who catches what matters and signs off on the rest. Teams take on more deals without adding headcount and the biggest reported gain isn’t any single automation, it’s having not to re-explain the deal or the asset class for new deals at every stage. In a market where speed to onboard is speed to invest, that creates the difference between a data pipeline that keeps pace with the business and one that holds it back. Use case 1: onboarding an asset class you’ve never seen An analyst was handed an integration in an asset class they had never worked with (GPU Financing). Normally, that means days of ramp-up: reading around the asset class, second-guessing which fields mean what, and building the mapping tentatively. With the engine, the analyst worked through it as a conversation. The engine explained how the asset class behaved, challenged assumptions that didn’t fit the data and reasoned through each mapping decision before generating the query, logging open questions and data anomalies as it went. What came out the other side wasn’t just a finished mapping but a consolidated list of questions ready to send back to the counterparty. An unfamiliar asset class stopped being a reason to slow down. Use case 2: same rigor, a fraction of the time Onboarding used to move at the speed of one analyst working step by step (profile, map, review,…), each stage waiting on the previous to complete. Now the mechanical work happens underneath, and the analyst supervises instead of executing. A session takes a short natural-language brief, profiles the data, proposes the mappings and gets to work, pausing only when it needs an input or an approval. The analyst confirms assumptions and reviews results rather than actively working in each step; so nothing is skipped and the output holds to the same standard as before, but a single integration finishes in a fraction of the time a manual one would have taken, from 3x to 7x the manual speed. And because the analyst is supervising rather than doing, their attention is freed for other work like reviewing a tricky covenant, prepping the next deal, or even onboarding a second deal in parallel. > *It let me run two complex integrations in parallel. The biggest gain is how it maintains context across project phases. handling mapping, testing joins, and catching edge cases so I don’t have to constantly re-explain things. > Senior Integration Analyst* If your onboarding pipeline still depends on one or two people who know the asset class, discover how Cardo AI puts that expertise into a system your whole team can run. [Book a demo →](https://cardoai.com/book-a-demo/) --- ### [Why Excel is the wrong home for a waterfall](https://cardoai.com/whats_new_in_product/why-excel-is-the-wrong-home-for-a-waterfall/) **Published:** October 8, 2026 **Author:** GiadaCardo26 **Excerpt:** Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. **Content:** Contents [A waterfall is priority logic](#first-par) [Where spreadsheets fall down](#second-par) [From documents to executable logic](#third-par) [What happens when a test breaks](#fourth-par) [The operational point](#fifth-par) Every structured finance deal has a waterfall. It decides who gets paid, in what order, and what happens when something goes wrong. It sits at the centre of the transaction, and it is usually the least robust part of the operating stack. Most teams still model waterfalls in Excel. There is a better way to do this. ![Left: CARD AI Waterfall Studio logo; right: laptop screen displaying code editor and a chart graphic.](https://cardoai.com/wp-content/uploads/2026/07/CARDO-AI-waterfall-studio.jpg) A waterfall is priority logic Strip away the presentation layer and a waterfall is a set of rules. Which tranche gets paid first. What happens when a coverage test breaks. How shortfalls carry forward from one period to the next. How reserve accounts are drawn and replenished, and how excess spread is applied once senior obligations have been met. That is logic, and logic behaves well when it is written as logic. It behaves badly when it is spread across three thousand linked cells, a set of named ranges, and a tab that only one analyst fully understands. Where spreadsheets fall down The issue is not that Excel is a poor tool. It is that a waterfall is not really a calculation. It is a rule set that has to be executed consistently, tested under stress, and defended to investors, auditors and rating agencies. Spreadsheets break quietly. A dragged formula, a hardcoded override left in from a previous scenario, a broken reference after a restructure. The model still produces a number, which is precisely the problem. They are also difficult to audit, since reconstructing why a payment landed where it did means tracing precedents across tabs, and version control tends to collapse into a folder naming convention. And nothing carries forward. Every new deal starts again from the closest previous transaction, edited until it looks right, so structural knowledge stays in the file rather than in the platform. From documents to executable logic Waterfall Studio starts somewhere else. You describe the deal in plain English, or upload the offering documents directly, and the system writes the waterfall for you. The output is a structured, readable rule set that mirrors the priority of payments as drafted, so the people who negotiated the structure can review it against the documentation rather than against a formula bar. Before anything runs, the engine validates the logic. Type mismatches, currency mismatches and broken references surface at build time rather than three scenarios into a conversation with an investor. In a spreadsheet a structural error usually presents itself as a plausible number. Here it presents itself as an error, which is the cheaper outcome by some distance. Once the structure is validated, the operational step is small. Drop in the periods, drop in the collateral numbers, run. What happens when a test breaks The clearest test of any waterfall model is a breach. When an overcollateralisation test fails, senior principal moves ahead in the queue and junior interest defers. In a spreadsheet that behaviour sits inside conditional formulas written under time pressure, and it is rarely obvious from the file whether they are correct. In Waterfall Studio the diversion happens because the rule says so. No formula edits, no hidden logic, no override tab. Every payment, shortfall, breach and scenario is visible in sequence, and the same inputs produce the same outputs for anyone who runs them. That reproducibility is what turns the model from a personal asset into shared infrastructure, and it is what makes a single source of truth defensible when investors and auditors ask not just for outputs but for the mechanism behind them. The operational point None of this removes judgement from the process. Structuring decisions, the credit view and the negotiation of the priority of payments all stay with the team. What changes is where the time goes. New deals get modelled in hours rather than weeks, stress testing becomes routine rather than a project, and less of the week is spent rebuilding and defending a spreadsheet. Waterfall Studio is built for structured finance and powered by AI. If you would like to see the engine run a live transaction, get in touch and we will walk you through it. If your onboarding pipeline still depends on one or two people who know the asset class, discover how Cardo AI puts that expertise into a system your whole team can run. [Book a demo →](https://cardoai.com/book-a-demo/) --- ### [From deal dashboard to board-ready report in one click](https://cardoai.com/whats_new_in_product/from-deal-dashboard-to-board-ready-report-in-one-click/) **Published:** October 7, 2026 **Author:** GiadaCardo26 **Excerpt:** Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. **Content:** Contents [One tool connected to your live deal data](#introduction) [Less time assembling, more time analysing](#first-par) [Build it once, reuse it every period](#second-par) [Reporting that scales with your team](#third-par) **One tool instead of PowerPoint, Excel, Acrobat and Skedler, connected to your live deal data** Every reporting period, senior stakeholders and investors expect the same report, refreshed. For most teams, that has meant screenshotting the dashboard, pasting it into Excel, fixing the layout in PowerPoint, and converting it to PDF in Acrobat, then doing it all again next period. A chart pasted into PowerPoint is just a picture the moment it’s copied, disconnected from the dashboard behind it. A PDF opened in Acrobat can only be edited as a file, so when the data changes, the whole thing gets rebuilt from scratch. And tools like Skedler can schedule and automate a report once it’s built, but the building still happens somewhere else first. Our **Dashboard PDF Report Generation** tool replaces that chain of tools with one. Any deal dashboard becomes a presentation-ready PDF, generated directly from the platform and connected to the live data behind it. Your team spends less time moving things between tools and more on the analysis. Less time assembling, more time analysing - **A full report in one click.** A single “Download PDF” action exports the whole dashboard, with tables, charts, KPIs and commentary included. Nothing has to be put together by hand. - **A document that reads like a report.** Smart layout turns the dashboard into a properly paginated document. Tables never split across pages, wide tables scale to fit and charts can sit side by side. What you send looks finished, not stitched together. - **Add your own notes to the numbers.** Add commentary next to a chart, insert text boxes, bring in external images and edit the cover page. Stakeholders receive your reading of the data, not just the data on its own. - **Figures you can stand behind.** Numbers, currencies and dates match the dashboard exactly. Exports respect existing user permissions, and every report is logged with who produced it and which template version was used, so each one can be traced back. - **Layout comes before generation, not after.** Set the structure, commentary and branding first, then generate the report. When the data updates, you run it again instead of reopening and re-editing a finished file ![](https://cardoai.com/wp-content/uploads/2026/10/CARDO-AI-dashboard-pdf.jpg) Build it once, reuse it every period Templates are saved on each deal, so the effort goes into the first report only. Every following cycle starts from a **ready-made layout** instead of a blank page. - **Set up once, refresh every period**: Each new run picks up the same structure, cover page, commentary blocks and branding, updated with the latest data. - **One deal, several audiences**: Keep multiple templates for the same deal, such as a “Monthly Investor Report” and a “Covenant Pack”, each tailored to the people who will read it. - **Control down to the detail**: Choose exactly which tables, KPIs and charts to include, and set each element individually, from its width and height to the comments anchored to it. - **A layout that fits the story**: Arrange charts side by side or one per row, choose portrait or landscape, set margins and resize tables and charts. - **Never locked in**: Add, remove or reorder elements, update commentary and adjust the cover page whenever the report needs to change. A screenshot shows the dashboard. A PDF report presents it. The output is a structured, paginated document with print-quality charts, tables that never break across pages and your commentary built in. When you only need a quick share, one-click Snapshot mode is still available. Reporting that scales with your team The quality of a report should not depend on who builds it or how many deals are in the portfolio. Scheduling tools like Skedler automate sending a report once it exists; here, the report, the design, and the automation all sit in the same place as the deal data itself. - **The same output, whoever prepares it**: Deal-level templates keep formatting identical across analysts and reporting cycles. - **No technical skills needed**: A drag-and-drop designer lets anyone build or adjust a report visually, with a live preview of the actual pages. - **Every deal in a single run**: Batch generation produces reports for all deals at once. - **Reports out on time, automatically**: Scheduling and automated distribution generate reports on your reporting date and email them to the right recipients. The result: **less time formatting, fewer errors** and more time for the analysis your stakeholders care about. See how Cardo AI turns your deal dashboards into board-ready reports. [Book a demo →](https://cardoai.com/book-a-demo/) --- ### [Why Rated Direct Lending CLOs break Ops teams](https://cardoai.com/whats_new_in_product/why-rated-direct-lending-clos-break-ops-teams/) **Published:** June 3, 2026 **Author:** RobertaCardo26 **Excerpt:** Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. **Content:** Contents [Introduction](#first-par) [Why Rated DL CLOs break Ops team](#second-par) [What real-time visibility actually looks like](#third-par) [The outcome](#fourth-par) Introduction Every drawdown notice. Every borrowing notice. Every waterfall payment. In a rated direct lending CLO structure with multiple insurance investors, vehicles, and capital tranches, each of these administrative events doesn’t execute once – it executes for every combination in the structure. Run the math on a platform with six insurance investors, fifteen CLO vehicles, and seven capital tranches and you arrive at the same number that stops most ops teams cold. Six hundred and thirty executions. Per event. For a structure that was designed to attract institutional insurance capital, deliver risk-adjusted returns, and scale efficiently, the operational reality behind it is anything but efficient. ![Dashboard metrics: 6 insurance investors, 15 CLO vehicles, 7 capital tranches, and 630x execution burden (orange).](https://cardoai.com/wp-content/uploads/2026/06/CLO-1-1.png "CLO-1 (1)") Why Rated DL CLOs break Ops team The complexity isn’t accidental. Rated direct lending CLOs combine bespoke legal structures, multi-party data flows, and real-time testing requirements in a way that was never designed to be managed in a spreadsheet. Yet that is, for most platforms, exactly what happens. **Seven failure modes** 1. **Structural complexity across vehicles:** Bespoke indentures, multiple SPVs, and co-investment structures leave no single version of truth across vehicles. 2. **Drawdown and payment date burden**: Manual borrowing-base reconciliation and payment date waterfall calculations against third-party data are error-prone and time-consuming at scale. 3. **Unstructured data and rating compliance**: PDF trustee reports and email loan tapes force manual extraction. Rating tests cannot be automated without a clean, validated data layer. 4. **Reinvestment period risk**: Real-time eligibility screening and OC/IC impact analysis done manually creates delays between opportunity and execution. 5. **Rate volatility and IC mistrack:** SOFR resets reprice every floating-rate loan simultaneously. Manual IC models go stale between resets, leaving ops teams blind until the next trustee report. 6. **PIK accumulation & OC/IC erosion:** [PIK toggle](https://cardoai.com/whats_new_in_product/pik-loans-automating-interest-accruals-and-risk-monitoring/) accruals deplete OC test headroom silently across the platform. Older vintage vehicles hit concentration caps before managers know, restricting the whole fleet. 7. **Cross-vehicle cash drag and reallocation:** Prepayment surges and rate-driven paydowns arrive simultaneously across 5–15 active vehicles. No single-vehicle tool can coordinate platform-wide reallocation to prevent cash drag. Each of these is painful on its own. Together, they compound and the result is an ops team spending more time reconciling the past than managing the present. What real-time visibility actually looks like The answer isn’t more headcount. A platform that scaled from two to twelve CLO vehicles without adding a single ops hire didn’t do it by hiring faster, it did it by changing the underlying infrastructure. Cardo AI replaces the disconnected tools and manual workflows with a unified operating layer purpose-built for the structural complexity of rated DL CLOs. Here’s what that means in practice. 1. **Single source of truth**: Unified data layer across all vehicles, tranches, and counterparties. One reconciled version, always live. 2. **Automated borrowing base**: Transaction Management ingests trustee data and loan tapes automatically, producing live, audit-ready borrowing bases, no spreadsheet models required. 3. **Live OC/IC surveillance**: PDF reports and loan tapes structured on ingest. Configurable OC, IC, and concentration tests run automatically against a clean, validated data layer. 4. **Portfolio construction manager**: Screen eligibility, model OC/IC impact, and stress-test concentrations across every vehicle before committing capital. 5. **Real-time rate and covenant monitoring**: SOFR resets trigger instant IC recalculation across the full portfolio. Covenant headroom is live, not lagged by a trustee cycle. 6. **Live PIK and coverage monitoring**: Real-time OC/IC views incorporating current PIK balances and haircuts. Every new PIK accrual reflected in test headroom before the trustee reports it. 7. **Cross-vehicle allocation and cash drag forecasting**: Route incoming paydowns to the vehicles that need reinvestment most. Daily cash drag forecasting across the full fleet prevents concentration in any single vehicle. The outcome Scaling a rated DL CLO platform shouldn’t mean scaling the ops burden alongside it. The managers who will define the next decade of this market are the ones building infrastructure that grows without breaking. ![Tier 1 Asset Manager Measured Outcomes](https://cardoai.com/wp-content/uploads/2026/06/CLO-2.png "CLO-2") See how Cardo AI handles your rated DL CLO operations. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Direct lending solution: automate, track, and mitigate risks](https://cardoai.com/whats_new_in_product/direct-lending-solution-automate-track-and-mitigate-risks/) **Published:** June 25, 2024 **Author:** RobertaCardo26 **Excerpt:** Direct Lending presents unique challenges in loan origination, borrower management, and collateral evaluation. Cardo AI’s software tackles these challenges by providing specialized solutions that boost operational efficiency and empower strategic decision-making. **Content:** Contents [Introduction](#introduction) [What’s in it for you?](#first-par) [Why does it matter?](#second-par) [How does it work?](#third-par) [Use case](#fourth-par) Introduction Direct lending presents unique challenges in loan origination, borrower management, and collateral evaluation. Cardo AI’s [software](https://cardoai.com/intelligent-solutions/platform/) tackles these challenges by providing specialized solutions that boost operational efficiency and empower strategic decision-making. What’s in it for you? - **Streamlined operations**: Simplify and accelerate loan processing from origination to servicing with automated workflows and real-time data integration. - **Customized risk management**: Tailor risk assessment models to fit diverse borrower profiles and lending scenarios, ensuring robust risk mitigation strategies. - **Enhanced portfolio insights**: Gain deep insights into portfolio performance and asset quality through advanced analytics, empowering proactive portfolio management. - **Scalable solutions**: Scale operations seamlessly with flexible, scalable technology that adapts to evolving regulatory requirements and market conditions. - **Investor confidence**: Boost investor confidence with transparent reporting and comprehensive performance metrics, showcasing adherence to covenants and strategic alignment. Why does it matter? There are several issues linked to Direct Lending activities: - **Customized loan structures**: Direct Lending involves creating highly customized loan agreements tailored to the specific needs of each borrower. This means having, among others, flexible repayment schedules, varied interest rates, and specific covenants to protect the investment. - **Credit risk assessment**: Direct Lending requires solid credit risk assessment as loans are often unsecured or secured by non-traditional collateral. Therefore it is necessary to continuously monitor loan performance to detect early signs of distress and ensure timely repayments. - **Strict covenants** to safeguard lenders’ interests, often encompassing financial ratios, performance metrics, and activity restrictions. - **Borrowing base** in Direct Lending involves determining eligible collateral, accurately valuing it, setting advance rates, and continuously monitoring and adjusting the collateral to ensure compliance with loan agreements, thereby mitigating risk and maintaining lending capacity. - **Legal and compliance issues**: Direct Lending must also adhere to regulatory requirements, which can vary significantly depending on the jurisdiction and type of borrower. How does it work? Our Direct Lending solution offers the following features: - **Customize your loans with our facility module**: Adapt loan settings to your specific requirements and configure different loan types effortlessly in a flexible environment; automatically calculate expected payments, interest accruals, and set detailed loan terms and conditions. - **Ensure accurate loan origination with our borrowing base**: Easily verify eligibility and concentration limits, ensuring the correct application of advance rates for each loan. This maintains accurate and compliant lending practices. - **Track borrower performance easily with borrower monitoring**: Standardize financial data from borrowers and gain valuable insights into their behavior and financial health. This facilitates informed lending decisions and enhances risk management. - **Manage loan covenants with covenants tracking**: Conduct automated compliance checks for precise monitoring of financial and operational covenants. Receive early warnings for potential breaches, allowing borrowers to address issues promptly. - **Efficiently manage collateral with collateral management**: Monitor all collateral assets, perform regular valuations, and ensure compliance with collateral eligibility criteria. This helps maintain accurate collateral values and mitigate risks from fluctuating asset values. - **Stay organized with transaction events**: Keep track of all loan-related events in one centralized location. Stay informed about upcoming borrower obligations and easily sync transaction events with your calendar for better organization and efficiency. Use case Our client, a leading European asset manager, launched a diversified corporate credit fund focused on development of Italian SME and Mid-Cap with ESG targets. The Asset Manager, who raised money from high-standing European institutional investors, recognized that managing the financial and operational aspects of loans using Excel would have posed significant challenges, such as: - **Complexity in data management**: Managing a diverse portfolio of loans in Excel is difficult, especially as data volume increases. - **Lack of automation**: Manual calculations for accruals is time-consuming and open to operational risks. - **Poor scalability**: As the fund grows, traditional systems struggle to handle the increased data load and complexity, affecting speed and accuracy. - **Limited collaboration**: Excel’s limitations in real-time data sharing and collaboration can hinder effective communication and coordination among team members. - **Risk management**: operational risk and compliance with investor mandate imposing strict controls on borrowers’ financial and ESG performance require solid processes and tools. Recognizing these limitations, the Asset Manager chose Cardo AI Direct Lending advanced software to enhance its operations. Our solution enables efficient management and monitoring of loan details and collateral data. With our software, the client can now digitize the entire loan management process. This transition from Excel to a centralized, automated system has greatly improved operational efficiency and strategic decision-making. See how Cardo AI gives you real-time visibility into borrower performance and collateral health across your entire portfolio. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Investing in data centers: capitalizing on the data boom](https://cardoai.com/whats_new_in_product/investing-in-data-centers-capitalizing-on-the-data-boom/) **Published:** January 16, 2025 **Author:** RobertaCardo26 **Excerpt:** The rapid expansion of the data center industry is driven by the growing need for data storage and processing power, presenting significant opportunities for asset managers and investors. Data centers, which house the IT infrastructure essential for data processing and storage, are critical to the digital economy. **Content:** Contents [Introduction](#introduction) [What’s in it for you?](#second-par) [Why does it matter?](#third-par) [How does it work?](#fourth-par) [Use Case](#eight-par) Introduction The rapid expansion of the data center industry is driven by the growing need for data storage and processing power, presenting significant opportunities for asset managers and investors. Data centers, which house the IT infrastructure essential for data processing and storage, are critical to the digital economy. Two key trends fuel this growth: - **The move to cloud services:** Organizations are migrating their data storage and operations to the cloud to improve scalability, reduce costs, and enhance accessibility. - **AI’s growing adoption**: AI technologies require massive computational power to process large datasets, train machine learning models, and enable real-time decision-making. Data centers provide the critical infrastructure to support these workloads. The explosion of data generation Data generation has experienced exponential growth. Annual global data creation grew from just 2 zettabytes in 2010 to 120 zettabytes by 2023, with 90% of the world’s data produced in the last two years alone. The timeline in Exhibit 1 showcases a progression from the modest growth driven by social media and cloud adoption (2010–2015) to accelerated expansion fueled by streaming and content creation (2015–2021), culminating in an era of explosive data generation powered by transformative AI technologies (2021–2025). ![Blackstone from IDC Data](https://cardoai.com/wp-content/uploads/2025/01/Data-center_1-1024x461.png) *Exhibit 1 – Source: Blackstone from IDC Data* Why data centers matter for Asset-Based Finance As tech giants expand their infrastructure, developers will need to secure substantial capital to meet the rising demand for data centers. Building and maintaining data centers involves substantial capital for acquiring land, constructing state-of-the-art facilities, and ensuring reliable power and cooling systems. Additionally, data centers must incorporate advanced technology to meet the increasing computational and storage needs of modern applications. Private equity firms and institutional investors are stepping in to fund these developments, attracted by the stable revenue streams and long-term growth potential of the data center industry. A recent [PitchBook report](https://pitchbook.com/news/articles/pe-takes-pick-and-shovel-approach-in-ai-gold-rush) highlights the growing interest in data centers as strategic infrastructure supporting AI-driven operations. ![PitchBook data](https://cardoai.com/wp-content/uploads/2025/01/private-investment-in-data-center--1024x565.png) *Exhibit 2 – Source: PitchBook data* Additionally, Moody’s reports that completed data centers are frequently refinanced through commercial mortgage-backed securities (CMBS) or asset-backed securities (ABS) markets. Both markets reached record activity in 2024, a trend expected to persist into 2025, with an increasing number of transactions extending to data centers outside the US (see Exhibits 3 and 4). *![Finsight and Moody’s Ratings](https://cardoai.com/wp-content/uploads/2025/01/exhibit-3-1024x461.png)* *Exhibit 3 – Source: Finsight and Moody’s Ratings* ![Trepp and Moody’s Ratings](https://cardoai.com/wp-content/uploads/2025/01/exhibit-4-1024x461.png) *Exhibit 4 – Source: Trepp and Moody’s Ratings* Evaluating data centers as a unique asset class Data centers represent a unique convergence of real estate and infrastructure investments. They are physical buildings with high barriers to entry due to the significant capital and energy requirements and generate relatively predictable revenues through medium- to long-term contracts. For some GPs and institutional investors, data centers are an attractive asset class with greater appeal than traditional real estate or infrastructure investments. However, despite the excitement surrounding this infrastructure, the market has experienced notable fluctuations in pricing. In the United States, prices steadily declined from 2014 to 2020 before rebounding, with an average increase of 35% between 2020 and 2023. Such volatility, particularly the sustained price drops in prior years, could significantly affect financing performance. These trends in storage, bandwidth, and computational costs introduce challenges for long-term profitability, revenue forecasts, and cost management, necessitating careful evaluation by investors. How Cardo AI supports data center investment strategies By leveraging Cardo AI’s advanced analytics and reporting tools, asset managers and investors can navigate this complex asset class with confidence. Understanding risks requires detailed data and advanced analytical capabilities. Despite strong fundamentals, this asset class comes with complexities: - **Technological risks**: Rapid innovation can render facilities obsolete. - **Energy concerns**: High energy consumption makes procurement critical, and sustainability is becoming a major focus, with operators adopting renewable energy and improving efficiency. Our platform provides: - **Data-driven insights**: Build custom benchmarks, incorporate geopolitical factors, and analyze key metrics across existing investments to balance risk and optimize returns. - **Custom metrics and reporting**: Tailor metrics to the unique characteristics of data centers, enabling granular analysis of divergence across investments. - **Predictive analytics**: Assess key trends such as: – **pricing declines** (historical data on falling costs for storage, bandwidth, and computing power) – **market saturation** (supply-demand dynamics and potential overcapacity in key markets) – **cost efficiency** (how advances in technology and energy efficiency impact pricing) – **competitive pressures** (pricing strategies of major players compared to smaller operators) – **customer sensitivity** (trends in customer migration and price sensitivity). - **LLM-powered discovery**: Utilize large language models (LLMs) to uncover unrelated but correlated investments, enabling more comprehensive diversification and innovative strategy development. *Source: “The 2025 data centers outlook”, Moody’s 14 Jan 2025* See how Cardo AI helps you size, monitor, and stress-test your data center exposure. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Dynamic portfolio views: see your investments, your way](https://cardoai.com/whats_new_in_product/dynamic-portfolio-views-see-your-investments-your-way/) **Published:** November 22, 2024 **Author:** RobertaCardo26 **Excerpt:** Traditional portfolio management tools often fall short, hindering users with rigid interfaces, time-consuming tasks, and limited analytical capabilities. This can compromise efficient decision-making and ultimately impact portfolio performance. Our platform‘s Dynamic Portfolio Views **Content:** Contents [Introduction](#introduction) [What’s in it for you?](#first-par) [Why does it matter?](#second-par) [How does it work](#third-par) Introduction Traditional portfolio management tools often fall short, hindering users with rigid interfaces, time-consuming tasks, and limited analytical capabilities. This can compromise efficient decision-making and ultimately impact portfolio performance. Our platform‘s **Dynamic Portfolio Views** feature addresses these challenges head-on, empowering asset managers and investors to gain deeper insights, streamline workflows, and improve data-driven decision-making. What’s in it for you? Dynamic Portfolio Views offers a range of powerful features to enhance your portfolio management experience: - **Customizable views:** Tailor the platform to specific asset classes, ensuring that relevant information is always at your fingertips. - **Advanced table management:** Easily manage and manipulate data with an Excel-like interface. - **Calculated fields:** Create custom calculations to derive specific insights and gain a deeper understanding of your portfolio. - **Automated exports:** Receive timely data updates without manual intervention. Why does it matter? Dynamic Portfolio Views empowers asset managers and investors to: - combine the strength of a database with the easy journey of excel. - reduce time spent on complex Excel calculations between tables and enable granular portfolio analysis. - integrate and manipulate all the levels of information in a transaction (loans, debtors, collaterals, cashflows, liabilities). How does it work Dynamic Portfolio Views enhances your user experience with the following improvements: **Asset-class configuration:** - **Tailored views**: Create custom views for different asset classes allowing users to see default sheets and fields relevant to their particular assets. - **Relevant fields**: Access essential information for each asset type, simplifying analysis. For example, users managing Residential Mortgage-Backed Securities (RMBS) will have default views for assets, real estate collaterals, guarantee collaterals, and debtors. **Customizable tables:** - **Excel-like interface**: Easily manage portfolio tables offering features such as renaming tabs, adding new sheets, and drag-and-drop column reordering. - **Flexible data manipulation**: Adapt column names to internal models for seamless integration, Instead of adjusting to static database terminology, therefore saving valuable time. **Calculated fields:** - **Custom calculations on pre-existing tables**: Easily create new columns based on existing data to derive specific insights. - **Enhanced data analysis**: Add specific calculations to the standardized data model to gain deeper understanding and enable a more personalized experience. **Automated exports:** - **Timely data delivery**: Receive automated file exports via email or directly to the database whenever updates are made. - **Efficient workflow:** Eliminate manual data extraction and distribution. - **Customizable data views**: Create and share as many data views as needed, tailoring the information accessible to specific customers. See how Dynamic Portfolio Views transforms the way you manage, analyze, and share portfolio data. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Climate risk assessment with location data providers](https://cardoai.com/whats_new_in_product/climate-risk-assessment-with-location-data-providers/) **Published:** January 21, 2025 **Author:** RobertaCardo26 **Excerpt:** Recent events, including the wildfires in Los Angeles and the tornadoes in Florida, have underscored the critical importance of climate risk assessment for investors in asset-based finance and the power of location data providers. As covered in a recent issue **Content:** Contents [Introduction](#introduction) [The challenge](#first-par) [Geo and Climate Risk Assessment](#second-par) [GeoMap in action: A Use Case](#fifth-par) Introduction Recent events, including the wildfires in Los Angeles and the tornadoes in Florida, have underscored the critical importance of climate risk assessment for investors in asset-based finance and the power of location data providers. As covered in a recent issue of [Green Street](https://greenstreetnews.com/product/asset-backed-alert/)’s *Asset-Backed Alert*, some concerned investors, lacking detailed data about the underlying collateral in their portfolios, are now turning to climate risk data providers. By triangulating the latitude and longitude of transaction collateral, they aim to determine if their bonds are backed by assets in disaster-stricken areas. Natural catastrophes not only threaten the underlying collateral in securitizations but also create uncertainty for investors who lack granular visibility into the geographic exposure of their portfolios. In this context, advanced tools like Cardo AI’s **GeoMap feature** have become indispensable for managing risk, calculating performance, and proactively balancing exposure. The challenge: limited geographic visibility in a riskier world In an era of environmental and climatic unpredictability, investors in asset-backed securities often face significant challenges: - **Uncertainty about asset exposure**: Without precise data on asset locations, it’s challenging to determine whether investments are vulnerable to localized disasters. - **Inaccurate risk assessment**: Aggregated geographic data often lacks the precision needed to identify high-risk areas, leaving investors in the dark about emerging threats. **Delayed response times**: A lack of real-time insights can result in missed opportunities to mitigate losses or rebalance portfolios effectively. GeoMap: A game-changer for geographic and climate risk assessment Cardo AI’s GeoMap feature, included in our asset-based finance software, addresses these challenges by providing investors with detailed, real-time geographic data on their asset-backed investments. Using precise latitude and longitude coordinates, GeoMap offers unparalleled visibility into collateral location, enabling investors to make informed decisions. ![Collaterals’ geographical concentration](https://cardoai.com/wp-content/uploads/2026/03/image-1-2-2048x817-1-1024x409.png) *Image 1: Collaterals’ geographical concentration* Here’s how GeoMap empowers investors: 1. **Granular risk assessment** GeoMap allows investors to pinpoint the exact locations of underlying assets, providing clarity on exposure to specific risks such as wildfires, hurricanes, and other natural disasters. For example: - - During the Los Angeles wildfires, investors could use GeoMap to instantly determine whether their collateral was situated within affected areas. - In Florida, investors could evaluate their exposure to tornado-stricken regions, identifying assets in high-risk zones and planning accordingly. 2. **Performance monitoring** GeoMap provides real-time updates on the status of assets within risk zones. Investors can monitor how events impact performance, such as delinquency rates or cash flows, and use this information to make data-driven decisions. *![Collaterals’ exact locations with profitability metrics](https://cardoai.com/wp-content/uploads/2026/03/image-1-1-2-2048x695-1-1024x348.png)* Image 2 – Collaterals’ exact locations with profitability metrics 3. **Proactive portfolio balancing** By identifying geographic clusters of high-risk assets, investors can strategically rebalance their portfolios, reducing exposure to affected areas while diversifying into safer regions. GeoMap’s insights help mitigate losses and optimize returns, even under adverse conditions. 4. **Integration with external risk data** GeoMap can be enriched by integrating external datasets, such as climate risk scores, FEMA disaster maps, or air quality indices, to provide a holistic view of risk. This allows investors to anticipate potential future impacts and proactively mitigate them. GeoMap: A Use Case Consider an investor managing a portfolio of residential mortgage-backed securities (RMBS) in California. As wildfires spread, the investor can: 1. Use GeoMap to identify properties within the wildfire-affected regions. 2. Assess how the potential destruction of collateral may impact bond performance. 3. Rebalance exposure by investing in bonds backed by assets in safer regions, such as the Midwest or Northeast, using insights from GeoMap’s risk analysis. This capability enables the investor to not only safeguard their current investments but also build a more resilient portfolio for the future. Climate events won’t wait for better data. Make sure you can see what’s at risk before the next one hits. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Early warnings: real-time visibility, proactive control](https://cardoai.com/whats_new_in_product/early-warnings-real-time-visibility-proactive-control/) **Published:** July 11, 2024 **Author:** RobertaCardo26 **Excerpt:** Early warning signals are essential for navigating the dynamic landscape of structured finance. By gaining a clear view of your structured finance mandates, you can identify potential risks early and take proactive measures to mitigate them. Our solution empowers you with real-time visibility and proactive control. Unify your view: Gain a comprehensive dashboard that seamlessly monitors covenants, triggers, and limits across all your structured finance m **Content:** Contents [What’s in it for you](#first-par) [Why does it matter](#second-par) [Use case](#third-par) Early warning signals are essential for navigating the dynamic landscape of structured finance. By gaining a clear view of your structured finance mandates, you can identify potential risks early and take proactive measures to mitigate them. What’s in it for you Our solution empowers you with real-time visibility and proactive control. - **Unify your view**: Gain a comprehensive dashboard that seamlessly monitors covenants, triggers, and limits across all your structured finance mandates. This real-time data stream keeps you informed and in control. - **Early warning system**: Don’t wait for problems to arise. Set customized thresholds for key metrics and receive instant alerts via email, SMS, or in-app notifications. This proactive approach allows you to address potential issues before it’s too late. Confidence through compliance and risk management: - **Covenant tracking**: Our system meticulously tracks all covenant requirements, alerting you to potential breaches. This ensures you stay compliant and take corrective action when necessary. - **Trigger monitoring**: Stay informed about critical financial events that may impact your deals. Set specific parameters to detect triggers and respond swiftly, safeguarding your portfolio’s health. - **Limit oversight**: Maintain financial stability with instant notifications when limits are approached or breached. This proactive risk management prevents unexpected exposures. Tailored for your needs: - **Customized thresholds**: Fine-tune the early warning system to your specific risk profile. Define thresholds for various metrics and indicators to receive alerts that truly matter. Why does it matter Real-time monitoring, early warnings, and seamless integration empower you to navigate the complexities of structured finance with confidence. Gain the control you need to: - **Mitigate risks:** Proactive identification of potential issues helps minimize losses and maintain portfolio health. - **Ensure compliance:** Our comprehensive covenant tracking feature keeps you on top of contractual obligations. - **Facilitate timely interventions:** Servicers can react swiftly to breaches, preventing problems from escalating. - **Enhance communication:** Foster better collaboration and informed decision-making with stakeholders through real-time data transparency. Use case **Challenge**: Our client, a financial institution managing a diverse structured finance portfolio needed to safeguard against potential risks. They leveraged our advanced trigger monitoring system to track the default/charge-off ratio. This ratio indicates the proportion of loans or financial products that have defaulted or been charged off compared to the total portfolio. They set a critical threshold of 5% and an early warning at 3%. **Solution**: The system constantly monitored the ratio in real-time. For an extended period, the default/charge-off ratio remained below the 3% early warning threshold, indicating a stable portfolio. However, as market conditions shifted, the default/charge-off ratio gradually increased and reached 3.1%. The early warning threshold breach triggered automatic email and in-app alerts for our client. **Results**: This prompt notification allowed our client to quickly review the portfolio and identify the underlying factors contributing to the increased default/charge-off ratio. A particular segment of the portfolio was experiencing higher-than-expected defaults due to economic downturns in specific regions. Armed with this critical information, they took proactive measures to mitigate the risk, including adjusting the exposure to the affected regions, enhancing credit assessment criteria for new loans, and increasing reserves to cover potential losses. The timely intervention helped stabilize the default/charge-off ratio, which eventually fell back to 2.9%, below the early warning threshold. See how Cardo AI keeps you one step ahead of every covenant breach, trigger, and limit breach. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Payment waterfall modeling, simplified](https://cardoai.com/whats_new_in_product/payment-waterfall-modeling-simplified/) **Published:** January 30, 2025 **Author:** RobertaCardo26 **Excerpt:** When a company takes on debt, it must pay interest and fees to its capital provider as specified in the credit agreement. These payments are essential to maintain access to funding. A payment waterfall defines the sequence in which lender of a ABF transactions are are paid, typically monthly or quarterly. **Content:** Contents [Introduction](#introduction) [What’s in it for you?](#second-par) [Why does it matter?](#third-par) [How does it work?](#fourth-par) [Use Case](#sixth-par) Introduction When a company takes on debt, it must pay interest and fees to its capital provider as specified in the credit agreement. These payments are essential to maintain access to funding. A payment waterfall defines the sequence in which lender of a ABF transactions are are paid, typically monthly or quarterly. When evaluating risk and profitability in an ABF investment, the payment waterfall plays a crucial role. Modeling payment waterfalls is often challenging, particularly in Excel, and even more so for intricate structures with complex dependencies and execution rules. Structure finance professionals often face problems like time-consuming manual setup, difficulty visualizing payment sequences, and a high risk of errors when adapting models to align with contractual provisions. These challenges can slow down deal analysis and risk assessments. What’s in it for you? Our Payment Waterfall Tool empowers users to design, model, and simulate payment waterfalls with accuracy and ease. Waterfall can be used for both simulation purposes and execute payments. It offers a range of advanced features to streamline the modeling process and enhance your analysis: - **Intuitive visualization**: Clearly define the computation of each payment and visualize the sequence in an intuitive interface. - **Drag-and-drop functionality**: Effortlessly reorder payments with simple drag-and-drop actions. - **Trigger modeling**: Define execution rules for different Waterfalls (e.g., pre-enforcement vs. post-enforcement waterfalls) to ensure accurate and compliant modeling. - **Contractual alignment**: Model waterfalls as close as possible to the contractual provisions, reaching “penny perfect precision” levels. - **Pre-deal simulation**: Simulate waterfall structures and test different payment mechanisms, tweaking triggers and thresholds to analyze impacts and results. Why does it matter? The payment waterfall configuration tool empowers structured finance professionals to: - **Ensure accuracy**: Align waterfall structures with legal documentation to reduce errors and improve compliance. - **Save time**: Simplify complex waterfall modeling with intuitive tools. - **Enhance analysis**: Evaluate the impact of different assumptions or inputs on deal performance. - **Build trust**: Deliver transparent, replicable models for all stakeholders. How does it work? Users can easily create the payment structure and visualize the sequence of each payment. The tool supports the creation of nested structures, allowing users to define multiple layers of payments. Payments can be reordered effortlessly using a drag-and-drop interface. For each payment, users can define the computation method, providing maximum flexibility in specifying formulas. Multiple waterfalls can be configured, and users can also set triggers that determine which waterfall should be executed under specific conditions. Once the payments and waterfalls are set up, the tool first evaluates which waterfall should be executed. It then processes each payment sequentially. Based on the available funds, the tool calculates the amount to be paid and determines any unpaid amounts that can be carried forward to the next payment date. Additionally, users can define different waterfall structures and simulate their impact on liabilities’ returns by stressing input variables. *![Payment Waterfall Structure](https://cardoai.com/wp-content/uploads/2025/01/waterfall_1-1024x879.png)* *Figure 1: Payment Waterfall Structure* Use Case **The challenge** A global [asset manager](https://cardoai.com/accelerate-growth/buyside/) overseeing a portfolio of Collateralized Debt Obligations (CDOs) faced challenges in accurately modeling payment waterfalls due to complex deal structures. Their transactions involved multiple layers of payments distributed across as many as 20 liability tranches. Additionally, the interest payments on the notes were determined by a combination of triggers calculated from the underlying portfolio, such as the quarterly rolling excess spread average and default levels. **The solution** After adopting our Waterfall Tool, the firm simplified its process: - Easy setup: They used the drag-and-drop feature to create accurate payment hierarchies quickly. - Clear rules: The tool allowed them to define specific conditions, such as acceleration triggers, directly within the model. - Error detection: By running the payment waterfall with our tool, they identified mismatches between their models and the calculations provided by the transactions’ Trustees, helping them avoid costly mistakes - Structure optimization: By running simulations, they anticipated losses across different scenarios and identified liability structures to optimize returns on new deals. **The results With our tool, the firm: - Corrected fund distribution errors, ensuring accurate allocation - Predicted cash shortfalls in junior tranches, helping them adjust strategies early. - Designed better liability structures for new deals using accurate simulations. See how Cardo AI models your entire ABS waterfall and stress-tests every scenario in seconds. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [You can’t clap with one hand: unlock the power of Insurers in Private Credit](https://cardoai.com/whats_new_in_product/you-cant-clap-with-one-hand-unlock-the-power-of-insurers-in-private-credit/) **Published:** February 4, 2025 **Author:** RobertaCardo26 **Excerpt:** Historically, insurers have relied on stable returns, often achieved through traditional bond portfolios. However, with yields dwindling in recent years, they are turning to private credit. In particular, Asset Based Finance aligns neatly with insurers’s needs, catering to their long-duration liabilities and predictable cashflow requirements, while also offering diversification and reduced volatility compared to public markers. **Content:** Contents [Introduction](#introduction) [US vs Europe](#third-par) [Strategic collaborations](#fourth-par) [Why Private Credit? ](#fifth-par) [The perfect synergy](#eighth-par) [Regulatory changes](#ninth-par) [Bridging the gap](#tenth-par) [How Cardo AI helps](#eleventh-par) Introduction Historically, insurers have relied on stable returns, often achieved through traditional bond portfolios. However, with yields dwindling in recent years, they are turning to private credit (see graph 1). In particular, Asset Based Finance aligns neatly with insurers’s needs, catering to their long-duration liabilities and predictable cashflow requirements, while also offering diversification and reduced volatility compared to public markers. ![US High Yield and Corporate OAS](https://cardoai.com/wp-content/uploads/2025/02/image-1-1024x704.png) *Image 1: US High Yield and Corporate OAS* Insurers are increasingly joining forces with private credit managers to unlock the potential of private asset-based finance (ABF). As they shift away from traditional public markets, these collaborations offer the opportunity for higher risk-adjusted returns. By partnering with experienced asset managers who optimize costs and tailor investments to specific needs, insurers can better navigate this landscape and maximize their impact. US vs Europe: A historical perspective on private credit Private credit plays a significantly larger role in US insurance compared to Europe. In the US, insurers have already increased their private credit holdings to 36% of their total investments in the region according to Moody’s Ratings. While European insurers are also adjusting their strategic allocations to the changing market conditions, the industry remains comparatively under-allocated. This disparity reflects key differences between the regions: private markets are far more developed in the US, where insurers often have strong relationships with alternative asset managers, providing better access to private credit opportunities. European insurers, on the other hand, have traditionally favored highly liquid, investment-grade fixed-income assets, such as government and corporate bonds, due to their more risk-averse investment approach. Strategic collaborations: real-world examples In an era of environmental and climatic unpredictability, investors in asset-backed securities often face significant challenges: - Uncertainty about asset exposure: Without precise data on asset Global insurers are increasingly partnering with asset managers to access private credit opportunities. Most recent examples are [Northwestern Mutual](https://www.ai-cio.com/news/northwestern-mutual-to-take-stake-in-sixth-street-form-investment-partnership/) partnering with Sixth Street, and [Generali](https://www.wsj.com/finance/investing/generali-and-natixis-ink-asset-management-joint-venture-5b0cab12) teaming up with Natixis. Guardian Life and HPS have also deepened their strategic ties. These collaborations extend beyond traditional partnerships, with capital infusions and acquisitions highlighting the growing alignment between insurers and asset managers. [MetLife Investment Managemen](https://investor.metlife.com/news/news-details/2024/MetLife-Investment-Management-to-Acquire-PineBridge-Investments/default.aspx)t is set to acquire PineBridge Investments, [Manulife Investment Management](https://www.manulifeim.com/institutional/global/en/about-us/press-releases/manulife-investment-management-completes-acquisition-of-cqs) has completed the acquisition of CQS and [Blue Owl Capital](https://www.blueowl.com/news/blue-owl-capital-acquire-kuvare-asset-management-750-million) is acquiring Kuvare Asset Management for $750 million. This trend reflects a broader industry move towards private credit, with insurers planning to significantly increase their allocations in the coming years. Why Private Credit? Enhanced returns and diversification Unlike public market instruments, private credit investments provide access to niche opportunities that often offer higher yields due to their limited liquidity, allowing investors to achieve higher returns while maintaining stability in their portfolios *(as illustrated in graph 2)*. This is especially beneficial for insurers, who can partner with experienced private credit managers to gain direct access to asset originators and eliminate intermediaries. This direct approach not only drives cost efficiencies but also enhances control over asset selection and structuring, enabling investments to be tailored to specific risk and return objectives. *![Private Performing Credit Index - Q3 2024](https://cardoai.com/wp-content/uploads/2025/02/image-1-1-1024x728.png)* *Source: [Private Performing Credit Index – Q3 2024](https://hl.com/insights/private-performing-credit-index-q3-2024/)* Moreover, private credit offers unique diversification benefits by exposing investors to non-traditional asset classes such as real estate debt, infrastructure finance, and asset-backed lending, which tend to have low correlations with public markets and help mitigate broader market volatility. The strong covenant protections often associated with private credit further reduce credit risk, while the predictability and flexibility of cash flows make it an ideal solution for meeting long-term liabilities such as pension risk transfers and structured settlements. Even the perfect synergy requires navigating risk Despite its benefits, private credit comes with inherent risks that require careful management: - **Illiquidity**: Limited liquidity can create challenges during unexpected cash needs. - **Complexity**: The opacity of private credit instruments demands robust due diligence and specialized expertise. - **Concentration risk**: Overexposure to specific asset classes or industries can undermine diversification. - **Regulatory scrutiny**: As private credit grows in popularity, insurers must adapt to increasing regulatory scrutiny and evolving compliance requirements. Regulatory changes: implications for insurers The regulatory landscape is evolving, with the National Association of Insurance Commissioners (NAIC) in the United States, introducing measures to ensure insurers effectively manage risks in private credit. The regulator is focusing on two major risks: - **Tail risk in subordinated tranches** The goal is to tackle regulatory rating arbitrage, where owning equally weighted tranches in CLOs or Asset-Backed Securities (ABS) often results in a lower risk-based capital (RBC) charge than holding the underlying assets, even when the default risks are comparable. Currently, in some CLOs and ABS structures, this lower “Blended RBC Charge” suggests that the security carries less default risk than the underlying assets. While subordination typically provides adequate protection for investment-grade tranches, it may not offer the same level of security for speculative-grade tranches. - **Concentration risk** Asset-backed structures are typically treated as uncorrelated individual securities with systematic risk. However, cumulative risks can emerge if the underlying assets across issuers are highly correlated, such as belonging to the same asset class, industry, or geography. These risks are similar to those seen in the 2008 Financial Crisis, where price changes across correlated assets amplified systemic vulnerabilities. The NAIC is particularly concerned about this type of concentration risk, which can occur at various levels, including single borrowers (e.g., Company XYZ), asset classes (e.g., data centers, offices, autos), industries, or regions. This misjudgment of correlation could lead to significant idiosyncratic risk in insurers’ portfolios. The NAIC aims to finalize an updated RBC framework for CLOs and ABS by 2025. Beyond the insurance sector, regulatory bodies like the Federal Reserve and SEC are also increasing oversight, with initiatives such as expanded FR Y-14 reporting for non-bank lending and broader access to private markets through updated accredited investor definitions. These developments are set to reshape insurers’ strategies, balancing risk management with capital formation goals. Bridging the gap: the need for technological innovation Insurance companies have the opportunity to ride the coming wave of private credit, capturing the lion’s share of the $40tn opportunity market. However, this potential is threatened by a complex and evolving regulatory landscape, which imposes significant constraints on the capital that can be deployed. Effectively managing risk in this environment requires not only a level of transparency that private markets have historically lagged to provide but also robust tools capable of handling vast amounts of data efficiently. Due diligence processes can often be inefficient, lacking the technological sophistication to provide a comprehensive view of underlying assets. Furthermore, reporting often falls short of insurer expectations, with limited customization and infrequent updates hindering effective monitoring and decision-making. For asset managers, this presents a crucial moment to adopt more innovative, technology-driven solutions that align with the specific requirements of insurance investors. By leveraging automation and advanced analytics, they can bridge the gap between private credit and insurers’ needs. How Cardo AI helps Portfolio Look-through and scenario analysis are crucial in assisting insurers navigate complex risk decisions in private credit investments. They also act as essential tools for monitoring multidimensional – idiosyncratic risks while supporting broader goals of maintaining financial stability by managing systemic risks. **Scenario analysis**: Scenario analysis can provide insurers with a valuable tool to manage tail risks effectively by identifying vulnerabilities, aligning capital charges, and mitigating regulatory arbitrate. - *Addressing rating arbitrage*: Scenario analysis allows insurers to conduct a transparent comparison of risk between the underlying collateral (securities) and liabilities. By testing the resilience of both under identical adverse scenarios, insurers can demonstrate whether the tranches justify reduced capital charges. Hence the insurer’s portfolio can be better aligned with regulatory requirements and enables better-informed capital allocation decisions. - *Mitigating tail risk in speculative-grade tranches*: Speculative-grade tranches, such as BB-rated, are particularly vulnerable in adverse conditions, with equity-like loss potential. Scenario analysis quantifies the exposure of these tranches to extreme market events, such as widespread defaults or sector-specific shocks. This analysis highlights their disproportionate risk and allows insurers to adjust portfolios by reducing exposure to high-risk tranches or reallocating capital to more senior tranches. *Example:* *Correcting discrepancies in RBC charges* *Structured tranches can face discrepancies in RBC charges compared to the underlying collateral, which can distort risk assessments. Scenario analysis identifies mismatches by simulating how collateral risks flow through the structure (waterfall) and negatively affects individual tranches (especially subordinated). Hence, scenario analysis, can assess that RBC charges accurately reflect the true risk, enabling insurers to optimise their portfolios by favouring securities where the risk-adjusted capital charge is justified.* **Concentration risk**: As highlighted by the NAIC’s focus on correlated exposures across issuers, sectors and geographies, look-through technology helps manage concentration risk through: - Consolidated exposure analysis across multiple issuers to reveal hidden correlations - Dynamic visualization of risk concentrations at borrower, industry and geographic level - Automated monitoring of concentration limits with customizable alerts*Example:* Utilising an interactive dashboard, an insurer managing a diversified portfolio of CLO’s can aggregate issuer (borrower) exposure by seniority, to identify hidden correlations (potential default risk) across different transactions. **Monitoring collateral evolution**: Look-through capabilities enable insurers to track underlying portfolio and collateral performance over time: - Analysis of historical trends in collateral quality metrics like delinquency rates - Early identification of deteriorating credit quality in underlying pools - Enhanced regulatory reporting demonstrating a thorough understanding of asset performance By incorporating look-through technology with powerful scenario analysis, it allows insurers to dynamically assess their underlying risk exposure to take proactive actions for risk mitigation and optimizing capital efficiency. *Example: An insurance firm can track historical performance for delinquency rates by geography and simultaneously assess trends in credit quality of the underlying portfolio to proactively adjust the portfolio’s strategic asset allocation.* **Advanced AI analysis for insurers**: CARDO AI is pioneering next-generation scenario analysis with generative AI. Our platform rapidly simulates complex economic downturns, predicts asset performance across diverse portfolios, and identifies vulnerabilities in high-risk tranches, delivering predictive insights and tailored capital reallocation recommendations. See how Cardo AI gives insurers the transparency and tools to confidently scale into private credit. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Transforming RMBS securitization with data intelligence](https://cardoai.com/whats_new_in_product/transforming-rmbs-securitisation-with-data-intelligence/) **Published:** February 27, 2025 **Author:** RobertaCardo26 **Excerpt:** Residential assets, whether whole loans or securitised as Residential Mortgage-Backed Securities (RMBS), are inherently complex, requiring data intelligence to manage their evolving risks. RMBS, in particular, stand apart from other ABS due to their long maturities, high prepayment sensitivity, and strong exposure to interest rate fluctuations. **Content:** Contents [Introduction](#introduction) [Seamless data ingestion and validation](#first-par) [Real-time risk tracking](#second-par) [Smarter collateral insights](#third-par) [Anticipating market shifts](#fourth-par) Residential assets, whether whole loans or securitised as Residential Mortgage-Backed Securities (RMBS), are inherently complex, requiring **data intelligence** to manage their evolving risks. RMBS, in particular, stand apart from other ABS due to their long maturities, high prepayment sensitivity, and strong exposure to interest rate fluctuations. That’s where **Cardo AI’s asset-based finance software** comes in. More than just portfolio tracking, our platform transforms portfolio management – seamlessly integrating data ingestion, real-time monitoring, collateral intelligence, advanced geo-analysis, and stress testing. We equip investors and asset managers with the speed and precision needed to **stay ahead in an unpredictable market**. Seamless loan-level data ingestion and validation **The challenge**: RMBS securitization portfolios rely on multiple loan originators, servicers, and data providers, each using different reporting formats and validation standards. This results in inconsistencies across borrower details, property addresses, square footage, or employment status. - Critical risk indicators – such as delinquency rates, prepayment speeds, and home price appreciation (HPA) – can be incomplete or outdated. - Missing or inaccurate data can lead to mispriced RMBS tranches, poor risk allocation, and unreliable securitization models. **How we solve it**: Our *Data Surveillance Tool* performs automated ingestion, validation, and reconciliation across loan tapes, servicer reports, and market data sources. With real-time anomaly detection, it flags missing or inconsistent data before errors impact decision-making. This tool also performs key reconciliations, including: - Cash flow vs. current balance reconciliation – Verifies that reported loan balances align with actual cash flow data. - Days in delay check – Ensures that delinquent loans reflect the correct number of days past due. - Performance status validation – Cross-checks expected vs. realized cash flows and delinquency status to confirm reported loan performance. By automating the tracking of property valuations, loan balances, and payment histories, our Data Surveillance tool enhances risk assessment and portfolio oversight. *Example: Our platform detects a batch of loans in the updated version of the target tape where property appraisal values have recently changed. With the support of a notification system, directly spot those properties for which there has been a change and immediately verify if the change is correct with the input data.* Real-time risk tracking: Know your portfolio, always **The challenge**: RMBS securitization are highly sensitive to home price appreciation (HPA), and macroeconomic conditions. Traditional risk models fail to capture dynamic borrower behavior, leading to unexpected defaults, early repayments, or liquidity mismatches. Loan-level performance tracking is often inadequate, making it difficult to detect early signs of distress. **How we solve it**: Our real-time *Monitoring Tool* tracks prepayment, default, and curtailment trends, revealing hidden risks before they impact portfolio performance. - Loan-level analytics help investors assess the impact of interest rate movements, borrower credit shifts, and regional economic trends. - Stratification dashboards break down performance by loan vintage, geographic exposure, FICO bands, and DTI ratios, providing granular insights for strategic decision-making. *Example: An RMBS investor notices prepayments spiking in a specific loan pool after a Federal Reserve rate cut. Our platform helps them adjust cash flow expectations and hedging strategies in real-time.* Smarter collateral insights: geospatial intelligence for RMBS securitisation **The challenge**: Collateral risk is more than just LTV (loan-to-value) – factors like crime rates, climate risk, and economic conditions significantly impact property values. Yet, traditional models fail to integrate spatial intelligence into valuation and risk management. **How we solve it**: Our GeoMap Tool enhances collateral analysis by instantly mapping properties and overlaying critical data, such as: - Proximity to transport hubs - Crime rates and safety indicators - Accessibility to key urban services - Environmental risk factors (e.g., flood zones, wildfire-prone areas). *Example: Our platform helps an investor discover that 25% of loans backing a high-rated RMBS tranche are concentrated in climate-vulnerable regions (hurricane or wildfire zones). This insight allows them to adjust loss assumptions and re-evaluate bond pricing.* Anticipating market shifts: simulation and stress testing **The challenge**: RMBS securitization are highly susceptible to economic shifts – without predictive modeling, investors fail to anticipate liquidity risks, default waves, or yield compression. **How we solve it**: Our Simulation Tool models multiple macroeconomic scenarios to evaluate RMBS securitisation portfolio resilience under different conditions. - Users can simulate the impact of rate hikes, home price declines, or rising unemployment on prepayment and default rates. - Sensitivity analysis helps investors identify vulnerabilities in specific loan pools or securitized tranches. *Example: Our platform’s scenario analysis indicates a potential 10% rise in adjustable-rate mortgage (ARM) delinquencies with a 1.5% mortgage rate increase, enabling investors to proactively refine risk models and prepayment forecasts.* See how Cardo AI turns complex residential data into clear, confident decisions. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Automated financials and covenant monitoring](https://cardoai.com/whats_new_in_product/automated-financials-and-covenant-monitoring/) **Published:** March 10, 2025 **Author:** RobertaCardo26 **Excerpt:** Covenants are key conditions in loan agreements that borrowers must meet to maintain financing terms. Lenders and asset managers rely on covenant monitoring to assess financial health and ensure compliance, but manual tracking using spreadsheets is slow, error-prone, and inefficient. **Content:** Contents [Introduction](#introduction) [What’s in it for you?](#second-par) [Why does it matter?](#third-par) [How does it work?](#fourth-par) [Use Case](#fifth-par) Introduction Covenants are key conditions in loan agreements that borrowers must meet to maintain financing terms. Lenders and asset managers rely on covenant monitoring to assess financial health and ensure compliance, but manual tracking using spreadsheets is slow, error-prone, and inefficient. Managing financial reports across multiple borrowers and reporting periods becomes increasingly complex, making it difficult to detect breaches, compare financial versions, and analyze trends effectively. What’s in it for you? At Cardo AI, we’ve built a tool within our asset-based finance software that automates financial data ingestion, tracks trends over time, and updates covenants in real time, eliminating the need for spreadsheets. Our platform: - Simplifies financial data uploads: Import financial reports with a single click or API integration. - Tracks covenants in real time: Automatically updates compliance status when new financials are uploaded. - Enables historical comparison: Stores and compares multiple versions (e.g., reported vs. adjusted) for deeper insights. - Offers custom metrics: Rename financial fields to better align with the specific industry and create tailored financial metrics. Why does it matter? Without automation, lenders and asset managers waste time, introduce errors, and struggle with compliance. Our solution: - Keeps financial records organized and accessible, enabling better performance. - Saves time and reduces errors by automating covenant monitoring. - Ensures transparency and compliance with a full audit trail, useful for regulatory reviews and internal reporting. How does it work? - Upload financial data: Import monthly, quarterly, semi-annual, annual, LTM or YTD financial reports. - Get instant insights: The platform automatically calculates key financial metrics like EBITDA, revenue growth, leverage and ICR, displaying them in interactive graphs. - Manually update the covenant: The platform allows for manual updates to covenants, maintaining logs of non-numerical covenants, including affirmative and negative covenants, as well as borrower declarations and representations. - Monitor covenants in real time: The system checks compliance instantly, flagging breaches as soon as new data is uploaded (*e.g., if a Net Leverage covenant requires monthly monitoring, it updates automatically with each new report).* - Manage adjustments & waivers: If needed, users can also manually update covenant levels, record waiver requests. - Maintain a full audit trail: Every financial update and covenant decision is logged, ensuring a clear, traceable compliance record. Use Case A private credit lender extends a €50 million loan to a mid-sized manufacturing company. The loan agreement includes quarterly financial reporting requirements and a Net Leverage covenant, which must stay below 4.5x EBITDA. **The challenge**: The lender relies on spreadsheets to manually track financial statements and covenant compliance. The borrower submits financial reports 10 days late, delaying the lender’s ability to review key financial metrics. By the time the lender manually processes the numbers, an EBITDA drop has already pushed Net Leverage to 4.7x, exceeding the agreed covenant. However, this breach goes unnoticed for over a month, allowing the borrower to continue drawing funds. By the next quarter, leverage has increased further to 5.2x, leaving the lender with limited options to mitigate risk. The delayed identification of these issues also creates problems for investor reporting and regulatory compliance. **The solution:** With Cardo AI’s automated covenant monitoring, the borrower uploads financials directly into the system, which instantly processes the data, calculates key metrics, and updates covenant levels in real time. The moment Net Leverage exceeds 4.5x, an alert notifies the lender, prompting immediate action to request a waiver, adjust loan terms, or demand additional collateral. The platform also analyzes historical trends, identifying early warning signs of EBITDA decline and allowing the lender to intervene before the situation worsens. The automated audit trail ensures full transparency, making regulatory reviews and investor reporting seamless. See how real-time covenant monitoring could transform your compliance workflow. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Meet the Cardo AI Excel Add-in: your entire portfolio data, one click away](https://cardoai.com/whats_new_in_product/meet-the-cardo-ai-excel-add-in-your-entire-portfolio-data-one-click-away/) **Published:** October 29, 2025 **Author:** RobertaCardo26 **Excerpt:** If you live in Excel, you shouldn’t have to jump between portals, exports, and manual cleanup just to get the data you need. Our Excel Add-in brings your entire portfolio data straight from our platform into the spreadsheet you already use – securely, consistently, and fast. **Content:** Contents [Introduction](#introduction) [Why Excel, why now?](#first-par) [Keep your Excel, benefit from the Cardo AI’s platform](#second-par) [Why you can pull into Excel (in seconds)](#third-par) [Two ways to work: automated or on-demand](#fourth-par) [Serve your team where they work](#fifth-par) Introduction If you live in Excel (and who in structured credit doesn’t?), you shouldn’t have to jump between portals, exports, and manual cleanup just to get the data you need. The Cardo AI Excel Add-in brings your entire portfolio data straight from our platform into the spreadsheet you already use – securely, consistently, and fast. Clean data every time. Whether you’re preparing investor packs, running stress tests, checking limits, or fielding last-minute questions from the Investment Committee, the Add-in puts your latest portfolio truth directly at your fingertips. Why Excel, why now? - Serve you where you are. No context switching, no CSV wrangling. - Trust the source. Pull data that’s already governed on the Cardo AI platform – same definitions, same lineage. Standardised every time. - Scale the boring stuff. Automate recurring pulls and repeatable reports so teams can focus on analysis, not admi Keep your Excel, benefit from the Cardo AI’s platform - Automated data pipeline. Leverage Cardo AI’s automated data pipeline to retrieve, sanitize and standardize data from any source and any format. - Data control. Over 600 different controls granting data quality and consistency over time. - Any calculation, any time. Let the Cardo AI platform manage the heavy lifting of managing millions of data points and calculations, use the results in your spreadsheet. Why you can pull into Excel (in seconds) Connect to any transaction on Cardo AI’s platform and retrieve data from: - **Analytics**: curated, analytics-ready views for monitoring and reporting, based on pre-canned templates or fully custom ones - **Stratifications**: instant [strat tables](https://cardoai.com/whats_new_in_product/stratification-the-power-of-layered-risk-analysis/) by vintage, sector, geography, rating, and more - **Limits and triggers**: exposure, concentration, and policy limits for oversight and compliance - **Transactions**: deal-level data, cash flows, waterfalls, and performance metrics - **Assets and liabilities**: raw data access for all your custom analysis needs Use it for reporting, stress testing, data sharing, ad-hoc checks, and model inputs, without manual exports. Two ways to work: automated or on-demand **1) Data workflow (set it & forget it)** Automate weekly reports, month-end packs, board dashboards, and more. Data Workflow automates data retrieval so your files refresh on schedule with the latest available data. Next time you open the workbook – or when your automation runs, your sheets update with one click. No re-mapping, no re-exporting. **2) Data on demand (answer it now)** Need the latest numbers right now? Data on Demand lets you manually pull fresh data into your active workbook. Perfect for one-off analyses of data, IC prep, or validating assumptions on the fly. Serve your team where they work Cardo AI’s mission is simple: bring trusted portfolio data to the tools you already use. The Excel Add-in allows your team to reduces friction, improves governance, and gives your team time back. - **Effortless stress testing**: quickly run [stress tests](https://cardoai.com/solutions/by-product/stress-testing/) on your portfolio using the latest data, all within Excel - **Flexible, customizable, presentation-ready reports**: generate analytics-ready reports for any need - **Tailored insights**: leverage Cardo AI’s powerful analytics engine for custom insights directly in your workbook. See how Cardo AI turns your Excel into a live, governed window into your entire portfolio. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Data Intelligence Engine: data profiling, with more power](https://cardoai.com/whats_new_in_product/data-profiling-and-ai-ensure-the-quality-of-your-data/) **Published:** December 18, 2025 **Author:** RobertaCardo26 **Excerpt:** Working with financial data in spreadsheets often means reconciling transactions, analyzing loan portfolios, and validating counterparty files under tight timelines. Understanding a loan tape received from an originator, or any dataset provided by a counterparty, can quickly become complex **Content:** Contents [Introduction](#introduction) [How dow it work](#first-par) [What’s in it for you](#second-par) [Why does it matter](#third-par) [Use case](#fourth-par) Introduction Working with financial data in spreadsheets often means reconciling transactions, analyzing loan portfolios, and validating counterparty files under tight timelines. Understanding a loan tape received from an originator, or any dataset provided by a counterparty, can quickly become complex, especially when column definitions are unclear and relationships across fields are implicit rather than documented. As a result, analysts are often forced to trade accuracy for speed. Time-consuming manual reviews slow down analysis, while incomplete validation increases the risk of missed issues. For investors and asset managers, decisions depend on having data that is accurate, consistent, and internally coherent. Ensuring anomalies are identified and correctly handled is critical to confident decision-making. How does it work Our Data Intelligence Engine analyzes your files and highlights both not previously known relationships and unexpected behaviours not falling under the usual Data Quality layers: 1. **Column level descriptive statistics:** - Frequency and distribution: understand how values are distributed within each column, identifying dominant patterns, rare occurrences, and unexpected concentrations – easy check that the geographical distribution is as expected. ![](https://cardoai.com/wp-content/uploads/2026/03/CARDO-AI-Product-Data-Intelligence-Engine-1.jpg "CARDO-AI-Product-Data-Intelligence-Engine-1") - **Complete statistical summary**: including minimum, maximum, average, standard deviation, percentiles and sums to quickly assess scale, dispersion, and consistency of numerical fields. - **Outliers**: identify extreme or abnormal values that deviate from expected ranges and may indicate data quality issues or exceptional cases – easy check that the outstanding amounts are consistent. - **NULL value analysis**: detect missing data patterns to understand completeness, structural gaps, and potential data quality risks – easy check that the identifiers are populated. - **Correlations**: measure statistical relationships between fields to identify dependencies, redundancies, or unexpected linkages across the dataset – easy check that the higher the rating the lower the interest rate. ![Correlation](https://cardoai.com/wp-content/uploads/2026/03/CARDO-AI-Product-Data-Intelligence-Engine-2-1024x550.jpg) **2. Relationships and anomalies across columns:** - **Linear relationships**: detect whether a column is derived from others (for example, sums, differences, or ratios) and flag records where those relationships break – easy check that amounts and fees are reconciled correctly. - **Association rules**: identify recurring patterns between variables of the form “If X happens, Y tends to happen”. Rules are ranked by the number of columns involved and by confidence, helping surface both simple and complex dependencies – easy check if there is a charge-off the status is aligned. - **Business rules**: automatically infer meaningful structured-finance relationships based on column names and semantics (e.g. balances, flags, dates, or amounts) – easy check that cashflows and purchased amount are reconciled correctly. - **Date rules**: validate logical and sequential relationships between date fields, checking whether expected temporal dependencies are respected – easy check the sequence of issue and maturity date. - **Missing value analytics**: analyze which fields are populated or null together, uncovering relationships such as default flags linked to default amounts or dates – easy check that bankruptcy flag and amount are aligned. Each layer assigns confidence levels and highlights anomalies, returning the specific **loan identifiers** that do not follow the identified patterns. What’s in it for you - **Serve you where you are**. No context switching, no CSV wrangling. - **Trust the source**. Pull data that’s already governed on the Cardo AI platform – same definitions, same lineage. Standardised every time. - **Scale the boring stuff**. Automate recurring pulls and repeatable reports so teams can focus on analysis, not admin. - **Reduce significantly time spent on file investigation**: in minutes, the Data Intelligence Engine reveals relationships that would otherwise take hours of manual analysis. It avoids unnecessary noise: if no consistent patterns are detected, it means the data does not exhibit stable relationships, saving time by confirming there is nothing to investigate. The result is faster, leading to more informed decisions without compromising analytical rigor. - **Uncover data patterns through statistical analysis outside the common Data Quality frameworks**: whether working with amounts, quantities, or dates, the engine provides comprehensive analytics to understand how data behaves, including: - *Outlier detection*: identifies extreme values that may indicate errors, inconsistencies, or exceptional cases, using statistical thresholds based on observed distributions. - *Flag analysis*: validates expected behaviors of boolean or categorical fields (e.g. bankruptcy dates populated when status equals bankruptcy), ensuring logical consistency across columns. - *Data drift monitoring:* detects changes in relationships over time, highlighting potential structural shifts, evolving data practices, or emerging anomalies. - **Identify data anomalies with ease**: the engine pinpoints inconsistencies that can block or slow down data conversion. For example, if a column is expected to be the sum of two others, it immediately identifies the specific loans where this relationship does not hold, allowing targeted remediation instead of manual review. Why does it matter - **Speed up onboarding for new asset classes, portfolios and vendors:** the Data Intelligence Engine enables teams to rapidly understand and validate data from new asset classes and counterparties. Analysts can now have in a bunch of minutes an understanding on the main logics of tapes for asset classes never inspected before. Decisions and analysis are based on verified, standardized outputs, reducing the risk of errors caused by poor data quality or incorrect assumptions. Because results are consistently structured, recurring analysis becomes faster and easier over time. - **Catch surprises early, at the most granular level, before they hit reports or investors:** By identifying hidden relationships, inconsistencies, and anomalies upfront, the engine helps prevent issues from propagating into downstream analytics, reporting, or investor communications. Use case **Challenge:** Assess a new Asset-Based Finance transaction within two days ahead of the investment committee decision. The analysis involved a loan origination history of over 3 million loans and 18 million cash flows, with the primary objective of building confidence in data quality by identifying potential issues early. **Solution:** a junior analyst, ran the data tape through the **Data Intelligence Engine**, which analyzed all **58 columns** of the loan tape to understand data behavior and cross-field relationships, without requiring manual rule configuration or custom controls. The engine automatically uncovered multiple anomalies, including: - Misalignments between cut-off and close dates, prepaid dates occurring after maturity dates. - Inconsistencies between the initial and ending balance of the loans considering the daily cash flows reported. This led to a recomputation of the ending balance based on the cash flows available to have a clearer picture. - Recurring behaviour in loans close to reaching 90 days past due that were flagged as prepaid: by delving into this with the originator, the analyst found out that those loans were indeed repurchased/refinanced with the purpose of curbing the default rate of the portfolio. This approach enabled a broader and more accurate assessment than manual review alone, without the overhead of manual setup and while avoiding the false positives commonly generated by generic AI tools or traditional data profiling systems. **Results:** By surfacing inconsistencies early, the client gained a clear and comprehensive understanding of data quality and used these insights to reassess and strengthen the negotiation strategy with the originator. ![Anomalies identified](https://cardoai.com/wp-content/uploads/2026/03/CARDO-AI-Product-Data-Intelligence-Engine-3-1024x771.jpg) See how Cardo AI catches data surprises early, so they never make it into your reports or investor communications. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Cardo AI for Asset Managers and Insurance LPs](https://cardoai.com/whats_new_in_product/cardo-ai-for-asset-managers-and-insurance-lps/) **Published:** February 3, 2026 **Author:** sviluppatore **Excerpt:** Asset managers running complex private-credit and mortgage-style strategies (direct CRE mortgages, mezzanine, specialty collateral like telecom towers & data centers) face the same bottleneck: one underlying portfolio, many insurance LP “clients,” each with statutory-grade requirements. **Content:** Contents [Introduction](#introduction) [ Your portfolio isn’t the hard part. LP servicing is](#first-par) [Cardo AI: Your framework for scale and optimization](#second-par) [Why this matters to asset managers](#third-par) Introduction Asset managers running complex private-credit and mortgage-style strategies (direct CRE mortgages, mezzanine, specialty collateral like telecom towers & data centers) face the same bottleneck: one underlying portfolio, many insurance LP “clients,” each with statutory-grade requirements. Cardo turns NAIC Schedule B from a quarterly burden into a repeatable [reporting factory](https://cardoai.com/solutions/by-product/insurance-reporting/), and upgrades the same dataset into allocation-ready capital insights insurers and their asset managers actually use. Your portfolio isn’t the hard part. LP servicing is With multiple insurers across multiple transactions and hundreds of underlying assets, the pain is operational: - Insurer-specific slices: ownership pro-ration, CPB/MV, covenants, appraisals. - Consistent definitions: NOI / DSCR / LTV conventions that hold up under scrutiny. - Audit defensibility: traceability, approvals, “what changed & why”. - Statutory alignment: Schedule B expectations + internal governance. Insurers allocate based on capital, not yield alone. They need a dataset that supports allocation committees, capital migration monitoring, and compliance sign-off, not another spreadsheet pack. Cardo AI: Your framework for scale and optimization **1. A regulatory-grade reporting spine. Schedule B source-of-truth** - Schedule B-ready dataset per insurer / per transaction / per period (clean IDs, consistent definitions, as-of snapshots). - Validation + controls for completeness, consistency, break detection. - Complete Audit trail from inputs to outputs, with approvals and change log. **2. A multi-investor servicing engine – the scalability unlock** - Investor-slice reporting at scale: one asset pool, many insurer-specific outputs. - Template governance: investor formats without bespoke rebuilds each quarter. - Exception workflow: resolve issues once, complete stale appraisals, missing dates, DSCR anomalies, and propagate consistently. **3. Capital intelligence insurers understand – allocation, not just reporting** - RBC/CM-aligned lens using the metrics you already track (DSCR/LTV + status). - Migration watch to flag “near-threshold” exposures before committee meetings. - Explainability pack to support governance. Why this matters to asset managers **Make your strategy “insurance-compatible” and easier to fund** - Faster diligence and less LP Q&A with a familiar statutory narrative. - More repeatable LP servicing across insurers, deals, and reporting periods. - Greater allocation potential by proving capital-efficient quality and surfacing early warnings **Core outputs: what your LPs actually receive** - Per-insurer Schedule B source data: position-level, ownership-adjusted, period snapshots. - Insurance allocations pack (per deal): CM distribution, drivers, exceptions, and trends. - Capital heatmap: exposure by CM bucket, collateral and geography. - Migration watchlist: loans most likely to change capital treatment. - Audit & traceability bundle: source fields, methodology, approvals, change log. If insurance LP servicing is limiting fundraising velocity or ops scale, Cardo turns Schedule B into an advantage. See how Cardo AI transforms Schedule B from a compliance burden into a fundraising advantage. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [AI powered document processing for Private Credit and Asset Based Finance](https://cardoai.com/whats_new_in_product/ai-powered-document-processing-for-private-credit-and-asset-based-finance/) **Published:** March 27, 2026 **Author:** RobertaCardo26 **Excerpt:** Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. **Content:** Contents [What it does](#introduction) [Key capabilities](#second-par) [How teams use it](#tenth-par) [Why it matters](#eleventh-par) [Coming soon](#twelve-par) Eliminate review risk: AI-powered, fully traceable document intelligence for ABS finance. Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. What it does The CardoAI Document Reader turns dense PDFs into instant, defensible answers and structured data. It leverages best of the breed LLMs coupled with our own ABS‑savvy RAG framework. Doc Reader reads, identifies relevant information, retrieves, and reasons across your documents, then cites exactly where every answer came from. Key capabilities - **OCR built for finance and legal teams**: Our proprietary models read scans, tables, footnotes, and redlines. We detect insertions/deletions and capture table structure so you don’t lose context. And the downstream LLM is able to see page and table layouts like an analyst would – this allows it to extract all data points with 100% confidence. ![Accurate table detection and parsing: The system is able to identify specific clauses within tables contained in an RMBS prospectus](https://cardoai.com/wp-content/uploads/2026/03/cardo-ai-document-reader-1-1024x257.png) *Image 1: Accurate table detection and parsing: The system is able to identify specific clauses within tables contained in an RMBS prospectus* - **ABS‑tuned hybrid retrieval (keyword + semantic):** We developed a domain model that blends exact‑match keyword retrieval with semantic search. In our tests on an internal ABS benchmark corpus, our retriever delivered an approximate 10‑point lift in retrieval metrics compared to leading general‑purpose baselines, including OpenAI models. - **Automated question rephrasing:** Our system expands and rewrites user questions into ABS‑aware variants to boost recall, precision, and coverage – without extra work for the analyst. *![ Beyond Ctrl+F: The system rephrases user questions and matches the semantics of the document. Top: an appraisal report use case; bottom: an auto receivables transaction use case.](https://cardoai.com/wp-content/uploads/2026/03/cardo-ai-document-reader-2-1024x276.png)* ![ Beyond Ctrl+F: The system rephrases user questions and matches the semantics of the document. Top: an appraisal report use case; bottom: an auto receivables transaction use case.](https://cardoai.com/wp-content/uploads/2026/03/cardo-ai-document-reader-3-1024x266.png) *Image 2: Beyond Ctrl+F: The system rephrases user questions and matches the semantics of the document. Top: an appraisal report use case; bottom: an auto receivables transaction use case.* - **Low‑hallucination prompting:** We use a grounding strategy that prioritizes citations and refusal when the answer isn’t in the docs. On a challenging real life ABS corpus, including for instance scanned financial statements and appraisal reports, we achieved near perfect extraction accuracy on all the relevant tables and data points. ![Citations and cross referencing: At the bottom, the Cardo AI system correctly cites the section and table cells where it retrieved the information, cross checking the correctness of the section name with a Fees and Expenses summary table from an auto receivables transaction prospectus.](https://cardoai.com/wp-content/uploads/2026/03/cardo-ai-document-reader-4-1024x270.png) ![Citations and cross referencing: At the bottom, the Cardo AI system correctly cites the section and table cells where it retrieved the information, cross checking the correctness of the section name with a Fees and Expenses summary table from an auto receivables transaction prospectus.](https://cardoai.com/wp-content/uploads/2026/03/cardo-ai-document-reader-5-1-1024x201.png) *Image 3: Citations and cross referencing: At the bottom, the Cardo AI system correctly cites the section and table cells where it retrieved the information, cross checking the correctness of the section name with a Fees and Expenses summary table from an auto receivables transaction prospectus.* ![ Refusal when missing in the text: When asked questions whose answers would not be grounded in the text, instead of hallucinating, the system correctly refuses to answer, motivating its refusal. An RMBS use case](https://cardoai.com/wp-content/uploads/2026/03/cardo-ai-document-reader-6-1024x194.png) *Image 4: Refusal when missing in the text: When asked questions whose answers would not be grounded in the text, instead of hallucinating, the system correctly refuses to answer, motivating its refusal. An RMBS use case.* - **Definition intelligence:** Our named‑entity engine flags defined terms, locates where they’re defined across the corpus, and provides a grounded summary of meaning and implications in plain English. - **Team question recommendations:** Unsupervised signals surface the most popular questions your teammates ask, sharing knowledge, accelerating onboarding and standardizing reviews. - **Templated extraction:** Users can automate retrieval at scale with custom templates and receive structured outputs mapped to their own fields or data dictionary – ready for downstream systems. - **Full traceability:** Every answer links back to the most relevant page and paragraph so you can verify in seconds. Moreover, unlike general purpose RAG systems, the Cardo AI platform correctly cites source sections and paragraphs. - **Bulk extraction via API:** We offer programmatic access to ingestion, hybrid retrieval, Q&A with citations, definition deep-dives, and template‑driven extraction for portfolios at scale. - **Enterprise grade framework:** Leverage best in class LLM models in a secured and optimized framework: we are not competing with major LLMs, we are putting them on steroids thanks to our ABF specific framework, delivering enterprise level security and data management. Overall, our document intelligence platform substantially reduces hallucination rates – often to zero – outperforming general-purpose, state-of-the-art standalone LLMs and RAG systems that we benchmarked and found fell short of our quality standards. **To date,** **the framework achieved a 95% live accuracy on a range of documents spanning from ABF contracts and real estate reports, to invoices and financial statements**. How teams use it - **Due diligence:** Pull covenants, triggers, eligibility criteria, reps & warranties, cure periods. - **Ongoing surveillance:** Track definition drift across vintages and issuers; monitor triggers and performance tests. - **Operations and servicing:** Locate notice requirements, waterfalls, and servicing standards without combing through hundreds of pages. - **Financial analysis:** Extract metrics, footnotes, and financial results from financial statements with table‑aware OCR. - **Valuations:** Parse appraisal packages to capture comparables, adjustments, and assumptions consistently. Why it matters - Faster reviews, fewer misses: From hours to minutes with answers you can audit. - Consistency at scale: Standardize what “good” looks like across analysts and portfolios. - Built for ABS nuance: Reads the structures, definitions, and redlines that drive real risk. Get results you can cite and safely integrate in your high-stakes downstream applications. Ready for a quick demo or a sample extraction on your documents? Coming soon - **Tailor made models:** Train your own retrieval models on proprietary documents. As few as 5–10 unlabeled documents are enough – while still enforcing strict data segregation – to achieve state‑of‑the‑art performance on your data, saving time iterating to get to the data points you need. We’re bringing this self‑serve capability to the UI and API so teams can spin up private retrieval models on demand. - **Standalone table extraction and data correction:** not just full legal documents. Soon we’ll offer the OCR+LLM interface as a standalone feature for small documents like balance sheets and financial statements. For instance, this will allow users to save manual data-entry time and reduce mistakes when computing and monitoring deal covenants by automating the full process. See how Cardo AI gives your analysts more time to think and less time to search. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Data lineage: full transparency on how your data becomes insight](https://cardoai.com/whats_new_in_product/data-lineage-full-transparency-on-how-your-data-becomes-insight/) **Published:** May 20, 2026 **Author:** GiadaCardo26 **Excerpt:** Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. **Content:** Contents [Full traceability, end to end](#first-par) [From fragile to auditable](#second-par) Numbers in an analytics platform are only as trustworthy as the logic behind them. When an investor challenges an assumption, an auditor requests documentation, or a risk team needs to validate a methodology, the answer should already be there. **Cardo AI’s Data Lineage** makes that possible, giving users **complete visibility** into how every field displayed in the platform is derived, from source to screen. Full traceability, end to end For each and every figure displayed in Cardo AI, users can **trace** exactly where it comes from. From the raw data provided by servicers and trustees, through the ingestion and transformation pipeline, all the way to the value shown on screen. **Powered by LLMs** (Large Language Models), the system automatically documents and interprets every step in plain language, so that the story behind any number is always **one click away.** ![Laptop screen displaying a data analytics dashboard with a left navigation rail and a floating Data Lineage panel titled Data Lineage on the right.](https://cardoai.com/wp-content/uploads/2026/05/cover-video-data-lineage.png) From fragile to auditable Governance and auditability in structured finance are not optional. Yet in most workflows, tracing a number back to its origin means going back to the source manually, digging through transformation scripts, and hoping the methodology is documented somewhere. That is a fragile foundation. What makes this particularly challenging is that relevant metrics are rarely a direct reflection of the raw data. Whether a loan is classified as defaulted, for instance, depends on contractual definitions that vary across transactions. The same underlying event can be treated differently depending on the deal, and those distinctions have real analytical consequences. Data Lineage gives users the tools to navigate that complexity with confidence. Every field carries its own explanation, from the source data all the way through the logic that shaped it. Reviews that used to require back and forth with data teams, reading of documentation and analysis of excel spreadsheets (or even code!) now take seconds. Audit trails and investor transparency become a natural byproduct of the analytical workflow, not a separate exercise that happens after the fact. See how Cardo AI gives your analysts more time to think and less time to search. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Trade Order Management System for portfolio managers](https://cardoai.com/whats_new_in_product/pre-trade-compliance-technology-for-portfolio-managers/) **Published:** December 11, 2024 **Author:** RobertaCardo26 **Excerpt:** Traditional credit management tools often impede portfolio managers with rigid processes, limited transaction capabilities, and inadequate compliance controls. OurTrade Order Management System transforms portfolio management by enabling seamless rebalancing, ensuring compliance, **Content:** Contents [Introduction](#introduction) [What’s in it for you?](#first-par) [Why does it matter](#fourth-par) [How does it work?](#fifth-par) [Use Case](#sixth-par) Introduction Traditional credit management tools often impede portfolio managers with rigid processes, limited transaction capabilities, and inadequate compliance controls. OurTrade Order Management System transforms portfolio management by enabling seamless rebalancing, ensuring compliance, minimizing operational risks, and enhancing transparency across your organization. Technology for Trade Order Management: What’s in it for you? Our advanced Trade Order Management System offers a comprehensive suite of features designed to optimize your credit portfolio management workflow directly from your portfolio monitoring and security master: - **Add and reduce your positions:** Effortlessly initiate buy or sell actions for any position within your portfolio. - **Draft and approval workflow:** Create drafts for transactions and utilize a robust approval process with four-eye controls to ensure accuracy and regulatory compliance. - **Efficient rebalancing:** Streamline the rebalancing of positions across multiple portfolios to maintain optimal asset allocation. - **Automated transaction details:** Pre-filled fields based on selected positions reduce manual entry and possible errors e.g. the system monitors allocation limits for credit facilities. - **Yield and price calculations:** Simulate the impact of the trade on the portfolio in terms of risk and return. - **Comprehensive validation rules:** Ensure transactions comply with internal portfolio limits and investor mandates and requirements through automated validations. - **Document management:** Attach relevant documents to trades for seamless internal communication and record-keeping. - **Reporting:** Provide clear visibility to internal stakeholders regarding portfolio construction changes and transaction histories. ![Portfolio trade overview table](https://cardoai.com/wp-content/uploads/2024/12/trade_order-image-1-1-1024x329.png) ![Increase position exposure](https://cardoai.com/wp-content/uploads/2024/12/trade-order-image-2-1024x502.png) Technology for Trade Order Management: Why does it matter? Trade Order Management isn’t just about transactions – it’s about driving better outcomes for your portfolio and your organisation. By **automating compliance checks and risk controls**, it ensures adherence to internal limits and regulatory standards, reducing operational and underwriting errors. In addition, it optimizes portfolio performance by enabling managers to **proactively rebalance positions** in response to market trends and evolving investment strategies. Efficiency improves through automated workflows and intuitive interfaces, streamlining operations. Technology for Trade Order Management: How does it work? Managers can easily initiate transactions, choosing to **buy or sell positions directly from either the portfolio or position level**. This flexibility is further enhanced with precise control over portfolio adjustments, as rebalancing can be initiated directly from a specific instrument. The system simplifies transaction workflows by automating many details. Key fields, such as **minimum lot size, available amounts, and current portfolio weights, are pre-filled** based on the selected position, reducing manual input and errors. For additional customization, managers can manually input new weights, rebalance amounts, and execution prices to ensure the portfolio reflects strategic decisions accurately. Validation and execution are robustly supported through **dynamic rules** that verify trade actions comply with predefined buy/sell decisions, internal portfolio limits, and regulatory standards. The system calculates IRR and execution prices to aid informed decision-making, while commissions and settlement dates are automatically applied to align with booking requirements. A **four-eye approval process** ensures each transaction is thoroughly reviewed and authorized by a second manager, adding an extra layer of compliance and governance. The Trade Order Management System streamlines operations with seamless **document management.** Managers can attach trade-related files, ensuring smooth communication, while **historical tracking** of all transaction details – such as amounts, dates, and responsible personnel – enables effortless monitoring and reporting. With these capabilities, Trade Order Management System ensures that portfolio adjustments are handled efficiently, with compliance and collaboration at the forefront, delivering a transparent and optimized workflow for portfolio managers. Use Case: Delayed draw facility for multiple portfolios **Challenge** An Asset Manager managing a corporate loan portfolio faced significant challenges with a delayed draw facility, which required sequential allocation across multiple credit portfolios. Tracking utilization, managing liquidity, and reflecting funded amounts in real-time within the portfolio system proved difficult. **Solution** Our Trade Order Management solution addressed these challenges by streamlining the handling of delayed draw facilities. The solution automated updates to portfolio exposure, ensured real-time visibility of funded amounts, and embedded compliance safeguards. **Outcome** As a result, the Asset Manager achieved enhanced operational efficiency by eliminating manual monitoring of loan exposure for incremental draws across multiple spreadsheets, and improved compliance. The system provided accurate portfolio updates, reduced manual errors, and strengthened reporting and auditing processes to support investment strategies effectively. See how Cardo AI takes the friction out of every trade, from draft to approval. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [PIK loans: automating interest accruals and risk monitoring](https://cardoai.com/whats_new_in_product/pik-loans-automating-interest-accruals-and-risk-monitoring/) **Published:** November 22, 2024 **Author:** RobertaCardo26 **Excerpt:** At Cardo AI, we’ve built a dedicated solution for PIK loans, automating real-time interest accrual tracking and risk monitoring to ensure lenders maintain accurate, up-to-date exposure visibility. Payment-in-Kind (PIK) loans offer companies a way to ease cash flow constraints by deferring interest payments and addi **Content:** Contents [Introduction](#introduction) [What’s in it for you?](#second-par) [Why does it matter?](#third-par) [How does it work?](#fourth-par) [Use Case](#fifth-par) Introduction At Cardo AI, we’ve built a dedicated solution for PIK loans, automating real-time interest accrual tracking and risk monitoring to ensure lenders maintain accurate, up-to-date exposure visibility. Payment-in-Kind (PIK) loans offer companies a way to ease cash flow constraints by deferring interest payments and adding them to the loan principal, effectively capitalizing the interest. While this provides immediate relief, it introduces complexities for lenders due to its structures and optional features. Accurate accounting of non-cash transactions, compliance assurance, and assessing leverage ratios, covenants, and overall risk all add complexity. What’s in it for you? - Flexible configuration: choose the PIK option defined in the legal agreement and configure it through a user-friendly interface. - Automated interest calculations: let the engine run complex computations for you according to the terms specified in the configuration. - Handy consultation of contractual PIK terms: refresh your memory on PIK terms historically applied without the need to open (or re-open) the contractual agreement. - Easy terms update following contractual amendments: update or remove your PIK terms mirroring a contractual amendment. Why does it matter? - Streamlines calculations: Automatically computes expected cashflows based on the chosen PIK option, eliminating the need for manual recalculations, saving time, and reducing errors. - Simulates cashflows: Offers a full view of PIK-based accruals through the facility’s maturity, supporting accurate performance metric calculations. - Tracks PIK details: Maintains records of PIK information and updates to contractual terms, ensuring timely and accurate references for facility performance assessment. How does it work? **Flexible PIK configurations** Cardo AI’s solution, within our asset-based finance software, supports flexible, user-friendly configurations that include: - **True PIK**: All interest is paid in kind for the entire duration of the facility. - **PIK Toggle** ***(Pay If You Like)***: Allows the borrower to choose whether to pay cash interest, in kind, or as a combination of both for any given period. - **Pay If You Can**: Requires the borrower to pay interest in cash unless specific conditions, such as financial covenants (e.g., debt-to-equity or interest coverage ratios), necessitate payment in kind. For all these options, users can configure details like fixed or floating PIK rates (capitalizing only the margin, base rate, or both), the applicability period, and how accrued interest is handled (PIK, cash, or a mix). **Automated management and adjustments** Once configured, the system updates Payment Schedules to include PIK details such as applicability, capitalized amounts, and daily accruals. In cases of a PIK Toggle, users can decide installment-by-installment whether to apply PIK fully, partially, or not at all, with the system ensuring accurate updates. Configurations can be adjusted to reflect contractual changes, such as extending or reducing applicability periods, modifying rates, or altering PIK portions in True PIK scenarios. Users can also access a term history with detailed logs of all actions performed on the platform, ensuring accurate tracking of all amendments and calculations. Use case A Credit Fund that manages a portfolio of direct lending transactions needs to manage and monitor PIK interest on its portfolio of loans, ensuring that the interest is properly tracked and reported over time. Our PIK loans solution supports the fund with: **PIK Interest calculation:** The solution automatically calculates the accrued PIK interest and ensures the accrual is properly compounded. **Accrual monitoring**: The user can view real-time accrual data, including interest added to the loan balance for each calculation period (e.g., monthly, quarterly, annually), and monitor the increased exposure due to capitalization. **Accurate payment processing:** The solution updates the interest instalment status based on the PIK component (full PIK, PIK-cash mix, full cash) generating the payment notice and sending the correct information to a payment processing service (where available). **Report generation**: The user can generate detailed reports outlining the PIK interest calculations, including the starting principal, interest rate, accrual periods, and the total interest accumulated. **Audit and compliance checks:** The solution tracks historical accruals and maintains a log of all calculations, making it easier for the user to audit the interest calculations for compliance with financial regulations or loan agreement terms. **Post-calculation adjustments:** If the loan terms change (e.g., interest rate adjustment or early repayment), the user can input the changes into the tool, automatically adjusting the PIK interest accrual calculations. See how Cardo AI automates every layer of PIK complexity, from accrual tracking to compliance reporting. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Beyond the loan tape: how Cardo AI supports the full data landscape of a structured finance deal](https://cardoai.com/whats_new_in_product/beyond-the-loan-tape-how-cardo-ai-supports-the-full-data-landscape-of-a-structured-finance-deal/) **Published:** June 4, 2026 **Author:** GiadaCardo26 **Excerpt:** Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. **Content:** Contents [How does it work?](#first-par) [What’s in for you](#second-par) [Why does it matter?](#third-par) [Use Case 1](#fourth-par) [Use Case 2](#fifth-par) In structured finance, loan-level data is a big deal, but still only part of the story. Every deal comes with a broader analytical landscape: underwriting curves that define expected performance, business plans that set the investment thesis, market benchmarks that provide context, waterfall schedules that govern how cash flows are distributed. These aggregated, deal (or market)-level datasets are not supplementary. They are central to how investors understand, monitor, and report on their portfolios. Cardo AI now supports both layers natively. Alongside the granular loan-by-loan records that power portfolio analytics, a new layer in our data model brings any additional data into the same governed, analytics-ready environment. How does it work? The **new data layer** can store any relevant data: from market benchmarks and underwriting curves to third-party data used for credit or identity verification.If it informs the analysis, it can live in Cardo AI’s data model alongside the underlying loan-level data. Once loaded, the data is immediately available in analytics functionality, enabling comparisons, charts, and reports that bring the aggregated and asset-level layers together in a single unified view. What’s in for you **Faster time to insight:** no more pulling data from separate sources, aligning formats, or verifying whether numbers are comparable. Teams get straight to the analysis that matters. **Deeper deal intelligence:** questions that used to require jumping between systems, like tracking actual performance against an underwriting curve, reconciling a waterfall, or benchmarking a portfolio against the market, can now be answered within a single unified workflow. **Operationally leaner:** fewer tools, fewer handoffs, and no reconciliation overhead. A cleaner and more complete way to manage and analyze deal data from day one. **Expand your transaction oversight:** manage and monitor your entire portfolio in Cardo, including transactions where data is available at the portfolio level only rather than loan by loan, for a truly complete view of your investments in one place. Why does it matter? The best investment decisions are made with the full picture: every single data available has to be taken into account. Combining asset, deal and market level data is a fundamental part of operating with the rigor that private credit demands, and teams that can do that in one place are faster to spot issues, more reliable in their reporting, and better equipped to act with conviction. Moving seamlessly between granular loan-level behavior and the broader deal-level context, underwriting assumptions, market positioning, cash flow distribution, makes the difference between an analytical workflow that keeps up with the pace of the business and one that slows it down. Use Case 1 Our client, a leading global alternative asset manager, needed to analyze the performance of their consumer loan portfolios against original underwriting expectations. The challenge was scale. Consumer loan transactions contain millions of individual records, making it practically impossible to perform that kind of analysis manually or in traditional spreadsheet tools. Uploading underwriting loss and prepayment curves and comparing them against actual portfolio behavior simply could not be done at that volume without a system built to handle it. By leveraging Cardo’s new deal-level data layer, the client was able to load their underwriting curves directly into the system and analyze them alongside the underlying loan-level data. With millions of records processed and made available in one environment, they could perform a complete deal analysis, tracking how actual losses and prepayments were evolving relative to original projections, across the full granularity of the portfolio. The ability to operate at that scale turned what had been an analytical gap into a core part of their investment monitoring process. [![Dashboard: Waterfall - Interest Proceeds selected with totals and a line-item table of amounts due and reconciliation status as of 21-Jan-25](https://cardoai.com/wp-content/uploads/2026/05/loan-tape-use-case-1-1-1024x688.png "loan-tape-use-case-1")](https://cardoai.com/wp-content/uploads/2026/05/loan-tape-use-case-1-1-1024x688.png) Use Case 2 Our client, a leading global CLO manager overseeing multiple transactions across the US and European markets, faced a growing operational challenge as their portfolio scaled. With each transaction generating its own trustee reports covering positions, cash movements, and waterfall distributions, performing a triparty reconciliation across all three dimensions manually in Excel had become unsustainable. The volume of data, combined with the structural complexity of each transaction’s waterfall, left significant room for error and absorbed analytical resources that should have been focused elsewhere. By loading trustee waterfall data directly into Cardo, the client was able to reconcile positions, cash, and waterfall payments against the values computed by the system in a single unified environment. What had previously required painstaking manual work across multiple spreadsheets became a structured, repeatable process, with full visibility into where figures aligned and where discrepancies arose. The result was a faster and more reliable reconciliation workflow across all transactions, with a clear audit trail and a significant reduction in operational risk. [![Six-panel dashboard showing monthly UVW Losses (orange) and CGL (gray) trends for Sep-24 to Feb-25, with filters per panel.](https://cardoai.com/wp-content/uploads/2026/05/loan-tape-use-case-2-1-1024x605.png "loan-tape-use-case-2")](https://cardoai.com/wp-content/uploads/2026/05/loan-tape-use-case-2-1.png) See how Cardo AI gives your analysts more time to think and less time to search. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Maximize integrity in ABF transactions with our Data Validation](https://cardoai.com/whats_new_in_product/maximize-integrity-in-abf-transactions-with-our-data-validation/) **Published:** April 8, 2024 **Author:** RobertaCardo26 **Excerpt:** The lack of integrity in asset based finance transactions can have severe repercussions, from legal and regulatory issues to reputational damages and financial losses. Therefore, it is important to implement robust controls and governance mechanisms to mitigate these risks. This is where Cardo AI’s Data Validation comes in to help. **Content:** Contents [What’s in it for you](#introduction) [Why does it matter](#second-par) [Key capabilities](#third-par) [Use case 1](#fifth-par) [Use case 2](#sixth-par) The lack of integrity in asset based finance transactions can have severe repercussions, from legal and regulatory issues to reputational damages and financial losses. Therefore, it is important to implement robust controls and governance mechanisms to mitigate these risks. This is where Cardo AI’s Data Validation comes in to help. What’s in it for you - Gain more success in building strong relationships with investors. Swift action for loan originators means faster funding for your clients. - Respond to regulatory requirements far faster and more accurately, safeguarding against reputational damage and financial penalties. - From days to seconds. Slash your report verification time and reduce operational costs. - Enhance your data intelligence across all investment strategies and leverage data from ABL transactions. Why does it matter? From calculating borrowing bases to managing cash flow waterfalls, every decision in today’s ABF landscape depends on the reliability of your data. Consider the volume of data involved: calculating available draw amounts, monitoring performance triggers, and activating contractual protections, all require an understanding beyond basic error detection. While data health checks offer a valuable first line of defense, they often fall short in the face of ABF’s complexities. Investing in Data Validation is an investment in confidence and efficiency. Key capabilities Cardo AI has introduced a two-step control process to guarantee the highest standards of data quality: **Step 1 – Data health check**: It meticulously reviews each dataset to ensure compliance with specific transaction contracts. By identifying and rectifying discrepancies early, DHC helps maintain seamless transaction flows and supports consistent data reliability. **Step 2 – Data validation checks**: It delves deeper, focusing on data consistency over time. It scrutinizes changes from one period to the next, confirming their validity. Leveraging a vast library of use cases, this step ensures continuous data integrity. Advanced Data Validation: Ensure accuracy and consistency in data processing through comprehensive validation algorithms. Our data validation process includes three components: - *Asset level reconciliation:* Our platform independently predicts asset values at the start of each period, aligning them with received data. This includes recalculating outstanding balances, assessing days in arrears and performance status, and verifying cash flows and interest payments for accuracy. - *Consistency checks:* Cardo AI’s system detects adjustments in reported data, including changes to anticipated cash flows. This ranges from simple date inconsistencies to deliberate alterations affecting asset performance or borrower concessions. - *Portfolio level reconciliation:* Cardo AI extends validation beyond individual assets to the portfolio level. This includes reconciling portfolio changes over time and matching reported cash flows with actual bank balances. Our approach directly compares with live bank data, preventing costly errors or delays in reporting and payments often missed in standard health checks. **What happens with mismatches?** Every time a mismatch is identified, we not only pinpoint it but also offer a clear explanation for its possible cause, ensuring users can directly review all the affected data. These mismatches can be quickly handled through a streamlined workflow. This includes notifying the data provider of the discrepancy and solving the issue by overriding the data. Cardo AI makes it easy for any data provider to access our platform. This means the entire data management process, from syncing and checks to fixing any issues, can be carried out independently. *![](https://cardoai.com/wp-content/uploads/2024/04/Maximize-transaction-integrity-1-1024x258.png)Image 1: Cardo AI’s data validation feature visualized inside our structured finance platform* Use case 1 Our client, a dynamic **asset manager**, is revolutionizing the way real estate instant buyers secure funding through an innovative asset-based facility. They were struggling with Excel calculations and time intensive verification processes. Cardo AI Data Validation has streamlined the entire borrowing base calculation, empowering them to expedite drawdown requests and make informed decisions in seconds, not days. Thanks to our AI-driven validation, they have gained more accuracy and agility in their financing operations. With real-time insights at their fingertips, they have been able to rebalance advance rates and optimize contractual provisions, all while focusing on driving new transactions and extracting insights from their data. Use case 2 Our client is a European **digital lending platform** for SMEs powered by asset-backed notes. Backed by a diverse group of investors across 10+ unique transactions, they navigate a complex landscape of limits and triggers. With Cardo AI Data Validation, loan classification management has ceased to be a hurdle. No more waiting for investor checks or lengthy validation periods. With our solution, portfolio data has been seamlessly uploaded onto the platform, instantly triggering checks and enabling originators to take action. Investors could effortlessly track progress through a dedicated dashboard ensuring transparency. See how Cardo AI catches data mismatches before they become costly errors. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Streamline complex ABL transactions through our tailored, end-to-end environment](https://cardoai.com/whats_new_in_product/navigate-abl-transactions-with-ease-through-our-new-tailored-solution/) **Published:** April 22, 2024 **Author:** RobertaCardo26 **Excerpt:** Successfully managing Asset-Based Lending (ABL) transactions demands expertise and continuous attention. Lacking the appropriate technology makes it difficult to efficiently analyze extensive data sets, leading to delays in decision-making and hampering adaptability to market shifts. **Content:** Contents [Introduction](#introduction) [What’s in it for you?](#first-par) [Why does it matter?](#second-par) [How does it work](#third-par) [Use Case](#fourth-par) Introduction Successfully managing Asset-Based Lending (ABL) transactions demands expertise and continuous attention. Lacking the appropriate technology makes it difficult to efficiently analyze extensive data sets, leading to delays in decision-making and hampering adaptability to market shifts. That’s where Cardo AI’s ABL solution steps in, mitigating inaccuracies in asset tracking, covenant management, and collateral monitoring. What’s in it for you? - Save operational costs by using our solution to manage, execute and report your ABL transactions. - Win more ABL deals with a faster bidding strategy, enabled by our solution. - Increase the probability to reach your fund return targets, even with complex ABL transactions, regardless of your private credit investment strategies. - Enhance data intelligence across all investment strategies and leverage data from ABL transactions. - Boost funding allocation by improving transparency for institutional investors. Why does it matter? Navigating Asset-Based Lending (ABL) can be complicated for several reasons: - ABL transactions are complex, involving intricate financial structures like revolving credit lines, term loans, and asset-based loans. - ABL agreements impose strict covenants to safeguard lenders’ interests, often encompassing financial ratios, performance metrics, and activity restrictions. - Collateral displays diversity, from accounts receivable and inventory to machinery and equipment, encompassing various asset types. - Collateral values may fluctuate over time, posing challenges in accurately assessing and monitoring asset dynamics. - ABL transactions must comply with stringent regulatory and legal frameworks, adding complexity to facility and collateral management. How does it work By centralizing both facilities and collateral data and integrating advanced monitoring capabilities, our ABL solution helps predict cash flow and analyze scenarios, giving crucial insights into borrower and collateral performance. Here’s top 5 features: 1. Customize your facilities with our **Facility Module:** tailor settings according to your needs and configure diverse facility types with ease in a flexible environment (automatically calculate expected payments and interest accruals, set margin ratchets and triggers). 2. Simplify funding requests with our **Workflow Manager:** facilitate a smoother funding process tailored to the unique requirements of each agreement. 3. Ensure accurate funding requests with our **Borrowing Base**: verify eligibility and concentration limits effortlessly, and ensure the correct application of advance rates. 4. Track borrower performance easily with **Borrowers Monitoring**: simplify the standardization of borrowers’ financial data and gain insights into borrower behavior and financial health to make informed lending decisions. 5. Easily manage covenants with **Covenants Tracking:** perform automated borrower compliance checks, enabling precise monitoring of financial and operational covenants. Receive early alerts for potential breaches, empowering borrowers to rectify issues promptly. 6. Stay organized with **Transaction Events**: track all contractual events conveniently in one place. Stay updated on upcoming borrower deliverables and effortlessly sync transaction events with your calendar for improved organization and efficiency. Use case **Background:** Our client, a leading asset manager based in the UK, is providing funding to an originator of consumer loans through an asset-based revolving facility. The company has traditionally struggled with the complexities of ABL transactions due to diverse collateral types, fluctuating asset values, and intricate covenant conditions. **Challenge**: - Data Management: Extensive datasets involving diverse collateral types (like machinery, inventory, and accounts receivable) were cumbersome to manage and analyze, leading to potential inaccuracies in asset tracking. - Decision Delays: Keeping track of the thousands of loans and ensuring a correct determination of the eligible criteria, as well as the correct calculation of excess concentrations and advance rates through Excel was delaying the ability of the asset manager to provide funding on time. - Regulatory Compliance: Maintaining compliance with strict ABL regulatory and legal frameworks was resource-intensive. - Operational Costs: High operational costs were incurred due to the manual and fragmented approach to facility and collateral management. **Solution**: The company decided to implement Cardo AI’s ABL solution to overcome these challenges. Our software has enabled them to digitalize the entire credit agreement, providing a centralized platform to manage the facility details and collateral data. The deployment focused on several key features of the technology: - Facility Module: Customizing facilities to cater to diverse financing structures and automatically calculating expected payments and interest, which streamlined financial management. - Workflow Manager and Borrowing Base: These tools facilitated smoother and more accurate funding processes, enhancing the company’s ability to manage cash flows and maintain lender confidence. - Borrowers Monitoring and Covenants Tracking: By standardizing financial data and automating compliance checks, the company could now proactively manage borrower behavior and covenant compliance, reducing the risk of breaches. - Data Integration: Leveraging advanced monitoring capabilities and data intelligence tools provided by Cardo AI’s solution enabled the company to gain crucial insights into borrower and collateral performance. See how much faster your next funding decision could be. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Beyond static metrics: transition matrix and vintage default analysis](https://cardoai.com/whats_new_in_product/beyond-static-metrics-transition-matrix-and-vintage-default-analysis/) **Published:** May 2, 2024 **Author:** RobertaCardo26 **Excerpt:** Asset-based finance thrives on data-driven decisions. Spotting trends requires dynamic, easily configurable metrics that can summarize vast datasets and streamline operations. At Cardo AI, we understand these challenges. That’s why we’ve pioneered advancements in transition matrix and vintage default analysis. **Content:** – Contents [Introduction](#introduction) [What’s in it for you](#first-par) [Why does it matter](#second-par) [How does it work](#third-par) [Use case](#fifth-par) Introduction Asset-based finance thrives on data-driven decisions. Spotting trends requires dynamic, easily configurable metrics that can summarize vast datasets and streamline operations. At Cardo AI, we understand these challenges. That’s why we’ve pioneered advancements in transition matrix and vintage default analysis. These powerful metrics give you the insights you need to make smart decisions, fast. What’s in it for you - **Predict and manage future loan performance**: Imagine you have different loan pools performing well today. Predicting which pools might have trouble down the line allows you to proactively manage risk and avoid potential losses. - **Optimize your portfolio for maximum return**: By identifying high and low-performing loan groups, you can strategically allocate resources. This targeted approach helps maximize returns. - **Make confident investment decisions based on clear data**: With over 30 filter options, you can create tailored metrics that align precisely with your business needs, ensuring that your measurements are both relevant and impactful. - **Free up time and resources for strategic growth**: Basic analysis can be time-consuming and prone to errors. Generating reports rapidly gives you a comprehensive picture with less effort and helps keep both internal and external stakeholders continuously informed and engaged. Why does it matter Asset based finance relies on accurate metrics for everything from loan approvals to portfolio management and risk assessment. But traditional tools can struggle. Charts and basic metrics often miss the connections between different factors, leading to misinterpretations and wasted time. By integrating an advanced transition matrix and vintage default analysis, Cardo AI equips market participants with robust tools to navigate the complexities of structured finance. Our solutions let you track performance trends over time, and identify optimal performing groups based on origination dates, pool additions, and transaction dates. How does it work Our advanced analytics tools, the Transition/Behavioral Matrix and Vintage Default Analysis, offer a deep dive into the dynamics of your financial assets. Here’s a closer look at how each tool functions: **Transition/Behavioral matrix:** - **Unveil asset performance dynamics over time**: This feature offers deep visibility into how your assets perform over time. By analyzing how portions of your portfolio transition between states (performing, delinquent, default), you can proactively anticipate risk exposure and adjust strategies. - **Actionable insights from historical data**: Leveraging historical data, this feature provides insights into asset behavior, enabling informed decision-making and risk mitigation. - **Flexible transition matrix**: The powerful transition matrix empowers data-driven decisions. Customize it to the most relevant dimension (e.g., current balance, number of assets) and transition type (e.g., performance status, amortization status). (Table 1) - **Behavioral matrix at a glance**: For a quick summary, the behavioral matrix option shows which parts of your portfolio have improved, remained stable, or worsened. (Table 2) ![Transition Matrix functionality preview](https://cardoai.com/wp-content/uploads/2024/05/Transition_matrix-1024x134.png) *Image 1: Transition Matrix functionality preview* ![Behavioral Matrix functionality preview](https://cardoai.com/wp-content/uploads/2024/05/Behavioural-matrix-1024x131.png) *Image 2: Behavioral Matrix functionality preview* **Vintage Default analysis:** This tool goes beyond static assessments to capture the evolving nature of credit risk. Imagine your loan portfolio as a forest, with different trees planted at different times (origination dates). Vintage Default Analysis is like taking time-lapse photos of the forest. By grouping loans based on when they were originated (planted), when they were added to a specific group (pool addition date), and when they were bought or sold (transaction date), we can see how the risk of default changes over time for each group (Table 3). This lets you see: - Which clusters of loans are performing better or worse? - How the risk of default changes for each cluster over time? Vintage Default Analysis gives you a much clearer picture of risk across your portfolio, with up to 30 different ways to slice and dice the data to find the information you need. ![Vintage default](https://cardoai.com/wp-content/uploads/2024/05/Vintage_Default-1024x298.png) *Image 3: Vintage default* Use Case **Challenge**: Our client is a leading asset manager, struggling with a large and diverse portfolio of asset-backed securities. Their traditional methods for risk assessment were time-consuming and offered limited insights into asset transitions and historical performance. This made it difficult to assess risk and optimize portfolio performance. **Solution:** They implemented Cardo AI’s advanced analytics solution, including Vintage Default Analysis. This powerful tool provided a deeper understanding of asset behavior by analyzing historical data and grouping assets based on origination dates, pool additions, and transaction dates. **Results**: - Reduced time spent on metrics: Cardo AI streamlined our Client’s process of updating and creating performance metrics. - Deeper portfolio analysis: With Cardo AI’s flexible filters, our Client could analyze their portfolio from multiple angles, uncovering hidden risk patterns and performance dynamics. - Improved risk-adjusted returns: By proactively identifying potential issues and optimizing asset allocation, our Client achieved higher risk-adjusted returns for their portfolio. - Enhanced operational efficiency: Streamlined workflows and reduced manual labor led to significant operational efficiency gains. See how Cardo AI’s transition matrix and vintage default analysis give you the portfolio intelligence to spot risk before it hits your portfolio. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Multicurrency: managing ABF transactions across global markets](https://cardoai.com/whats_new_in_product/multicurrency-managing-abf-transactions-across-global-markets/) **Published:** May 15, 2024 **Author:** RobertaCardo26 **Excerpt:** No more limits on where you invest globally. With our new Multicurrency feature, you can invest in every currency, and with a click of a button, integrate all your complex ABF transactions into the base currency of your fund. Implementing robust systems, processes, and risk management strategies is **Content:** Contents [Introduction](#introduction) [What’s in it for you?](#first-par) [Why does it matter?](#second-par) [How does it work?](#third-par) [Use Case](#fourth-par) Introduction No more limits on where you invest globally. With our new Multicurrency feature, you can invest in every currency, and with a click of a button, integrate all your complex ABF transactions into the base currency of your fund. Implementing robust systems, processes, and risk management strategies is essential to mitigate the challenges associated with multicurrency transactions and ensure smooth operations in international markets. Done so free of errors caused by the lack of visibility, and from a single point of view. What’s in it for you? Our new Multicurrency feature supports investors manage opportunities beyond borders and diversify their portfolios across various products and regions: - Increase your Fund’s ROI considerably through centralized management of FX fluctuations and derivatives costs. - Save up to 30% of time with streamlined currency conversion processes. - Reduce risk with real-time insights into currency fluctuations, strengthening risk management strategies. - Improve accuracy in transaction management by automating currency calculations to eliminate errors. Why does it matter? Managing Asset-Based Finance transactions in multi-currency can be complicated for several reasons: - Exchange rates fluctuate constantly in the international currency markets, impacting the value of outstanding balances, interest payments, and cash flows, making it challenging to accurately predict financial obligations. - Currency markets are exposed to risk, where fluctuations in exchange rates can result in financial losses or gains. - Accurate accounting is required for currency conversions including translation of financial statements into a single reporting currency and compliance with relevant accounting standards. - Operational challenges, including managing multiple bank accounts, reconciling accounts in different currencies, and processing foreign currency transactions. - Compliance and regulatory framework can vary depending on specific currency jurisdiction. How does it work? Our Multicurrency feature integrates seamlessly with your existing systems to provide a streamlined approach to managing globally diversified portfolios: - **Automated currency conversion**: Automatically converts all transactions to the base currency of your fund at the most current exchange rates, ensuring that financial reporting is consistent and compliant. - **Centralized currency management**: Manages all currency transactions from a single platform, allowing for centralized control and visibility, which reduces the time and effort needed to manage multiple accounts in different currencies. - **Risk Management tools**: Incorporates real-time monitoring of foreign exchange rates and automated alerts. This enables proactive management of currency exposure and minimizes potential losses due to rate fluctuations. Use Case A Credit Fund that manages a portfolio of direct lending transactions needs to manage and monitor PIK interest on its portfolio of loans, ensuring that the interest is properly tracked and reported over time. Our PIK loans solution supports the fund with: **PIK Interest calculation:** The solution automatically calculates the accrued PIK interest and ensures the accrual is properly compounded. **Accrual monitoring**: The user can view real-time accrual data, including interest added to the loan balance for each calculation period (e.g., monthly, quarterly, annually), and monitor the increased exposure due to capitalization. **Accurate payment processing:** The solution updates the interest instalment status based on the PIK component (full PIK, PIK-cash mix, full cash) generating the payment notice and sending the correct information to a payment processing service (where available). **Report generation**: The user can generate detailed reports outlining the PIK interest calculations, including the starting principal, interest rate, accrual periods, and the total interest accumulated. **Audit and compliance checks:** The solution tracks historical accruals and maintains a log of all calculations, making it easier for the user to audit the interest calculations for compliance with financial regulations or loan agreement terms. **Post-calculation adjustments:** If the loan terms change (e.g., interest rate adjustment or early repayment), the user can input the changes into the tool, automatically adjusting the PIK interest accrual calculations. See how Cardo AI lets you manage globally diversified ABF portfolios from a single platform, in any currency. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Trade faith with fact: Transaction Manager](https://cardoai.com/whats_new_in_product/trade-faith-with-fact-transaction-manager/) **Published:** May 30, 2024 **Author:** RobertaCardo26 **Excerpt:** Asset-based transactions require complex calculations to determine the borrowing base, which affects the amount that can be drawn from existing facilities. In a standard scenario, a borrower must adhere to various asset-level eligibility criteria, enforce concentration limits (often around 50 for a typical US consumer loan transaction) on an aggregated level, and dynamically adjust advance rates based on asset characteristics. **Content:** Contents [Introduction](#introduction) [What’s in it for you](#first-par) [Why does it matter](#second-par) [How does it work](#third-par) [Use case](#seventh-par) Introduction Asset-based transactions require complex calculations to determine the borrowing base, which affects the amount that can be drawn from existing facilities. In a standard scenario, a borrower must adhere to various asset-level eligibility criteria, enforce concentration limits (often around 50 for a typical US consumer loan transaction) on an aggregated level, and dynamically adjust advance rates based on asset characteristics. This culminates in establishing a borrowing base from which drawdown requests can be issued. This process might pose significant challenges given the complexity of the calculation needed and the amount of data to be processed, and these difficulties are often compounded by a lack of transparency and the potential for calculation errors Cardo AI’s enhanced Transaction Manager addresses these challenges by enabling a customized workflow to manage any drawdown request across various asset classes, in accordance with the provisions of the credit agreement. What’s in it for you - Set eligibility criteria to identify ineligible assets. - Configure concentration limits ranging from simple threshold to combined control across multiple dimensions. - Define asset-level advance rates based on performance status and FICO scores. - Configure the borrowing base at the tranche/facility level. - Centralize and streamline communication between borrowers and lenders on a unified platform. Why does it matter In the funding process of an asset-based transaction, it can be particularly challenging for lenders to retrieve data on the collateral pool provided by the borrower, verifying its integrity, review all calculations, and ultimately approving or rejecting the drawdown request. This process can be time-consuming, incurring additional costs and delays, especially when extensive data manipulation is required for accurate calculations. With Cardo AI’s Transaction Manager, borrowers and lenders can rely on a common calculation engine that ensures transparency at every step, eliminating the need to re-perform calculations done by other parties, and reducing the risk of lengthy discussions about the interpretation of contractual provisions. Setting up the entire process takes only a few hours, even for the most complex use cases. Thereafter, the process can be executed in seconds at the user’s discretion, facilitating monthly, weekly, or even daily borrowing base calculations. In summary, by leveraging our tool, users can achieve: - **Speed**: Reduce processing time from days to seconds. - **Efficiency**: Avoid redundant tasks and controls. - **Accuracy**: Achieve bullet-proof results with full control over each datapoint. - **Single Source of Truth**: Ensure consistency and reliability in all calculations. How does it work Our tool offers maximum flexibility to customize calculations for eligibility criteria and concentration limits at both the asset and aggregate levels, ensuring alignment with contractual provisions. It also allows for the customization of all steps needed to compute the borrowing base and manage drawdown requests, tailored to the specifics of each transaction. For example, users can set eligibility criteria to identify ineligible assets, compute concentration limits, and then determine the borrowing base. Users have the flexibility to define borrowing base calculations for each tranche/facility and determine the calculation of each component that impacts the borrowing base, such as advance rates. *![Asset Level Checks functionality under Eligibility Criteria](https://cardoai.com/wp-content/uploads/2026/03/Trade-Faith-with-Fact-1-1024x506.png)* *Image 1: Asset Level Checks functionality under Eligibility Criteria* ![Borrowing Base Calculation functionality under Eligibility Criteria](https://cardoai.com/wp-content/uploads/2026/03/Trade-Faith-with-Fact-2-1024x513.png) *Image 2: Borrowing Base Calculation functionality under Eligibility Criteria* Finally, borrowers can define the drawdown request to be submitted to investors. Investors, in turn, can review all the calculations and make informed decisions on whether to approve or reject the requests. ![View of Draw Request Submission](https://cardoai.com/wp-content/uploads/2026/03/Trade-Faith-with-Fact-3-1024x513.png) *Image 3: View of Draw Request Submission* Use Case Our client, a global investment firm, acts as a lender, extending funding to a consumer loan originator through an asset-based revolving facility. Prior to implementing Cardo AI’s solution, the identification of eligible assets, as well as the computation of excess concentrations and advance rates, relied solely on Excel spreadsheets. This manual approach was prone to errors and posed challenges in validating the accuracy of borrower-provided data. As a result, accurately determining the borrowing base to establish appropriate funding levels often encountered delays. With Cardo AI’s Transaction Manager tool, our client has fully automated these critical calculations, effectively eliminating potential errors. This automation has significantly enhanced the efficiency and speed of the approval process for new drawdowns, thereby strengthening our client’s relationship with the originator and ensuring smoother funding operations. See how Cardo AI turns complex borrowing base calculations into a matter of seconds [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Stratification: the power of layered risk analysis](https://cardoai.com/whats_new_in_product/stratification-the-power-of-layered-risk-analysis/) **Published:** June 5, 2024 **Author:** RobertaCardo26 **Excerpt:** Many originators currently take days to understand the risks associated with your transactions. This is due to their reliance on manual processes for analyzing asset data. These manual processes involve pulling information from internal management systems, classifying each asset or transaction individually, and then running calculations to assess risk. This time-consuming and tedious approach lim **Content:** Contents [Introduction](#introduction) [What’s in it for you?](#second-par) [Why does it matter?](#third-par) [How does it work?](#fourth-par) [Use Case 1](#fifth-par) [Use Case 2](#sixth-par) Introduction Many originators currently take days to understand the risks associated with your transactions. This is due to their reliance on manual processes for analyzing asset data. These manual processes involve pulling information from internal management systems, classifying each asset or transaction individually, and then running calculations to assess risk. This time-consuming and tedious approach limits investors’ ability to make quick and informed decisions. Our stratification tool grants swift access to risk intelligence, empowering faster and more informed investment decisions. What’s in it for you? - **Interactive reporting**: Enhance reporting automation through the development of custom reports leveraging granular stratification tables. - **Transparency**: Reinforce confidence in reported data through our unbiased, comprehensive analysis and data processing expertise. - **Risk management**: Empower Investors with actionable transaction intelligence, enhanced security protocols, and leverage risk management technology. - **Smart stratification**: Granular time series analysis that captures historical data, enabling performance evaluation at both the transaction and asset levels. Utilizing the stratification tool, one can compare transactions and their performance with other transactions, identify trends, and concentrate exposure on less risky and more profitable opportunities. Additionally, one can optimize reporting workflows through comprehensive automation. Centralized data resources, calculations, and reports ensure seamless team collaboration and consistent access to critical resources for all stakeholders and business partners. Furthermore, leverage Cardo AI technology to bolster security by facilitating report sharing through a secure platform, eliminating reliance on potentially vulnerable email attachments. Why does it matter? - **Effortless data preparation:** Our built-in reporting tools streamline data preparation workflows, enabling you to focus on analysis and decision-making. - **Granular risk assessment:** Effortlessly generate multi-risk layer stratification reports using any asset data field. Gain a deep understanding of transaction-level risk profiles. - **Improved investment strategies** – Identify assets that align with your investment objectives through historical portfolio analysis. Compare performance and trends across different transactions to make adjustments and optimize portfolio performance. How does it work? - **Intuitive interface:** Cardo AI’s stratification tool features a unified user interface, enabling users to seamlessly interact with data. Construct custom, multi-risk layer stratifications tables, as well as time series analyses with ease. - **Comprehensive data analysis:** Effortlessly collect and standardize asset-level data across diverse dimensions, including geography, sector, borrower rating, maturity, and other relevant metrics. Aggregate this data by dynamically constructing stratifications that analyze single or multiple transactions across various risk factors. - **Risk minimization strategies:** Leverage the insights derived from multi-risk layer stratification to identify and mitigate potential risks associated with your transactions. Cardo AI empowers you to optimize transaction management by making informed decisions based on comprehensive risk profiles. Use Case 1 **Challenge**: Our client is a digital lending platform originating SME loans, financing its activity with multiple asset-based transactions (warehouse financing, ABS and credit line) for which it has to report to investors and lenders. Prior to implementing Cardo AI’s platform, the originator required up to three working days to process each transaction’s loan tape and generate reports for each transaction and investors. This process involved extracting relevant data from its own loan management system, classifying each loan based on different contractual definitions, checks limits and triggers, slice & dice the portfolio to calculate concentrations and statistics. **Solution**: Our platform transformed its operations by delivering real-time, comprehensive insights. The originator can now stratify and conduct in-depth risk analyses across various dimensions (e.g., sector, performance status, maturity, outstanding balance, days past due) at any time, leveraging on transaction specific metrics, ready to be used KPIs, and the most current data available.![](https://cardoai.com/wp-content/uploads/2024/06/stratification-1024x410.png) **Results**: - Time efficiency: Drastically reduced the time required for data processing and risk analysis from days to hours. - Error reduction: Minimized data errors, leading to more reliable and effective decision-making. - Improved investor communication: Streamlined the reporting process, allowing for timely and accurate updates to investors. Use Case 2 **Challenge**: A global investment firm, which has invested in over 15 transactions in the US primarily focused on Consumer Loans and Auto Loans, struggled with operational inefficiencies in analyzing data across multiple portfolios. Internal report generation often took 4-5 working days and frequently contained data errors, hindering effective decision-making. **Solution**: With our advanced data stratification capabilities, the investor can now aggregate and stratify data as needed, modify the reporting at will and thus allowing for a more detailed and digestible analysis of specific fields, such as increase in days past due, dropping borrower ratings, indicate interest rate trends, or highlighting the increase in charge-offs. **Results**: - Portfolio structuring: Enabled better portfolio structuring by focusing on the most profitable and least risky sectors without compromising returns. - Insightful analysis: Enhanced the ability to dive deeper into data relationships, providing clearer insights and better investment strategies through real-time, in-depth analysis. - Operational efficiency: Significantly reduced the time required to generate accurate reports. See how Cardo AI turns days of manual risk analysis into real-time stratification across any dimension. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Distressed assets: sell smarter, collect more, invest confidently](https://cardoai.com/whats_new_in_product/distressed-assets-sell-smarter-collect-more-invest-confidently/) **Published:** June 13, 2024 **Author:** RobertaCardo26 **Excerpt:** Since the financial crisis of 2007-2008, distressed assets have been one of the most attractive and highly remunerative asset classes. However, for sellers, servicers and investors navigating this asset class presents unique challenges due to its inherent complexity and unreliable data. Relying solely on aggregated data treats distressed assets as isolated entities, overlooking the interconnected factors influe **Content:** Contents [Introduction](#introduction) [What’s in it for you?](#first-par) [Why does it matter?](#second-par) [Our platform: How does it work?](#third-par) [Use Case: Seller](#fourth-par) [Use Case: Servicer](#fifth-par) [Use Case: Investor](#sixth-par) Introduction Since the financial crisis of 2007-2008, distressed assets have been one of the most attractive and highly remunerative asset classes. However, for sellers, servicers and investors navigating this asset class presents unique challenges due to its inherent complexity and unreliable data. Relying solely on aggregated data treats distressed assets as isolated entities, overlooking the interconnected factors influencing their value and risk profile. What’s in it for you? Understanding the interdependencies of distressed assets at a granular level is crucial for accurate risk management and decision-making. Here are the benefits for different stakeholders: - **Sellers:** Attract and nurture investors appetite, make informed decisions about which assets to sell, and maximize the price. - **Servicers:** Access real-time data and gain deeper insights for evaluating business plans. Implement effective collection strategies by uncovering trends leveraging on traditional and alternative data. - **Investors:** Bid with confidence and maximize return, benchmark collections, keep the performance under control and act in time to improve results. ![Example of business plan evaluation](https://cardoai.com/wp-content/uploads/2024/01/business-plan-realized-vs-expected-e1732094459657-1024x253.png)Example of business plan evaluation*Image 1 – Example of business plan evaluation (Realized vs Expected)* Why does it matter? - **Data disarray:** The landscape surrounding distressed assets is not straightforward. It includes incomplete records, inconsistent financial statements, intricate legal documentation and missing or inaccurate information about the asset’s condition. - **Hidden complexities:** Distressed assets often come with complex information making it difficult for sellers and servicers to accurately value the asset, understand the risks involved, and present a clear picture to potential investors. - **Investor information gap:** Potential investors require a clear understanding of risk and reward before committing capital. Lack of transparency in pricing, risk assessment, and future performance projections can lead to investor hesitation. - **Price dislocation**: Purchase prices are based on expected return. When there is limited or unreliable information, investors demand larger discounts to compensate for the potential increase in risk that could materialize in the future. Our platform: How does it work? Our platform leverages advanced data collection and analysis tools to: - **Aggregate detailed loan-level data** across various dimensions like collateral location, enforcement borrowers’ status, and recovery activities. - **Ensure reliability** of data via multiple controls and data validation and enrichment. - **Provide insights at both granular and portfolio levels** to support informed decision-making. Standardized data allows for faster analysis than traditional spreadsheets. - **Enable the visualization of interconnected data points** such as real estate associated with credit lines tied to a single debtor. - **Set customized metrics and analytics** for deeper understanding of your recovery process and boosted collections – incorporated metrics like Collection over GBV, CCR, and NPV. ![Example of custom metrics – CCR (Cumulative Collection Ratio)](https://cardoai.com/wp-content/uploads/2024/01/example-of-custome-metrics-CCR-1024x387.png)Example of custom metrics – CCR (Cumulative Collection Ratio)*Image 2: Example of custom metrics – CCR (Cumulative Collection Ratio)* Use case: *Seller* **Challenge**: Our client is a bank that manages a growing portfolio of non-performing loans across various industries. The manual analysis of data posed challenges in identifying optimal assets for sale and attracting qualified buyers. **Results:** Through the implementation of Cardo AI’s technology, they significantly improved their distressed loan sales process. They were able to: - Sell assets faster and at a fair market price by establishing trust with investors through transparency - Reduce holding costs associated with distressed assets - Redirect resources towards core lending activities Use case: *Servicer* **Challenge**: Our client is a servicer managing diverse portfolios of distressed assets including defaulted mortgages and commercial loans. Manually sifting through data to understand the full picture of each asset was time-consuming and prone to errors. **Results**: By leveraging Cardo AI’s technology, they significantly improved their distressed asset management capabilities. They can now: - Make data-driven decisions in real-time - Be more effective in the collection process - Evaluate business plans with greater confidence - Maximize recovery rates Use case: *Investor* **Challenge**: Our client, a leading Asset Manager investing in distressed exposures, faced challenges in accurately assessing risk and potential returns when evaluating individual assets and benchmarking the performance of different special servicers. The manual process of gathering and analyzing data hindered their ability to quickly identify investment opportunities. **Results:** By leveraging Cardo AI’s technology, the Asset Manager has gained a significant edge in the distressed asset market. They are now able to: - Confidently navigate risk and make well-informed investment decisions. - Reduce time spent on due diligence and streamline their investment process. - Monitor the performance of the servicers See how Cardo AI validates your data, tracks recovery in real time, and gives every stakeholder the granular insight they need to price accurately. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Simplify collateral management with technology](https://cardoai.com/whats_new_in_product/simplify-collateral-management-with-technology/) **Published:** November 6, 2024 **Author:** GiadaCardo26 **Excerpt:** Our technology simplifies collateral management with rapid onboarding and efficient eligibility checks for enhanced risk management. **Content:** Contents [Introduction](#introduction) [The challenges of traditional collateral management](#first-par) [Why in-house technology often falls short](#second-par) [The benefits of intelligent automation in asset management](#third-par) Introduction In asset-based finance, the need for highly customized solutions has transformed the way collateral is managed. Unlike the standardized, predictable structures of public financing, private financing requires frequent adjustments to match the specific needs of each borrower. These adjustments extend to collateral terms, risk profiles, and compliance, all of which are subject to change with each new facility activity. Traditionally, however, managing these dynamic requirements has been slowed down by manual work and legacy technology. In this environment, asset managers are constantly racing to keep pace with weekly changes in collateral – onboarding new assets, running eligibility checks, and updating valuations. When handled through manual processes or rigid in-house systems, these tasks become bottlenecks. By implementing specialized software, firms can overcome these inefficiencies, allowing teams to move away from repetitive manual tasks and focus on strategic decision-making. The challenges of traditional collateral management - **Manual onboarding**: Each new asset must be manually input, analyzed, and validated, a process that can take days or even weeks, delaying the overall workflow and slowing response times. - **Time-consuming eligibility processes**: Ensuring assets meet the necessary criteria can become a bottleneck, slowing down deals and decision-making. - **Increased risk of errors**: As processes become more complicated, the pressure on teams mounts, potentially leading to human error. Why in-house technology often falls short Many asset managers attempt to handle these processes with in-house technology, which is often not equipped to adapt quickly to market shifts. Updating in-house systems to handle new types of collateral or integrate faster workflows can be costly and time-consuming, leaving internal teams struggling to keep pace with rising demand. This creates a disconnect, where the need for speed and flexibility exceeds the system’s capabilities, resulting in missed opportunities and a slowdown in growth. The benefits of intelligent automation in asset management As asset-based finance grows in complexity and volume, relying on intelligent automation will increasingly distinguish high-performing asset managers from those constrained by traditional systems. As a top software provider in the space, Cardo AI technology offers critical advantages to asset managers for better risk management, collateral optimization, and greater efficiency: 1. **Rapid collateral onboarding**: Automated workflows streamline data intake, analysis, and validation. What used to take days or weeks now happens in hours, allowing managers to onboard new assets confidently and rapidly. 2. **Efficient eligibility checks**: AI algorithms can cross-check assets against compliance and eligibility requirements automatically. This reduces errors, speeds up decision-making, and ensures a consistent standard is met without constant manual oversight. 3. **Empowered teams**: With technology handling repetitive tasks, teams can redirect their expertise towards initiatives that drive growth and client satisfaction. See how Cardo AI transforms collateral management from a manual bottleneck into a fully automated workflow advantage. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Unlocking the power of BNPL collateral with scalable technology](https://cardoai.com/whats_new_in_product/unlocking-the-power-of-bnpl-collateral-with-scalable-technology/) **Published:** March 18, 2025 **Author:** RobertaCardo26 **Excerpt:** The Buy Now, Pay Later (BNPL) market has skyrocketed, 12x growth from 2019 to 2021 (CFPB). Initially perceived as a disruptive alternative to credit cards, BNPL has now evolved into a significant financial asset class. With increasing adoption and transaction volumes, **Content:** Contents [Introduction](#introduction) [Key attributes of BNPL receivables as collateral](#first-par) [Challenges and risks of BNPL collateral](#second-par) [How Cardo AI unlocks the potential of BNPL collateral](#third-par) Introduction The Buy Now, Pay Later (BNPL) market has skyrocketed, 12x growth from 2019 to 2021 ([CFPB](https://files.consumerfinance.gov/f/documents/cfpb_buy-now-pay-later-market-trends-consumer-impacts_report_2022-09.pdf)). Initially perceived as a disruptive alternative to credit cards, BNPL has now evolved into a significant financial asset class. With increasing adoption and transaction volumes, financial institutions and investors are exploring BNPL receivables as collateral, a move that could unlock new funding opportunities and enhance liquidity. As the market matures, BNPL-backed securitization and asset-based financing present a compelling opportunity for lenders, fintech companies, and institutional investors. Key attributes of BNPL receivables as collateral BNPL receivables differ from traditional credit assets in several key ways, offering a unique and dynamic collateral base. Short-term and high turnover are defining characteristics, with BNPL loans typically having repayment cycles ranging from two weeks to six months, allowing for quick portfolio replenishment. This is complemented by **granular and diversified risk**, as BNPL receivables are spread across millions of small transactions, significantly reducing exposure to any single borrower. Additionally, **strong payment performance data** is leveraged by BNPL providers using AI-driven underwriting models and real-time transaction data, improving repayment predictability. The **embedded merchant agreements** between BNPL providers and retailers further strengthen the collateral base by creating a dual-layered revenue stream through both lending fees and merchant commissions. This structure is transforming the market by providing investors with a collateral base that’s both dynamic and resilient. With hundreds of distinct merchant categories and a broad set of merchants in typical BNPL portfolios, the resulting diversification rivals that of traditional consumer credit assets. BNPL pools also benefit from **high transaction velocity**, with hundreds of thousands of daily cash flow events—far exceeding the transaction volume of traditional credit card ABS (~10,000–20,000 per day). This is coupled with **predictable repayment cycles**, often tied to payroll schedules, adding further stability to the asset. Moreover, BNPL’s diversified exposure to **hundreds of merchant categories** and various **regions and consumer segments** reduces sector concentration risk. The rich data insights generated from these transactions provide granular tracking of consumer behavior, merchant performance, and spending patterns, offering investors early signals for trends in other credit asset classes (e.g., subprime auto loans) before they impact broader markets. These combined attributes make BNPL receivables an increasingly attractive and transformational collateral class in the financial market. Challenges and risks of BNPL collateral While BNPL-backed financing offers significant potential, it also comes with unique risks that lenders and investors must consider: 1. **Credit risk & default rates**: Unlike traditional credit models, BNPL often relies on soft credit checks and alternative data. While this expands access to credit, it also introduces higher default risk, particularly during economic downturns. 2. **Regulatory uncertainty**: Governments worldwide are tightening regulations around BNPL lending to protect consumers from over-indebtedness. New policies could impact underwriting models, risk management, and the overall profitability of BNPL-backed financing. 3. **Operational and data challenges**: The fragmented nature of BNPL transactions across multiple retailers, platforms, and geographies creates challenges in standardizing asset pools, tracking cash flows, and structuring securitizations. Investors need transparent performance data to assess risk properly. How Cardo AI unlocks the potential of BNPL collateral The BNPL market is on a strong growth trajectory, with a projected compounded annual growth rate (CAGR) at >19% through 2028 ([GlobalData](https://www.globaldata.com/store/report/buy-now-pay-later-market-analysis/)), and global transaction volumes expected to thrive in the ABF market over the next decade. At Cardo AI, we leverage our cloud-based, modular platform, driven by AI-powered analytics, to unlock the full potential of BNPL collateral, maximizing returns for both originators and investors. Cardo AI differentiates itself from other fintech platforms through its ability to provide dynamic, real-time processing at scale. By integrating full historical data, our platform enables longitudinal analysis, offering valuable insights into portfolio performance over time. This allows us to support a wide range of clients—whether they’re originators seeking efficient management or investors looking for data-driven strategies—while also incorporating robust portfolio management capabilities. **Seamless portfolio onboarding:** Our platform instantly processes millions of loans, standardizing data across hundreds of merchants. Real-time eligibility checks and concentration monitoring ensure compliance with investment rules, making onboarding a smooth and efficient process. **Automated workflows & real-time monitoring:** With [Transaction Manager](https://cardoai.com/trade-faith-with-fact-transaction-manager/), we automate asset purchases, drawdowns, and portfolio updates. Stratification tools break down data by merchant, region, and risk profile, offering both real-time and historical insights that help adjust strategies to market conditions. **Scalability & speed:** Cardo AI is built for speed and flexibility, processing over 5 million cash flow events monthly. Powered by Python, .NET, and microservices, our platform delivers the performance and reliability necessary for high-volume environments, ensuring scalability and quick decision-making. **Advanced risk & cash flow analytics:** Our Limits and [Waterfall ](https://cardoai.com/payment-waterfall-modeling/)features provide deep insights into portfolio performance, allowing users to spot breaches and adjust strategies in real-time. Additionally, cash flow simulations offer forward-looking predictions of portfolio returns, empowering investors to make data-driven decisions. Cardo AI enables the full potential of BNPL collateral through a seamless integration of advanced technology, real-time data processing, and sophisticated risk management capabilities. This approach empowers both originators and investors to navigate the BNPL market, optimizing portfolio strategies with precision. See how Cardo AI can transform your BNPL receivables into a high-performing, fully managed asset. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Introducing Document Reader for credit agreements](https://cardoai.com/whats_new_in_product/introducing-document-reader-for-credit-agreements/) **Published:** April 10, 2025 **Author:** GiadaCardo26 **Excerpt:** Cardo AI developed a deep scalable document analyzer, using a system of language models and software built starting from the ABS data ingestion workflow. This trusted framework enables users to easily onboard deal documents, financial statements, appraisal reports, and more. **Content:** Contents [Introduction](#introduction) [From document to automation](#first-par) [What makes our Document Reader stand out in the market?](#second-par) [What it does](#fourth-par) [How it works](#fifth-par) Onboarding a new transaction in asset-based finance is often a time-consuming and complex task. You’re not just dealing with multiple data formats and legal definitions — you’re navigating input from advisors, managing different asset classes, and interpreting hundreds of pages of dense legal language. Publicly available AI tools aren’t a viable solution either. They often fail to meet data privacy requirements and are prone to hallucinations. At Cardo AI, we’ve built a better way. Our **Document Reader** tool simplifies the onboarding process by extracting key information directly from credit agreement PDF, .doc, and .docx files — helping you move faster, with more accuracy and confidence. The models powering this tool are either trained and hosted by Cardo AI on AWS infrastructure or are part of AWS cloud services (like Bedrock), ensuring that your credit agreements and financial data remain confidential and protected at every step. From document to automation: why we built the Document Reader - **Onboarding takes too long**: Understanding and configuring complex deals can take days just to map the relevant events that need to be monitored. - **Junior analyst ramp-up is slow**: It takes time for new team members to get up to speed on interpreting legal language and identifying key terms. - **Contracts are complex and interconnected**: Many critical terms rely on layered, nested definitions that aren’t easily extracted. - **Manual review is inefficient**: Searching across pages to retrieve a single data point is frustrating and error-prone. Our tool solves all of that. It helps teams **save up to 90% of the time** typically spent onboarding contracts. What makes our Document Reader stand out in the market? Many teams try to use general-purpose AI tools like the ones from OpenAI (or Anthropic, Google, Deepseek, Adobe…) for document analysis, but here are 4 things to take into consideration: - **Model specialization**: Publicly available models are general-purpose. Ours is tailored to extract structured finance information and is trained on real-world credit agreements. - **Understanding definitions**: Mainstream models don’t have explicit knowledge of loan agreement definitions. When they face a tough question, they “think harder” with the risk of going off-topic and ultimately hallucinating. Our model “thinks smarter”—by retrieving and using related definitions within the legal document and organizing them with business knowledge, just like a human analyst would. - **Fact-checking transparency**: Most publicly available AI interfaces are unable to reference the exact point in a contract where the answer comes. Our reader displays the contract excerpt used in the answer and lets you click to jump directly to the source page in the document. - **Hallucinations: Besides enabling our users to inspect the specific source text, we systematically benchmarked multiple LLMs and LLM providers to select the models with the lowest hallucination rates – those that would not provide answers unless supported by the source documents. - What it does We built a specific solution that combines SLMs (small language models) and LLMs to get the best performance for the entity extraction task in credit agreements. In a RAG (retrieval augmented generation) setting, we have trained a retrieval model on hundreds of legal documents. Given a user query, the retrieval model extracts the most relevant piece of information from the document. The pieces of text extracted by the retrieval model are then “read” by an LLM (or generator model), which uses the text as context to answer the user query. This approach allows us to: - Provide the most relevant context for the question, enabling the LLM to generate a precise answer - Reduce hallucinations by instructing the LLM to respond using only the pieces of text extracted. - When the LLM struggles to give a clear answer, it states so by relying on the information passed, pointing out both what is present and what is missing. Our Document Reader uses a custom-trained AI model to: - Answer any questions about your credit agreement (e.g. issue date, counterparties, committed amounts…) - Point to the exact clause used to answer - Understand and explain nested definitions - Guide you through follow-up questions - Extract key information (e.g. counterparties, amounts, dates) - Automatically highlight and dive deeper into contractual definitions - Support a more accurate and complete understanding, even for complex queries Our clients will also soon be able to train custom retrieval models based on the specific semantics of their documents by simply uploading a couple of them and letting the platform do the rest! How it works 1. Upload a credit agreement in PDF, doc\* or docx\* 2. Start document processing, to use the Cardo AI custom language model for document search. 3. Get the most relevant elements of the contract (i.e. summarize it in a termsheet) 4. Ask a question about the contract (i.e “What is the commitment amount?”): 5. Get an answer – plus a clickable link to the clause or definition used (\*clickable link available only for PDFs) 6. Explore follow-up suggestions, often based on definitions found in the initial answer. 7. Drill deeper by increasing the answer’s depth – great for tricky or layered questions. It’s not only about answering faster – it’s about answering better. 1. Ready to cut onboarding time by 90%? Try our Document Reader. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Data Profiling and AI: ensure the quality of your data](https://cardoai.com/whats_new_in_product/data-profiling-and-ai-ensure-the-quality-of-your-data-2/) **Published:** April 28, 2025 **Author:** GiadaCardo26 **Excerpt:** Working with financial data in Excel presents challenges such as reconciling transactions, analyzing loan portfolios, and verifying bank statements. The process involves connecting different datasets (e.g., loans with cash flows or borrowers), calculating key metrics **Content:** Contents [Introduction](#introduction) [What’s in it for you](#first-par) [Why does it matter](#second-par) Introduction Working with financial data in [Excel](https://cardoai.com/beyond-excel-risks-of-using-spreadsheets-to-manage-your-structured-finance-investments/) presents challenges such as reconciling transactions, analyzing loan portfolios, and verifying bank statements. The process involves connecting different datasets (e.g., loans with cash flows or borrowers), calculating key metrics (e.g., total outstanding balance), and visualizing data. Scaling this process to handle millions of transactions from multiple sources, each with its format and rules, significantly increases the complexity of ensuring accuracy and consistency. For investors and asset managers, making decisions hinges on having access to accurate, standardized, and consistent data. Standardized data enables investors to evaluate the risks associated with various structured finance investments, guiding their investment choices. Similarly, asset managers can improve their portfolio management by having a clear and complete picture of their investments. This helps them identify potential problems and opportunities. Our data onboarding approach at Cardo AI follows a three-step process: **1. Data ingestion:** - **Data retrieval:** Retrieving data from various sources (including FE applications, SFTP folders, S3 buckets, API connections) and in multiple formats (parquet, xlsx, csv, txt, etc.), such as loan databases and financial reporting systems. - **Data loading:** Loading the extracted data into a centralized data lakehouse for storage and processing. - **2. Data quality check:** - **Data cleansing:** Identifying and correcting data errors, inconsistencies, and missing values. - **Data standardization:** Ensuring that data is formatted and sanitized consistently across different sources with the support of a Data Quality AI agent. - **3. Data transformation:** - **Data modeling:** Creating a data model that defines the relationships between different data elements. - **Data transformation:** Converting raw data into a format that is suitable for analysis and reporting. - Integrating an advanced data profiling tool with a data quality AI agent throughout the onboarding process offers a comprehensive understanding of dataset structures without the need for manual inspection of large source files. This combination not only automates the analysis of data structures but also provides actionable insights into data quality checks necessary to ensure consistency and accuracy.​ What’s in it for you - **Stay ahead with real-time data insights:** We provide continuous input data profiling, offering insights into table and column statistics. This empowers you to make informed decisions for data quality checks and transformation logic, allowing you to verify your assumptions about the data at any given moment. By having access to up-to-date information, the AI agent identifies potential issues that you can quickly address before they impact downstream processes. - **Uncover data patterns with statistical analysis:** Whether dealing with amounts, quantities, or date ranges, we offer comprehensive statistics on data distribution. This includes: - *Numerical analysis*: using average, median, mode, standard deviation, and variance to understand data dispersion and potential anomalies. Using quartiles and percentiles to understand data distribution across different ranges. - *Outlier detection*: identifies extreme values that may indicate data entry errors or inconsistencies. Use statistical thresholds to detect values that deviate significantly from historical patterns. - *Pattern & format analysis*: validates expected formats (e.g., IBAN, ISIN, phone numbers, and email structures). Ensure consistent text-based inputs across multiple sources. - *Data drift monitoring*: detect shifts in data distribution over time, highlighting potential systemic changes or unexpected anomalies. - **Identify data anomalies with ease:** We help you pinpoint anomalies that might hinder data conversion. For example, if a column intended for numbers or dates contains unexpected values, the tool will highlight potential issues based on statistics like distinct values, character counts, and outliers. This eliminates the need for manual inspection of source files and provides a clear overview of data quality problems. Why does it matter - **Speed up onboarding for new asset classes, portfolios and vendors:** the Data Intelligence Engine enables teams to rapidly understand and validate data from new asset classes and counterparties. Analysts can now have in a bunch of minutes an understanding on the main logics of tapes for asset classes never inspected before. Decisions and analysis are based on verified, standardized outputs, reducing the risk of errors caused by poor data quality or incorrect assumptions. Because results are consistently structured, recurring analysis becomes faster and easier over time. - **Catch surprises early, at the most granular level, before they hit reports or investors:** By identifying hidden relationships, inconsistencies, and anomalies upfront, the engine helps prevent issues from propagating into downstream analytics, reporting, or investor communications. See how Cardo AI’s Data Intelligence Engine reconciles messy, multi-source data into clean, audit-ready insights in minutes, not days. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Speeding up due diligence for ABF investments](https://cardoai.com/whats_new_in_product/speeding-up-due-diligence-for-abf-investments/) **Published:** June 3, 2025 **Author:** RobertaCardo26 **Excerpt:** In today’s borrower-driven market, originators shuffle the cards, while investors fight for a seat at the table. Strong demand for high-performing assets and limited supply mean borrowers can choose their capital partners and speed up due diligence. **Content:** Contents [Introduction](#introduction) [What’s in it for you](#first-par) [How it works](#second-par) [Use case](#third-par) Introduction In today’s borrower-driven market, originators shuffle the cards, while investors fight for a seat at the table. Strong demand for high-performing assets and limited supply mean borrowers can choose their capital partners and speed up due diligence. This challenges investors to quickly assess new transactions, as the best opportunities go to those who act fast. In this environment, competition for **high-quality portfolios is fierce**, and **timing is everything**. Typically, in an asset-based finance deal, originators send files containing years of historical performance – often millions of rows in a proprietary format – and investors have just a few days to submit a non-binding offer. Yet in practice, these datasets are frequently inconsistent, poorly formatted, and lack standardized structure, turning what should be a strategic evaluation into a frustrating data-cleaning exercise. Slow, manual processes simply don’t cut it. If you can’t respond quickly with a credible, data-driven assessment, another investor will. This is where our platform gives you a real advantage. It automates data exploration, provides asset class-based mapping support, scales analysis across large loan tapes, and delivers investor-grade outputs within minutes, empowering you to act decisively in a market where the clock is always ticking. What’s in it for you Here’s how our platform gives you an edge at the pre-deal stage: **Processing speed: structured outputs in minutes** - **Excel**: Requires a long data exploration process – create pivot tables, plot distribution graphs, assess categories in a list, check minimum and maximum value, make sure all the rows have the same format (let alone different date settings) – manual mapping, data and formula validation, and row-by-row error checks. Complex files (>100k rows) cause slow load times and frequent crashes. - **Cardo AI**: Automatically ingests loan and cash flow-level CSV or Excel files with millions of records, and returns normalized, schema-mapped outputs in minutes. Outputs include: – Expected cash flow based on different amortization plans – Cumulative Default Rate (CDR), Conditional Prepayment Rate (CPR), and vintage curves – Key cohort-level performance breakdowns – Stratification tables by custom attributes and across different transaction (benchmarking) – Cash flow simulation ready to be used **Automated data validation and cleansing** - **Excel**: Requires manual filter logic to identify anomalies (e.g., empty cells, out-of-range values, format mismatches), which often introduces subjective errors. - **Cardo AI**: Performs automated data quality validation at ingestion using rulesets defined per asset class and data type (e.g., valid FICO ranges, loan amount consistency, duplicate borrower IDs). Issues are flagged in a consolidated data quality dashboard for traceable resolution. **Reconstructed historical metrics using cash flow data** - **Excel**: Rebuilding time-series metrics (e.g., monthly default or prepayment curves) requires extensive formula modeling and assumptions, with a high risk of logic errors. - **Cardo AI**: Leverages transactional cash flow histories (e.g., payment, delinquency, recovery events) to reconstruct dynamic performance indicators across time, including: – Time-based cohort tracking – Static pool and vintage loss analyses – Rolling CDR/CPR metrics, even when only partial snapshots exist **Pre-configured data templates and visualizations** - **Excel**: Visual and tabular analyses must be created from scratch, tested for consistency, and adapted for each asset class. - **Cardo AI**: Comes with out-of-the-box dashboards tailored to asset class (e.g., auto ABS, consumer loan pools, leasing). Templates include: – Strat tables (e.g., by LTV, DTI, income band, loan term) – Performance trend charts – KPI summaries for credit, amortization, and risk segmentation **Dynamic analytics without code or formulas** - **Excel:** Every new hypothesis or investor question requires building custom pivot tables, formulas, and error-prone calculations. - **Cardo AI**: Use Dynamic Views to construct bespoke data aggregations, pivot logic, and visualizations without writing a single line of code. Filter by date, geography, asset type, or originator – all within the UI. KPIs can be compared across different transactions, originators or datasets. How it works **Data ingestion Upload raw loan-level data in Excel or CSV format via UI or API. The platform automatically detects schema types and asset classes. **Data validation** Run predefined data quality rules: - Field completeness - Schema conformity - Duplicate detection - Business logic violations (e.g., future-dated defaults) **Data normalization & processing Normalize attributes (e.g., loan terms, credit metrics) and structure data into standardized models for aggregation and visualization. **Analytics & output delivery Instantly generate: - Strat tables (e.g., FICO, original term, loan purpose) - Performance metrics (CDR, CPR, static loss curves) - Cashflow-based reconstructions (monthly prepayment/default timelines) *![Stratification tables](https://cardoai.com/wp-content/uploads/2025/06/Imag2-Stratification-1024x400.png)* *Image 1: Stratification tables* *![Performance KPIs historical evolution](https://cardoai.com/wp-content/uploads/2025/06/Data-controls-summary-1024x486.png)* *Image 2: Performance KPIs historical evolution* **Advanced customization Use Dynamic Views to: – Segment portfolios by custom criteria (ex: high-risk borrowers, short-term loans) – Create investor-ready exports with key visualizations – Stress test hypothetical risk scenarios Use Case **The challenge**: A US-based specialty finance investor received a consumer loan portfolio in a non-standard format with limited historical performance data. With just days to decide, they needed a full risk-return analysis fast. **The solution: They uploaded the raw file to our platform. Within minutes, they: - Identified data gaps and engaged the originator for fixes - Generated performance metrics (CDR, CPR, delinquency) - Built stratification tables by FICO and DTI - Flagged prepayment-heavy segments using custom views **The result: They completed due diligence in under 48 hours, spotted high-DTI borrowers posing elevated risk, and adjusted their pricing. With a compelling, data-backed offer, they beat slower competitors. See how Cardo AI helps you move faster than the competition when the best deals are on the table. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [How cash-flow simulation improves ABS portfolios](https://cardoai.com/whats_new_in_product/how-cash-flow-simulation-improves-abs-portfolios/) **Published:** August 20, 2025 **Author:** RobertaCardo26 **Excerpt:** Understanding the cash-flows generated by an Asset-Based Security (ABS) is paramount for investors seeking to assess its profitability and make informed decisions. However, predicting future cash-flows can pose a significant challenge, especially given the intricate nature of ABS transactions. **Content:** Contents [Introduction](#introduction) [What’s in it for you?](#second-par) [Why does it matter?](#third-par) [How does it work?](#fourth-par) [Use Case](#eight-par) Introduction Understanding the cash flows generated by an Asset-Based Security (ABS) is paramount for investors seeking to assess its profitability and make informed decisions. However, predicting future cash flows can pose a significant challenge, especially given the intricate nature of ABS transactions. Such complexity can often lead to unforeseen losses. The inability to accurately forecast cash-flows not only impedes risk detection but also hampers proactive risk mitigation efforts. This is where our Cash-Flow Simulation tool comes in to help. What’s in it for you? - Asset level forecast: Our tool operates at a single asset level, generating the underlying pool’s cash flows forecast with the highest level of accuracy. - Comprehensive performance evaluation of your existing or new ABS transactions: You will gain insight into the cash-flows generated by the ABS tranches through the exact waterfall structure, and access key metrics such as Internal Rate of Return (IRR) and Fair Value. - Sophisticated stress testing: You can properly stress-test your assumptions by generating a wide range of scenarios and comparing them. This empowers you to anticipate various market conditions and proactively manage potential risks, minimizing the impact of unforeseen events. Why does it matter? When it comes to ABS investments, it’s crucial to navigate the intricate landscape of potential future scenarios to effectively manage risks and mitigate losses. This is especially true when it comes to mezzanine and junior tranches, as they often carry higher levels of risk, requiring diligent monitoring and proactive risk management strategies. Given the high volume of data and the complexity of ABS transactions’ structures, relying solely on Excel is not an optimal solution, as it is prone to manual errors, and setting up an accurate cash-flow model can be extremely time-consuming. By leveraging our tool, investors can understand the evolving landscape of cash flows, defaults, and prepayments and gain invaluable insights into the performance and resilience of their ABS investments, effortlessly and in a matter of seconds. Armed with this knowledge, investors can tailor their strategies, adjust their portfolios, and implement risk mitigation measures to navigate market uncertainties with confidence. In essence, our tool offers investors the means to anticipate unforeseen events, enabling them to effectively manage risks and maximize returns in dynamic market environments. How does it work? Our tool operates on a dual level, providing cash-flow forecasts for both the underlying assets of an existing or new portfolio and the associated ABS tranches. The forecasts are based on a wide array of user inputs, ranging from interest rates to default and prepayment rates. These inputs can be customized by providing a flat value or a curve, and stressed by creating multiple scenarios, allowing the users to explore various market conditions and their potential impact on cash flows (See Figure 1). Moreover, for each scenario, the tool offers the option to specify inputs for specific clusters within the portfolio, based on underlying asset attributes such as borrower’s geography or interest rate type, for example. *![Inputs set up for comprehensive scenario analysis](https://cardoai.com/wp-content/uploads/2025/08/How-cash-flow-simulation-improves-ABS-portfolios-image1-1024x558.png)* *Figure 1: Inputs set up for comprehensive scenario analysis* Once the parameters are set, users can execute the scenario. The tool then computes forecasts for each individual underlying asset, taking into account the specified parameters and market conditions, returning the aggregate evolution over time of cash-flows, defaults, and prepayments. It also generates cash-flows for each ABS tranche, according to the transaction’s waterfall structure. In addition to cash flow forecasts, the tool calculates essential metrics such as IRR, Fair Value, and Losses on the tranches. These metrics offer valuable insights into the performance and risk profile of the ABS tranche, empowering users to make informed decisions and effectively manage their investments. The scenario outputs are presented in configurable views to facilitate the results analysis and get immediate insights (See Figure 2). *![Result views (Cash-flow charts)](https://cardoai.com/wp-content/uploads/2025/08/How-cash-flow-simulation-improves-ABS-portfolios-image2-1024x458.png)* *Figure 2: Result views (Cash-flow charts)* Use case Our client, a leading European asset manager specializing in mezzanine and junior ABS tranches investments, faced challenges in accurately understanding the returns on their investments as the underlying portfolio evolved. With a diverse portfolio containing a high number of assets, they struggled to create a cash-flow model in Excel. Additionally, modeling the transaction’s waterfall structure proved to be complex. Consequently, the client resorted to forecasting on an aggregated level, relying on broad assumptions for tranches repayment. This approach, however, yielded less accurate results. Upon adopting Cardo AI, the client experienced a transformative shift. They gained access to more accurate results in a fraction of the time, thanks to our tool’s advanced capabilities. The ability to simulate multiple scenarios provided invaluable insights, allowing them to assess potential losses across various scenarios. Moreover, with the Fair Value estimation, the client could proactively identify notes at higher risk of losses and take preemptive actions. This empowered them to optimize their portfolio and mitigate potential losses effectively. In summary, our client leveraged Cardo’s sophisticated tools to enhance decision-making, minimize risks, and maximize returns in their investments. See how Cardo AI replaces your Excel cash flow models with accurate, asset-level forecasts across any market scenario. [Book a demo](https://cardoai.com/book-a-demo/) → --- ### [Automated calendar: dates done right, every time](https://cardoai.com/whats_new_in_product/automated-calendar-dates-done-right-every-time/) **Published:** September 9, 2024 **Author:** RobertaCardo26 **Excerpt:** Introducing the Automated Calendar Module, a powerful tool specifically designed to enhance the management of key dates and events within private Asset-Backed Financing (ABF) deals. This innovative tool enables users to automate and customize the scheduling of essential agreement dates, including intere **Content:** Contents [Introduction](#introduction) [What’s in it for you](#first-par) [Why does it matter](#second-par) [How does it work](#third-par) [Use case](#seventh-par) Introduction Introducing the Automated Calendar Module, a powerful tool specifically designed to enhance the management of key dates and events within private Asset-Backed Financing (ABF) deals. This innovative tool enables users to automate and customize the scheduling of essential agreement dates, including interest accruals and fee management. What’s in it for you The Calendar Module provides a range of benefits to financial professionals managing ABF deals: - **Automated scheduling:** Say goodbye to manual entry errors and streamline your scheduling processes with automation. - **Customization for precision:** Tailor the system logic to meet the specific needs of your financial arrangements, ensuring accurate interest and fee accruals. - **Enhanced visibility:** Visualize all critical dates and events, allowing you to stay on top of your financial operations with ease. - **Flexible modifications:** Adjust dates, events, and periods as needed to accommodate changing circumstances in your deals. Why does it matter Managing the intricate web of payment schedules, interest periods, and event triggers in ABF deals is a complex and time-sensitive task. A missed date or incorrect calculation can have costly consequences, including breaches of contract and financial penalties. The Calendar Module addresses these challenges by centralizing and automating the scheduling of key events, ensuring that all dates align with contractual agreements and compliance requirements. This tool integrates seamlessly with other financial information, enabling accurate interest and fee calculations, and helping professionals maintain compliance across all levels of a deal. How does it work The Calendar Module operates through three main components: - **Holidays Manager:** Users can select specific country calendars to identify standard business days. When a generated date falls on a non-business day, users can specify how the system handles it, whether by moving the date forward (Following), backward (Preceding), or leaving it unchanged (Actual). - **Loan Payment Schedule Manager:** This feature integrates with the Payment Schedule Engine to receive and manage loan payment dates. It ensures that payments are made on time and following the terms of the agreement. - **Periods and Events Orchestrator:** This is the core of the module, allowing users to set up single or recurring events (e.g., Payment Date, Calculation Date, Covenants Checks) and define single or recurring periods (e.g., Collection Period, Interest Period). Users can configure events based on their contractual agreements and link them to specific conditions, such as covenant monitoring or new drawdowns. The tool also allows users to modify and visualize all relevant dates, periods, and events, providing a comprehensive overview of all key timelines. Furthermore, it integrates with other financial components to enable accurate accrual calculations based on the dates and events created within the tool. Use Case Consider a scenario where you are managing a complex private ABF transaction with multiple facilities in different jurisdictions, having a different payment schedule each. You need to ensure that all key dates, such as payment dates, interest periods, and event triggers, are managed accurately across multiple jurisdictions. Using the Calendar Module, one can: - Set the Holidays Manager to align with the appropriate business days for each jurisdiction, ensuring that payment dates are adjusted for holidays. *![Holidays manager](https://cardoai.com/wp-content/uploads/2026/03/automated_calendar_1-1024x425.png)* *Image 1: Holidays Manager* - Leverage the Loan Payment Schedule Manager to automatically receive and track all loan payment schedules. *![Loan Payment Schedule Manager](https://cardoai.com/wp-content/uploads/2026/03/automated_calendar_2-1024x420.png)* *Image 2: Loan Payment Schedule Manager* - Utilize the Periods and Events Orchestrator to define and manage key financial periods and events, such as recurring payment dates and collection periods, while linking these events to relevant financial conditions. *![Periods and Events Orchestrator](https://cardoai.com/wp-content/uploads/2026/03/automated_calendar_3-1024x425.png)* *Image 3 – Periods and Events Orchestrator* With this level of automation and customization, you can confidently manage all aspects of your ABF deal, ensuring that no critical date is overlooked, and that your financial operations run smoothly and compliantly. See how Cardo AI eliminates missed dates, manual errors, and compliance risk across every deal in your portfolio. [Book a demo](https://cardoai.com/book-a-demo/) → --- ## Blog ### [Weekly Deal Roundup | September 18–24, 2026](https://cardoai.com/article/weekly-deal-roundup-september-18-24-2026/) **Published:** September 28, 2026 **Author:** GiadaCardo26 **Content:** Contents [Introduction](#introduction) [The ABF Edge](#podcast) [The week at a glance](#first-par) [Deal 1 – Rivonia Road Capital](#second-par) [Deal 2 – Trinity Capital](#third-par) [Deal 3 – AgWest Farm Credit](#fourth-par) [The rest of the week](#fifth-par) [Key risks to watch](#sixth-par) [Where the opportunities are](#seventh-par) [Strategic considerations](#eight-par) Last week the story was refinancing, six of eight tabled deals taking out an incumbent. This week the volume came back with it. Sixteen financings hit the ABL Advisor deal tables and news feed between Friday 18 and Thursday 24 September, roughly $1.97 billion in disclosed commitments, and the two largest were both refinancings of existing debt. Underneath them sat a very different set of credits. The week’s most interesting exit facility funds an estate whose main asset is more than $1 billion of antitrust claims. A $50 million corporate loan sits on top of a lender whose own collateral is merchant sales. A $270 million facility advances against tax credits a factory earns by producing. None of that fits on a standard borrowing-base certificate. Here is our read on the deals, what they mean for the lenders and asset managers holding them, and where the risks and opportunities are. **The ABF Edge** ![](https://cardoai.com/wp-content/uploads/2026/09/ABF-Edge-03-cover.png) **The week at a glance** Sixteen financings, largest first: AgWest Farm Credit’s $475MM refinancing for Clearwater Paper, a $200MM revolver plus a $275MM fully funded term loan; Apollo’s $585MM financing for The Executive Centre; a bank syndicate’s $270MM revolver and Section 45X tax credit advance facility for Form Energy; $220MM of tax equity bridge, construction and term debt for GridStor’s White Tank battery project from ING Capital, KeyBanc Capital Markets and Zions Capital Markets; Atlas Grove Partners’ $180MM exit facility for the Harvest Sherwood estate; Rivonia Road Capital’s $150MM facility for Metycle; Trinity Capital’s $50MM corporate debt facility to Wayflyer; MidCap Business Credit’s $13MM acquisition ABL for Anchor Manufacturing; Rosenthal Capital Group’s two transactions totalling over $10MM; SLR Business Credit’s $8MM ABL for a premium beverage mixer brand; Entrepreneur Growth Capital’s $3MM line for a Chicago digital talent marketplace; Big Shoulders Capital’s $1.75MM DIP for an Arizona contractor; Republic Business Credit’s $750K staffing factoring facility; and undisclosed facilities from Cambridge Savings Bank for Belle Brands’ acquisition of Vegamour, SG Credit Partners for a family-owned food distributor, and Monroe Capital for Sweetmore Bakeries’ acquisition of Fantasy Baking. Also in the news feed this week, not financings to a borrower: Wingspire Equipment Finance closed WEF 2026-1, a $407.07MM equipment ABS across six classes of notes and its largest to date; KKR committed $350MM to launch Akrapoint Commercial Capital in mid-ticket equipment finance; Ares and PSP Investments formed a joint venture to invest up to $2.4B in U.S. logistics real estate; and LIV Golf won interim access to $14MM of a $49.6MM DIP facility. Sector spread: paper, flexible workspace, long-duration energy storage, utility-scale batteries, litigation assets, secondary metals, SMB fintech, metal stamping, beverages, prestige beauty, food distribution and staffing. **Rivonia Road Capital: $150MM credit facility to Metycle** Metycle is a Cologne-based supplier of secondary copper and aluminium, founded in 2022 by Rafael Suchan and Sebastian Brenner, with a team across 15 countries and more than $120 million of secondary metals traded in the past twelve months, up around 150% year on year. Rivonia Road Capital, a Los Angeles-based asset-backed lender founded in 2017, is providing $150 million to fund eligible transactions across the full trade cycle, from the moment Metycle pays a scrap supplier through to buyer settlement. **What it means for asset managers.** This is commodity trade finance done as a borrowing base. The collateral is metal in transit and receivables from industrial buyers and smelters, so the facility is a series of individually underwritten transactions rather than a static pool. What makes it financeable at this size is verification: Metycle specifies and traces each batch, so the lender is advancing against data about the material rather than a warehouse receipt. **Risk.** Copper and aluminium prices move fast, so goods in transit need marking, not a monthly estimate. Add concentration on a small number of smelter counterparties, eligibility drift as transactions slip in below spec, quality disputes on delivery, custody risk while material is shipping, and a currency mismatch between a euro-reporting borrower and a dollar facility. **Opportunity.** Banks have pulled back from physical commodity flows, and few private credit managers can underwrite them transaction by transaction. Lenders that can reconcile a borrowing base to batch-level quality and custody data can price this where others cannot look. **Trinity Capital: $50MM corporate debt facility to Wayflyer** Wayflyer funds working capital for e-commerce and consumer brands and has deployed more than $6 billion since 2020. Trinity’s $50 million corporate facility adds to a $1.5 billion forward flow with Fortress agreed in July and a $250 million facility with ATLAS SP Partners agreed in February. Proceeds fund growth and let Wayflyer invest its own capital in new areas, including its AI capability. **What it means for asset managers.** The forward flow and the warehouse fund the receivables. This loan sits at the holding company and funds what those vehicles do not. Trinity’s real collateral is the residual economics of Wayflyer’s book: excess spread, servicing income and the equity in its funding vehicles. **Risk.** Structural subordination. If merchant performance slips, a warehouse trigger can step down the advance rate or trap cash at the SPV. The senior funders stay whole while nothing flows up to the corporate lender. **Opportunity.** Lending to originators, above their asset-level facilities, is a growing part of lender finance, and KKR is entering the same space this week with Akrapoint. The corporate lender that sees the same loan-level data as the warehouse lender can price the junior risk properly. **AgWest Farm Credit: $475MM refinancing for Clearwater Paper** The largest ticket of the week. Clearwater Paper entered an amended and restated credit agreement on 18 September with AgWest Farm Credit as administrative agent and a syndicate of lenders, replacing its 2028 senior notes, its term revolver and its existing ABL revolver with a $200 million revolving facility and a $275 million term loan funded at closing, plus an uncommitted $100 million increase option. **What it means for asset managers.** A paperboard producer has moved from a notes-and-ABL structure to a single bank-syndicate package and pushed its maturities out. When the borrower performs, the cheapest capital still wins, and specialty lenders are not in that conversation. **Risk.** Commodity pricing and a term loan funded in full at closing. Paperboard margins move with input costs and containerboard pricing, and the revolver is nearly undrawn, so liquidity depends on the borrowing base staying clean through a cycle. **Opportunity.** Refinancings of this size reset the reporting relationship. The agent that can consolidate collateral and covenant reporting across a syndicate, rather than collect it by email, keeps the administrative economics. **The rest of the week** Apollo provided $585 million to The Executive Centre, large private credit into flexible workspace, where value sits in lease economics and occupancy rather than hard collateral. A bank syndicate closed $270 million for Form Energy, pairing a revolver with a facility that advances against Section 45X credits earned by producing eligible components. The collateral is a government credit created by output, so verified production, not forecast capacity, should drive the advance rate. ING Capital, KeyBanc and Zions put $220 million behind GridStor’s White Tank project, a 100 MW / 400 MWh battery in Arizona under a 20-year tolling agreement with Arizona Public Service. The offtaker carries the credit; completion and tax equity timing are the risks. Rivonia Road Capital provided $150 million to Metycle, a global supplier of secondary copper and aluminium. Scrap metal trade finance is a set of individual trades rather than a static pool, priced off commodity volatility, counterparty performance and title through shipment. In the middle market, MidCap Business Credit funded a $13 million acquisition ABL for Anchor Manufacturing, a Cleveland metal stamper serving automotive, agriculture, solar and defence; Rosenthal Capital Group closed two transactions totalling over $10 million; and SLR Business Credit provided an $8 million revolver to a premium carbonated mixer brand. Banks moved in both directions. Cambridge Savings Bank backed Belle Brands on its acquisition of Vegamour, while SG Credit Partners took out a money-center bank at a family food distributor and Entrepreneur Growth Capital provided $3 million to a venture-backed talent marketplace whose bank called its loan. At the small end, Big Shoulders Capital closed a $1.75 million DIP in under three weeks and Republic Business Credit funded a $750,000 staffing facility, its first through the Meritus Capital partnership. **Key risks to watch** - **Non-standard collateral, standard reporting.** Claims, tax credits, trade flows and holdco residuals do not fit a borrowing-base certificate. Monitoring these quarterly and by hand means pricing stale data. - **Litigation duration.** Coverage on claims-backed facilities decays with time, legal costs and contingency fees. The timetable belongs to the court, not the lender. - **Structural subordination in lender finance.** Corporate lenders to originators are paid last. A trigger inside an SPV can starve the holdco long before any corporate covenant trips. - **Eligibility risk on tax-credit collateral.** A 45X advance is only as good as the production that earns the credit and the rules that define eligibility. - **Stress at the small end.** Two staffing facilities, two bank take-outs and a DIP landed in the same week. Houlihan Lokey reported this month that stress among the smallest private credit borrowers has risen more than tenfold since 2023. **Where the opportunities are** - **Litigation and special-situations ABF.** Estates with contingent assets need capital that can price claims, and the lenders that can model them face almost no competition from banks. - **Corporate facilities to scaled originators.** Fintech lenders with multiple warehouses still need holdco capital. The premium is paid for junior risk and earned by seeing the collateral underneath. - **Commodity and trade-flow finance.** Rivonia Road’s Metycle facility shows the appetite for transaction-level lending where each trade is underwritten and tracked on its own. - **Bank take-outs in the lower middle market.** A called loan at a bank is a mandate for a specialty lender that can diligence and close in weeks, as SG Credit and Entrepreneur Growth Capital both demonstrated this week. **Strategic considerations** The two largest deals of the week were refinancings won on price and maturity. The most interesting ones were not. A bankruptcy estate holding lawsuits, a fintech lender’s holding company, a factory’s tax credits and a metals trader’s shipments have nothing in common except this: none of them has a borrowing base anyone can walk into a warehouse and count. In each case the collateral is a model. The coupon is set at closing. The return depends on how often that model is updated, because a settlement, a production report, a shipment or a slowdown in merchant repayments moves collateral value the day it happens. Cardo AI gives ABF lenders, private credit managers and capital allocators one platform for all of it: borrowing-base calculation, eligibility and concentration testing, covenant surveillance, waterfall modeling and loan-level collateral analytics. AI-driven ingestion of borrower reporting keeps the data current in days rather than months. Whether the collateral is a docket of antitrust claims, verified 45X production or an originator’s merchant book, the workflow is the same and the visibility is continuous. If any of this week’s deals look like your book, we would like to show you how our clients run them. **Sources** *[ABL Advisor deal tables](https://www.abladvisor.com/deal-tables) *(deal IDs 9723–9731) and news feed, 18–24 September 2026; Atlas Grove press release on court approval of its $180 million financing commitment, 8 July 2026, and Harvest Sherwood case reporting; Trinity Capital press release on the Wayflyer facility, 18 September 2026; Clearwater Paper credit agreement as reported in the ABL Advisor deal detail. Deal terms as reported by the lenders. This post is for information only and is not investment advice.** If you’re scaling an asset-based or private credit book, [we should talk. ](https://cardoai.com/company/contact-us/) --- ### [Cardo AI is one of Italy's ten most talent-dense scale-ups](https://cardoai.com/article/cardo-ai-ranks-in-the-top-2-for-talent-density/) **Published:** September 22, 2026 **Author:** GiadaCardo26 **Content:** Contents [What the score measures](#first-par) [What 86.1 actually means](#second-par) [Italy’s top ten scale-ups by Talent Density Score](#third-par) [Why it matters to us](#fourth-par) Every company says it hires the best people. [Nova](https://www.novarecruiter.ai/solutions/talent-density/) has tried to put a number on it. Its Talent Density Score ranks companies by the strength of the people who work there today, drawn from the more than 800 million professional profiles Nova ranks. Cardo AI scored 86.1 and enters Italy’s top ten scale-ups by talent density, alongside some of the best known names in the country’s tech scene. What the score measures The score is not about brand, funding or headcount. It is the average talent of every person currently working at a company. Each individual score is built from four pillars: experience, education, international exposure and languages. These are weighted by career stage, so a junior analyst and a senior engineer are assessed on different terms. The scale runs from 0 to 100, where 100 is the strongest company Nova ranks today. It moves with the market rather than with Nova. What 86.1 actually means Nova places every company in the segments it belongs to. Here is where Cardo AI sits in each: - **Among companies our size** (1 to 200 employees): 672nd out of 34,140. Fewer than 700 small companies in the ranking score higher. - **In technology:** 673rd out of 16,687 firms. The average technology company scores 67.8, so we sit 18.3 points above it. - **In financial services:** 393rd out of 9,780 firms, against an average of 65.2. Put simply, of every 100 companies of our size that Nova measured, 98 have a lower talent density than we do. Italy’s top ten scale-ups by Talent Density Score RankCompany1Circeus2Bending Spoons3AIKO4Axyon AI5Agile Lab6Domyn7Empatica8indigo.ai9Cardo AI10CubbitSource: Nova Talent Density, data updated August 2026. Why it matters to us Private credit and asset-based finance are complex markets. Building infrastructure for them takes people who understand both the technology and the finance, and who can work across borders and languages with clients in different markets. That is the team we have tried to build. It is good to see an independent, data-led measure reflect it. A score is a snapshot, not a finish line. Talent density moves one hire at a time, and every person who joins shapes the next number. If you want to work alongside this team, take a look at our open roles. [Join us](https://jobs.deel.com/cardo). --- ### [Weekly Deal Roundup | September 14–18, 2026](https://cardoai.com/article/weekly-deal-roundup-september-14-18-2026/) **Published:** September 21, 2026 **Author:** GiadaCardo26 **Content:** Contents [Introduction](#introduction) [Deal 1 – Great American Capital](#first-par) [The ABF Edge](#podcast) [Deal 2 – Post Road Equipment Finance](#second-par) [The rest of the week](#third-par) [Key risks to watch](#fourth-par) [Where the opportunities are](#fifth-par) [Strategic considerations](#sixth-par) Last week, no bank appeared as sole lender on the tabled deals. This week First Citizens, J.P. Morgan, BMO, TD and East West Bank were all back. Sixteen deals hit the ABL Advisor tables, totalling roughly $1.1 billion in commitments. Yet the largest ticket, a $600 million exit ABL for a retailer just out of Chapter 11, went to non-bank capital at SOFR + 5.75%. The most interesting small ticket was $20 million of equipment finance against GPUs. Together they show where the credit premium sits right now. It goes to lenders who can underwrite collateral that moves fast, whether a receivable settling in two days or a chip that loses value with every new generation. **The ABF Edge** ![](https://cardoai.com/wp-content/uploads/2026/09/ABF-Edge-02-cover.png) **Great American Capital Corp.: $600MM exit ABL** GACC, majority-owned by GA Group and funds managed by Oaktree, joined a new $600 million ABL tied to a borrower’s completed restructuring. The borrower is unnamed, but the size, timing and Oaktree link match the exit ABL QVC Group put in place on emerging from Chapter 11 on 6 August. According to QVC Group’s 8-K, that revolver advances 90% against eligible card receivables, 85% against instalment receivables and 85% of NOLV on inventory, with a drawn margin of SOFR + 5.75%. That is a bank product, run and priced by non-bank capital. The premium pays for post-bankruptcy status. The collateral itself is ordinary. **Risk:** Three pools on three clocks. Card receivables settle in days, instalment receivables take months, and inventory is seasonal and sold through a TV channel in structural decline. Takeback notes also hold first-priority liens, so the intercreditor is central. **Opportunity:** Exit ABLs offer a near-double-digit coupon on short-dated, self-liquidating collateral. Lenders who can re-underwrite the base weekly capture it without equity-like risk. **Post Road Equipment Finance: $20MM GPU facility** Post Road financed next-generation GPUs for a U.S. cloud provider, tied to a new multi-year customer contract. This is the lower mid-market version of the neocloud trade. If amortisation matches the contract, the loan is repaid from contracted cash flow before hardware residual value matters. **Risk:** Obsolescence, rental rate pressure, single-customer concentration and dependence on a third-party data centre. **Opportunity:** Mid-market GPU providers are too small for structured desks and too asset-heavy for venture debt. Lenders who track assets by serial number can own this niche. **The rest of the week** First Citizens’ $157 million refinancing of Blue Doors Storage Fund IV is fund-level lender finance against stabilising and development-stage storage. The test is occupancy ramp against maturity. BMO and TD’s $80 million syndicated facility for Cannara Biotech shows Canadian cannabis credit moving from specialists to bank syndicates. The spread narrows. The monitoring burden does not. CIT Northbridge ($70 million, flexible packaging), Republic Business Credit ($15 million apparel factoring with a $20 million accordion) and SLR Business Credit ($10 million A/R-only ABL for a fuel distributor) round out a week where structure mattered as much as size. **Key risks to watch** - Post-reorganisation collateral: Two exit facilities closed this week. Appraisals done in bankruptcy reflect stress, and eligibility shifts most in the first 90 days after emergence. - Split liens: When takeback notes and an ABL both claim priority, the intercreditor decides who owns which dollar. - Consumer credit inside commercial ABL: Instalment receivables need roll rates and vintage losses in the eligibility tests. - Depreciating tech collateral: GPU loan-to-value must be marked to secondary pricing and contracted revenue, not the original invoice. - Bank re-entry: Where banks return, specialist spreads narrow. Structure and monitoring become the differentiator. **Where the opportunities are** Exit ABLs at term-loan pricing. Contract-backed GPU facilities of $10–50 million. Fund-level lending into operating real estate. Accordion-led factoring for brands with strong sell-through. **The strategic read** The two most interesting deals sit at opposite ends of the size range but rest on one idea. In both, the IRR is decided after closing. Utilisation, reserve accuracy, eligibility drift and collateral marks determine whether the coupon becomes return or a workout. Cardo AI gives ABF lenders and private credit managers one platform for borrowing-base calculation, eligibility and concentration testing, covenant surveillance, waterfall modelling and loan-level analytics. AI-driven ingestion keeps borrower data current in days, not months. If any of this week’s deals look like your book, we would like to show you how our clients run them. **Strategic considerations** The two most interesting deals this week sit at opposite ends of the size range, yet they rest on the same idea. In QVC-sized retail, the credit premium compensates the lender for owning a borrowing base that has to be re-underwritten every week after a restructuring. In GPU finance, the premium compensates the lender for owning an asset whose value falls on a schedule set by the chip industry rather than by the borrower. In both cases, the IRR is decided after closing. Utilization, reserve accuracy, eligibility drift, contract cash-flow coverage and collateral marks determine whether that coupon turns into return or into a workout. Cardo AI gives ABF lenders, private credit managers and capital allocators one platform for all of it: borrowing-base calculation, eligibility and concentration testing, covenant surveillance, waterfall modeling and loan-level collateral analytics. AI-driven ingestion of borrower reporting keeps the data current in days rather than months. Whether the collateral is card settlements and installment vintages at a post-reorg retailer or serial-numbered GPU servers in a leased data hall, the workflow is the same and the visibility is continuous. **Sources** *[ABL Advisor deal tables](https://www.abladvisor.com/deal-tables) (September 11-17, 2026); QVC Group Form 8-K, 7 August 2026; GACC press release, 16 September 2026; Post Road press release, 14 September 2026. The GACC–QVC link is Cardo AI’s inference from public information and is unconfirmed by GACC. For information only, not investment advice.* If you’re scaling an asset-based or private credit book, [we should talk. ](https://cardoai.com/company/contact-us/) --- ### [Weekly Deal Roundup | September 7–11, 2026](https://cardoai.com/article/weekly-deal-roundup-september-7-11-2026/) **Published:** September 14, 2026 **Author:** GiadaCardo26 **Content:** Contents [1. Introduction](#introduction) [2. Pathlight Capital – $60MM](#first-par) [3. Hilco Global Real Estate Capital – $44.5 MM](#second-par) [4. Ares Commercial Finance – $25 MM](#third-par) [5. The ABF Edge](#podcast) [6. MidCap Business Credit – $10 MM](#fourth-par) [7. TradeCap Partners – $7.5 MM](#fifth-par) [8. SouthStar Capital – $3 MM](#sixth-par) [9. Commercial Finance Partners – $750,000](#seventh-par) [10. SouthStar Capital – $500,000](#eight-par) [11. Takeaways](#nine-par) [12. How Cardo AI helps asset managers and lenders](#ten-par) Eight facilities were announced in asset-based and specialty credit last week, totalling roughly $151.3 million across eight sectors and seven lenders. No syndicate, no billion-dollar headline, nothing that will move a league table. Look at how the deals were built rather than how big they were and a pattern appears. Six of the eight had to get past a lender who was already in the structure. Three refinanced an incumbent out. Three negotiated a carve-out or a subordination and left the incumbent in place. That is the real work in this market now. The borrower conversation is the easy part. The structuring conversation happens with whoever holds the existing lien. Here is what closed, why each deal matters, what could go wrong, and where the opportunity sits. **Pathlight Capital: $60 million second lien term loan to Lifetime Brands** Pathlight Capital acted as administrative agent on a $60 million senior secured term loan to Lifetime Brands (NasdaqGS: LCUT), the Garden City, New York designer and marketer behind Farberware, Mikasa and S’well. The company’s own disclosure describes it as a second lien term loan replacing its existing Term Loan B. It closed alongside an amended and extended $200 million asset-based revolving credit facility agented by JPMorgan. Both facilities now mature in August 2031. Why it matters: This is the clearest picture of the current division of labour. The bank kept the revolver, secured against receivables and inventory through a borrowing base it already knows how to monitor. The private credit fund took the second lien, secured against everything else and priced for the risk of standing behind a $200 million first-lien claim. Neither lender wanted the other’s position. Both wanted the same borrower. It also puts a date on a maturity wall that was getting close, and it does so for both layers at once. Refinancing a term loan without extending the revolver underneath it solves half a problem. Key risks: Consumer discretionary demand across kitchenware and tableware is cyclical and currently soft. Retail customer concentration is high in this category, and a single account renegotiating terms moves the receivables base. Sourcing exposure to tariffs remains live. For the second lien specifically, recovery sits behind a $200 million first-lien borrowing base that can grow with inventory build, which means the effective attachment point moves with seasonal working capital. There are now two credit agreements with interacting covenant packages and two sets of reporting obligations on the same collateral. Opportunities: For Pathlight, a co-existence structure with a money-centre bank on a listed borrower is a template it can repeat. For asset managers, this is the deal to study when sizing second-lien exposure behind an ABL: the risk is not the borrower’s earnings, it is the volatility of the first-lien availability sitting above you. That is a data problem before it is a credit problem. One aside worth noting. ABL Advisor’s table classifies Lifetime Brands under “Marketing”. The company makes kitchenware. Anyone building a sector exposure view from vendor tags rather than the underlying obligor will misclassify this position. **The ABF Edge** ![](https://cardoai.com/wp-content/uploads/2026/09/abf-edge-cover-1.png) **Hilco Global Real Estate Capital: $44.5 million first mortgage to Foundation 8** Hilco Global Real Estate Capital structured a $44.5 million first mortgage secured by the office component of 333 N Central Avenue in downtown Phoenix, the former Freeport-McMoRan headquarters. The collateral covers 246,490 square feet of Class AA office across the top eight floors, 2,522 square feet of ground-floor retail, rooftop amenity rights and shared access to the parking structure. A separately owned 242-key Westin hotel occupies the middle of the tower. Foundation 8, a joint venture of GIA Hospitality and Trillium Management, acquired the office component in December 2025. The loan refinances that acquisition financing and funds leasing costs, tenant improvements and capital works for the conversion from a single-tenant corporate headquarters to a multi-tenant building. Sterling Realty Capital acted as debt placement agent. Why it matters: Transitional office is the asset class bank credit committees have spent three years avoiding. A vacant former headquarters undergoing a repositioning has no in-place cash flow to underwrite and no comparable to anchor an appraisal. It is now being financed, at scale, by a specialty credit platform rather than a bank. That is a structural shift in who owns US office risk, not a one-off. The structure is also interesting on its own terms. The lender is funding the business plan, not just the asset, which means a large share of the commitment will be drawn against future leasing activity rather than advanced at close. Key risks: Lease-up pace in downtown Phoenix is the whole thesis. Absorption of Class AA space in a submarket where the anchor tenant just left is untested. Tenant improvement and leasing commission spend can outrun the plan quickly when a landlord is competing for the first few tenants. This is single-asset, single-market, single-strategy concentration. The shared parking and the adjacent hotel add operating complexity and an unusual title and easement structure. Exit financing on a repositioned asset assumes a takeout market that does not currently exist at scale. Opportunities: For lenders willing to underwrite a business plan rather than a rent roll, the basis is attractive and competition is thin. For asset managers, transitional CRE credit is where the illiquidity premium is genuinely being paid right now. The constraint is operational: a facility that funds in tranches against leasing milestones needs draw-level monitoring, not quarterly valuation marks. **Ares Commercial Finance: $25 million ABL revolver to Real Good Foods** Ares Commercial Finance, the commercial finance platform of Ares Management, provided a $25 million senior secured asset-based revolving line of credit to Real Good Foods, a Bolingbrook, Illinois producer of protein-focused frozen foods. The company’s CFO described ACF as its new revolving lender. Why it matters: The word “new” is doing the work. An incumbent revolving lender was replaced. This is a straightforward re-banking of a growth food business into a platform that underwrites the borrowing base rather than the earnings profile, which is the right structure for a company whose working capital swings faster than its EBITDA stabilises. Ares also brings something a smaller ABL shop cannot: the ability to hold the whole facility and grow it without a syndication process. Key risks: Frozen inventory is harder collateral than it looks. Turns matter, shrink matters, and liquidation value depends on cold chain integrity and brand transferability. Grocery customer concentration is high in this category, and retailer deductions and chargebacks erode receivable eligibility in ways that only show up in dilution testing. Advance rates on perishable inventory need active appraisal refresh. Availability will swing seasonally. Funding growth through a revolver works until a build cycle outruns the base. Opportunities: Food and beverage ABL remains a reliable middle-market category with real collateral and predictable dilution behaviour once you have the data history. For managers, this is a reminder that the incumbent lender is the competitive set: winning here means offering a better-cut borrowing base, not a tighter spread. **MidCap Business Credit: $10 million facility to a paper converter** MidCap Business Credit closed a $10 million asset-based facility for a privately held paper converter that makes custom paper products for packaging manufacturers. The structure combines a working capital revolver with an equipment term loan, refinances existing bank debt, and was sourced through MidCap’s Chicago office. The lender’s own comment is the important detail: the structure unlocked additional borrowing availability that had been constrained under the previous lending relationship. Why it matters: Same company, same assets, more availability. Nothing changed about the collateral. What changed was the eligibility criteria, the advance rates and the willingness to lend against equipment alongside working capital. This is the quiet mechanism behind a large share of lower-middle-market ABL volume. Borrowers do not usually leave a bank because of price. They leave because the bank’s eligibility definitions were leaving liquidity trapped on the balance sheet. Key risks: Packaging demand tracks industrial and consumer shipping volumes and is cyclical. Paper input costs move sharply and pass-through lags. The equipment term loan depends on orderly liquidation values for converting machinery, which is specialised and thinly traded. Most importantly, the higher advance rests on dilution and eligibility assumptions that have not yet been tested through a full cycle with this borrower. Customer concentration in a private converter is usually higher than it first appears. Opportunities: Re-cutting eligibility is the most repeatable origination strategy in lower-middle-market ABL, and it requires no additional risk appetite, only better collateral analytics. For asset managers allocating to this segment, the question to ask a manager is not what they lend against but how they determine what is eligible, and how often they re-test it. **TradeCap Partners: $7.5 million purchase order facility to a warehouse rack importer** TradeCap Partners provided a $7.5 million purchase order funding facility to a Midwest importer of warehouse rack systems and carts. The company was referred by an asset-based lender who assessed that the immediate need was PO funding rather than a receivables and inventory line. The facility pays the overseas supplier against shipping documents so the borrower can fulfil large orders from a national food distributor. The company had been running on a small SBA-backed working capital line that could not carry the order size. TradeCap underwrote in two weeks and obtained a subordination from the SBA relating to the working capital assets tied to those specific customer orders. Why it matters: This is a three-party structure on a mid-sized private company. An ABL lender who could not do the deal referred it rather than lose the relationship. A government-guaranteed lender agreed to step back from a defined slice of collateral. A specialty funder took first position on the assets tied to named purchase orders. The collateral is now carved by transaction, not by asset class. That is a meaningful increase in structural complexity at the $7.5 million level. Key risks: Performance risk sits with the overseas supplier: pay against documents and the goods still have to arrive and conform. Tariff and duty movements can change landed cost after funding. The facility depends on a single national end customer honouring the orders. The SBA subordination is scoped to specific orders, so every new order cycle raises the question of whether the carve-out still covers it. Reconciling which assets sit under which lien, order by order, is an operational burden that does not scale on spreadsheets. Opportunities: PO finance is the highest-yielding, shortest-duration corner of trade working capital, and referral flow from ABL lenders who cannot fund the pre-shipment gap is a durable origination channel. For managers, short-duration self-liquidating exposure with defined collateral is attractive precisely because it is operationally demanding, which keeps competition out. **SouthStar Capital: $3 million invoice factoring facility to a Charlotte stud welding company** SouthStar Capital closed a $3 million invoice factoring facility for a family-owned Charlotte stud welding business serving steel fabricators. Founded in 2018, the company was taking on larger structural steel projects, including data centre developments, and the gap between project completion and customer payment was creating pressure on payroll and operating costs. The referral came from an existing SouthStar client doing similar work. Why it matters: Trace the collateral. A $3 million factoring line in North Carolina is funding the steel that goes into data centres. The same capital expenditure cycle that produces billion-dollar syndicated facilities for power developers arrives at the bottom of the market as a receivables timing problem at a family firm. Anyone modelling thematic exposure to AI infrastructure across a private credit book will find it distributed across positions that carry no thematic tag at all. Key risks: Construction receivables carry retainage, pay-when-paid terms and lien rights that complicate collection and materially change effective duration. Obligor concentration among a handful of fabricators is likely. Project delay pushes out the entire collection cycle. A business founded in 2018 has limited history through a downturn, and growth into larger projects means larger single invoices against the same equity base. Opportunities: Construction-adjacent factoring benefits from the infrastructure build cycle with weekly visibility into obligor payment behaviour, which is better real-time data than most credit strategies get. Client referral flow is the cheapest origination channel in factoring and tends to bring pre-screened credit. **Commercial Finance Partners: $750,000 receivables facility to a St. Louis security services company** Commercial Finance Partners closed a $750,000 accounts receivable purchase facility for a St. Louis contract security services company that had just completed a change-of-ownership acquisition financed with an SBA 7(a) loan. The acquisition was a second-generation family transition, with the incoming owner buying the business from his father and his father’s partner. The referral came from the SBA lender itself, which wanted a working capital facility in place at closing rather than see the borrower carry more term debt. Because that lender held a blanket first lien on all business assets, the transaction required an intercreditor agreement subordinating accounts receivable and inventory to CFP while preserving the SBA lender’s first position on everything else. CFP executed the subordination concurrently with the SBA closing and funded the first advance from its own balance sheet the day after documents were signed. Why it matters: This is the smallest deal of the week and the most structurally involved. A $750,000 facility required a negotiated intercreditor agreement, a collateral carve-out from a government-guaranteed blanket lien, and simultaneous execution with a separate closing. Ten years ago that level of engineering did not happen below $10 million. It does now. Intercreditor complexity has moved decisively down-market, and the operational cost of administering these facilities has moved with it. Key risks: Contract security is a labour business with thin margins and heavy payroll timing pressure, where a single contract loss removes a large share of the receivables base. Ownership has just transitioned, so management depth is unproven. The carve-out only covers receivables and inventory, so any dispute over what constitutes proceeds of receivables becomes a live intercreditor question. Small-obligor concentration is likely. Opportunities: SBA lenders are a structurally underused referral channel for working capital providers, because the guaranteed loan cannot flex to working capital needs and the lender would rather introduce a partner than watch the credit deteriorate. Lenders who can execute an intercreditor quickly and fund from their own balance sheet win business on certainty of execution, not on rate. **SouthStar Capital: $500,000 receivables facility to a mining supply company** SouthStar Capital provided a $500,000 accounts receivable financing facility to a newly established mining supply company distributing tungsten carbide mining bits to commercial mining operations. The facility bridges the gap between delivery and customer payment. Why it matters: This is what day-one credit looks like for a company with no trading history. There is no balance sheet to lend against, so the underwriting is entirely about the credit quality of the mining operators receiving the goods. The obligor, not the borrower, is the risk. Key risks: A newly established distributor has no dilution history, no verification baseline and no evidence of how disputes are handled. Concentration will be extreme at this size. Mining capital spending is commodity-price sensitive and can stop abruptly. Consumable mining products carry low resale value if goods come back. Opportunities: Early-stage receivables finance is high margin and builds a data history that becomes the basis for a larger facility later. The lender who is there at $500,000 usually holds the relationship at $5 million. **Takeaways** 1. **Incumbency is the competitive set.** Six of eight deals last week involved a lender who was already in the structure. Three were refinanced out, three were subordinated or carved around. Origination in this market is not about finding borrowers. It is about being able to execute against an existing lien quickly and credibly. 2. **Intercreditor engineering has moved down-market.** A $750,000 receivables facility and a $7.5 million purchase order line both required subordinations from an SBA lender. Structural complexity is no longer a function of deal size, and the administrative cost of a small facility is now closer to that of a large one. 3. **Availability is winning mandates, not price.** MidCap won by releasing availability the prior lender had constrained. Ares won by underwriting a borrowing base rather than an earnings profile. In both cases the collateral was unchanged and the eligibility logic was not. 4. **Banks and private credit are co-existing on the same borrower.** Lifetime Brands now has a JPMorgan-agented revolver and a Pathlight second lien maturing on the same date. This is no longer displacement. It is a division of collateral, and it requires both lenders to monitor overlapping assets under different definitions. 5. **Specialty credit has absorbed the assets banks stepped away from.** Transitional office in Phoenix, perishable inventory in Illinois, pre-shipment trade risk in the Midwest. None of these were financed by a bank last week. 6. **Thematic exposure does not arrive with a label.** A $3 million factoring facility in Charlotte is data centre exposure. Sector tags in vendor data will not find it, and neither will a quarterly report. **How Cardo AI helps Asset Managers and Lenders** Every deal above creates the same operational requirement, whether it is $60 million or $500,000: collect, normalise, monitor and report on loan and collateral data, continuously, across structures that do not resemble each other. Last week’s deals make the point precisely. One borrower now carries a first-lien ABL and a second-lien term loan against overlapping collateral. Another carries an SBA blanket lien with receivables and inventory carved out to a separate funder. A third has a purchase order facility whose priority is scoped to named customer orders. There is no single collateral position in any of these credits that can be read off a statement. Cardo AI is the operating system for that problem: - Loan tape ingestion across any source or format, so a factoring book, a CRE first mortgage and a syndicated revolver sit in one data model rather than three spreadsheets - Borrowing base, eligibility and waterfall computation, run continuously rather than reconstructed monthly, so availability is a live number and not a lagging one - Intercreditor and collateral carve-out tracking, so priority by asset class, by obligor and by transaction is explicit and auditable - Concentration, covenant and trigger monitoring, with breaches surfaced when they happen - Portfolio analytics and stress testing, including look-through to underlying obligors so thematic exposure is visible even when the sector tag is wrong - Institutional-grade investor reporting, generated from the same source of truth the risk team uses - Multi-asset-class onboarding, from commercial real estate to trade finance to lender finance The lenders who won mandates last week did it by understanding the collateral better than the incumbent. That is an analytics advantage before it is a credit judgement. **Sources** *[ABL Advisor deal tables](https://www.abladvisor.com/deal-tables) (September 7-11, 2026); company press releases via [Business Wire](https://www.businesswire.com/news/home/20260625720357/en/), [GlobeNewswire](https://www.globenewswire.com/news-release/2026/06/29/3318886/0/en/), [PR Newswire](https://www.prnewswire.com/news-releases/news-releases-list/) and [Bloomberg;](https://www.bloomberg.com/) [ABF Journal](https://www.abfjournal.com/category/news/); and issuer investor relations pages.* If you’re scaling an asset-based or private credit book, [we should talk. ](https://cardoai.com/company/contact-us/) --- ### [Weekly Deal Roundup | June 22–25, 2026](https://cardoai.com/article/weekly-deal-roundup-june-22-25-2026/) **Published:** June 29, 2026 **Author:** GiadaCardo26 **Content:** Contents [1. Energy scales, biotech borrows, the tail compounds](#introduction) [2. $400M Hercules Capital](#first-par) [3. $325M Spearmint Energy](#second-par) [4. $300M Terawatt Infrastructure](#third-par) [5. $125M M&T Bank](#fourth-par) [6. $33M Wingspire Capital](#fifth-par) [7. The specialty tail: ABL at every scale ](#sixth-par) [8. Takeaways](#seventh-par) [9. How Cardo AI helps Asset Managers and Lenders](#eight-par) **Energy scales, biotech borrows, the tail compounds** Thirteen deals printed last week and together they moved roughly $1.2 billion. The capital did not spread evenly, and that is the story. Three energy and infrastructure financings accounted for about $750 million. A single life-sciences term loan carried another $400 million of capacity. Everything else sat in a long tail of working-capital facilities, most of them under $3 million. That barbell shape, heavy at both ends and light in the middle, is asset-based finance in miniature. Big institutional tickets chasing contracted, long-duration cash flows on one side. Granular, self-liquidating collateral funding the real economy on the other. Same market, two very different underwriting jobs. Here are the deals that mattered and what each one signals. **$400M Hercules Capital expands Dyne Therapeutics’ non-dilutive term loan** Hercules Capital expanded its non-dilutive senior secured term loan to Dyne Therapeutics to up to $400 million of total capacity. Dyne is a clinical-stage biotech advancing treatments for two genetically driven neuromuscular diseases, Duchenne muscular dystrophy and myotonic dystrophy type 1. Around $200 million has been drawn, with a similar amount still available across milestone-linked tranches. Why it matters: This is venture debt doing what equity used to do. Dyne funds two potential US launches without issuing shares into a volatile biotech tape, and Hercules holds a senior secured position with milestone gates that release capital only as the science de-risks. The structure, not the headline number, is the innovation. Key risks: The exposure is binary. A clinical or regulatory miss can impair a credit that looks healthy on every financial metric, and the collateral is largely intangible. Runway, cash burn and milestone timing all sit close to the centre of the underwriting. Opportunity: Premium yield and a senior secured claim on companies that, a few years ago, would have had to dilute shareholders to fund the same runway. For managers who can diligence the science, life-sciences debt offers returns that are hard to find elsewhere in senior credit. **$325M Spearmint Energy clubs a facility for grid-scale battery storage** A club of Nuveen Energy Infrastructure Credit, Elda River Capital Management, Harrison Street Asset Management and Aiga Capital Partners provided Spearmint Energy a $325 million debt facility. Spearmint builds and operates grid-scale battery storage in the ERCOT market in Texas, and the capital backs both operating assets and a development pipeline. Why it matters: Institutional credit is no longer dabbling in the energy transition. Nuveen turns up here and recently put preferred tax equity into community-solar developer Solar Landscape as well. Credit platforms are building concentrated, repeat exposure to storage and distributed solar through private structures rather than public markets. Key risks: Construction and completion risk, dependence on the investment tax credit regime, the split between merchant and contracted revenue, and battery degradation over the asset’s life. These are long-duration assets, so they carry real rate sensitivity too. Opportunity: Long-dated, often contracted and inflation-linked cash flows that match insurance and pension liabilities better than almost anything else on offer. Storage is becoming the connective tissue of a grid under strain from data-centre demand and intermittent renewables. **$300M Terawatt Infrastructure closes its first commercial bank facility** Terawatt Infrastructure closed its first commercial bank facility, up to $300 million over five years, structured by RBC Capital Markets with SMBC and UBS in the syndicate. Terawatt builds and operates charging depots for electric and autonomous fleets and counts Waymo among its customers. Why it matters: The facility is senior secured, with a committed tranche and an incremental option on top. A first bank facility for a category like this is a signal in itself. Fleet-charging infrastructure is being underwritten as an emerging asset class, with a bank syndicate rather than venture capital carrying the risk. Key risks: Buildout and permitting friction, counterparty and customer concentration, the utilisation ramp as fleets electrify, and the J-curve between capex today and revenue later. Opportunity: Early-mover exposure to infrastructure that sits underneath two structural shifts at once, fleet electrification and autonomy. Matched-duration capital against long-life assets, with a structure that scales as the network grows. **$125M M&T Bank leads an oversubscribed revolver for Solar Landscape** M&T Bank led an oversubscribed $125 million revolving credit facility for Solar Landscape, a distributed-energy developer that builds community and commercial solar on commercial rooftops. Flagstar, Atlantic Union and Valley Bank joined, and M&T syndicated the line as the developer’s pipeline outgrew its original working-capital facility. Why it matters: The money funds projects from origination through construction readiness, which is exactly the development-stage window where capital is hardest to source. It is also a reminder that banks still compete hard where distributed-solar credit fits their box, even as they retrench elsewhere. Key risks: Development-stage execution, interconnection and permitting delays, pipeline conversion, and sensitivity to policy and the tax-credit backdrop. Opportunity: A revolving structure that lets the developer keep multiple projects moving at once, and lets lenders take exposure to distributed generation with bank-grade structure and syndication. **$33M Wingspire Capital backs a secure communications platform** Wingspire Capital provided a $33 million first-out revolver and term loan to a secure communications and computing business, a defence-adjacent credit. Why it matters: The first-out structure carves up risk inside a single facility, allocating the safest dollars to the first-out lender and leaving room for a last-out tranche to price for more risk. It is a clean example of how structure, not just collateral, drives the risk profile in a single deal. Key risks: Customer and contract concentration typical of specialist technology and defence suppliers, and the usual borrowing-base and covenant exposures inside a leveraged ABL structure. Opportunity: Mid-market exposure with structural seniority and pricing that reflects where the lender sits in the waterfall. **The specialty tail: ABL at every scale** Underneath the headline tickets, the bread-and-butter of asset-based lending kept turning. Beacon Bank and Stream Finance backed Capstone Building Supplies with an $11.5 million revolver. SixCap Healthcare Finance lent $3 million against a skilled-nursing portfolio. Culain Capital Funding put $2.5 million of accounts-receivable financing behind a materials distributor. JPalmer Collective backed Rambler Sparkling Water with a $2.5 million asset-based working-capital line. Amerisource Business Capital funded a fishing-gear retailer with a $2.5 million working-capital and cash-flow facility. SouthStar Capital appeared twice, with $0.8 million of A/R financing for a home healthcare provider and $0.6 million of purchase-order and A/R financing for a government contractor and manufacturer. National Business Capital opened a $0.2 million flex line for a coffee retailer. Why it matters: This is where the asset class actually lives. Small individually, but in aggregate it is the quiet backbone of working capital across distribution, retail, beverage, building supplies and healthcare receivables. With banks still selective on smaller credits, specialist lenders are filling the gap and pricing it well. Key risks: Collateral dilution, receivables fraud, customer concentration and the seasonality baked into consumer and retail borrowers. A fishing-gear retailer, a coffee brand and a sparkling-water company all carry demand patterns that move the borrowing base around through the year. Opportunity: Diversification, self-liquidating collateral and genuine pricing power, precisely because bank retrenchment has left room for specialist lenders to underwrite credits the big balance sheets have stepped back from. **Takeaways** Four signals worth carrying into the week ahead. 1. Bank syndicates are bifurcating: They are walking away from plenty and competing hard where they fit. The Solar Landscape and Terawatt deals show where banks still want to lead. 2. Non-dilutive debt is reshaping life sciences: Milestone-gated term loans are letting clinical-stage companies fund launches without diluting equity, and lenders are getting paid for the binary risk. 3. Institutional credit is going deep on the energy transition: The same names keep showing up across storage and distributed solar, building concentrated, repeat exposure through private structures. 4. The specialty tail compounds: From a $33 million first-out revolver to an $800k healthcare receivables line, the small deals are where the volume quietly accumulates, and where data discipline decides who scales. **How Cardo AI helps Asset Managers and Lenders** From a $400 million milestone-gated term loan to an $800k receivables line, the operational requirement is identical. Collect, normalise, monitor and report on loan and collateral data, continuously, across wildly different asset types. That is the operating system Cardo AI was built to be. - Loan tape ingestion across any source or format - Borrowing base, eligibility and waterfall computation - Concentration, covenant and trigger monitoring - Portfolio analytics and stress testing - Institutional-grade investor reporting - Multi-asset-class onboarding, from battery-storage facilities to skilled-nursing portfolios As the deals get more varied, the managers who can actually see their portfolios, in close to real time and across every collateral type, are the ones who keep scaling. If your monitoring has not kept pace with your origination, that is a conversation worth having. **Sources** *[ABL Advisor deal tables](https://www.abladvisor.com/deal-tables) (June 22–25, 2026); company press releases via [Business Wire](https://www.businesswire.com/newsroom), [GlobeNewswire](https://www.globenewswire.com/newsroom), [PR Newswire](https://www.prnewswire.com/news-releases/news-releases-list/) and [Bloomberg;](https://www.bloomberg.com/) [ABF Journal](https://www.abfjournal.com/category/news/); and issuer investor relations pages.* If you’re scaling into new asset classes or just tired of stitching data together in spreadsheets, [book a demo.](https://cardoai.com/book-a-demo/) --- ### [Weekly Deal Roundup | June 15–19, 2026](https://cardoai.com/article/weekly-deal-roundup-june-15-19-2026/) **Published:** June 22, 2026 **Author:** GiadaCardo26 **Content:** Contents [Clarus Capital leases to an infrastructure services company](#first-par) [First Business Bank backs CarParts.com](#second-par) [Post Road funds a metal components manufacturer](#third-par) [Salem Five blends a revolver with real estate](#fourth-par) [The specialty tail: $1.0M to $8M](#fifth-par) [Takeaways ](#sixth-par) [How Cardo AI helps Asset Managers and Lenders](#seventh-par) **Nine deals closed in three days. Just under $100M of fresh capital, spread across receivables, equipment, inventory and real estate.** A $30M infrastructure lease and a $25M auto-parts revolver led the week. Underneath them, a wave of smaller asset-based facilities financed apparel, food distribution, home décor, packaged goods and a shrink wrap distributor. Most of these borrowers stayed undisclosed. That is the asset class doing what it does best. The collateral carries the credit, not the name on the file. Here is the week, deal by deal, with what each one means for lenders and asset managers, and the risks worth watching. **Clarus Capital leases to an infrastructure services company** A $30M lease facility from Clarus Capital to an undisclosed infrastructure services company. **Why it matters:** This is the largest ticket of the week, and it is a lease rather than a loan. The structure keeps the equipment on the financier’s side of the ledger and ties repayment to assets that earn while they sit on a contract. Hard-asset leasing into infrastructure has become a reliable home for private capital that wants contracted, equipment-backed cash flow rather than pure corporate credit. **Key risks to monitor:** Residual value at lease end. Re-marketability of specialised kit that few buyers want. Lessee credit and the strength of the underlying service contracts. Utilisation and renewal, since an idle asset stops paying for itself. **First Business Bank backs CarParts.com** A $25M revolving credit facility from First Business Bank to CarParts.com. **Why it matters:** Consumer auto parts is a working-capital business at heart. A revolver sized to receivables and inventory lets the borrower breathe with the demand cycle and draw when it needs to. For the lender, the appeal is a borrowing base that resets continuously rather than a fixed term exposure, so availability tracks the collateral in close to real time. **Key risks to monitor:** Inventory obsolescence in a fast-moving parts catalogue. Receivables dilution from returns and credits. Sensitivity to consumer demand and discretionary spend. Eligibility and advance-rate drift inside the borrowing base. **Post Road funds a metal components manufacturer** A $20M capital lease from Post Road Equipment Finance to an undisclosed metal components manufacturer. **Why it matters:** Capital leases let a manufacturer fund plant and machinery without drawing down the working-capital line. The two facilities can then do different jobs, equipment finance for the long-lived assets and a revolver for the day-to-day. For the lender, the equipment is the collateral and the lease schedule is the repayment plan, a clean structure when the kit holds its value. **Key risks to monitor:** Residual value on specialised plant. Slow re-marketability if the asset has to be redeployed. Lessee concentration. Cyclicality in the manufacturing end market, which can hit utilisation fast. **Salem Five blends a revolver with real estate** A $10M revolving line of credit plus commercial real estate financing from Salem Five Bank to Concord Building & Design. **Why it matters:** This is two collateral types in one relationship. The revolver flexes with trading. The CRE piece sits against property. That combination is efficient for the borrower, and it tells you something about where bank-led ABL is heading, packaging working-capital and asset-backed real estate lines together rather than financing them in silos. **Key risks to monitor:** Two different monitoring cadences under one facility, one fast-moving and one slow. Real estate valuation and interest-rate sensitivity on the CRE side. Borrowing-base discipline on the revolver. The risk that attention drifts to the larger property exposure while the working-capital line quietly deteriorates. **The specialty tail: $1.0M to $8M** **Key terms:** Five smaller facilities rounded out the week. TAB Bank provided an $8M working capital facility to Optimum Foods in food distribution on 17 June. Sallyport Commercial Finance arranged a $3.5M A/R finance facility for an undisclosed apparel company on 15 June and a $1.0M A/R line for a consumer packaged goods company on 16 June. Celtic Capital Corp wrote a $1.1M facility for a shrink wrap distributor, split $800k against receivables and $300k against inventory, on 17 June. The Hedaya Capital Group provided a $1.0M factoring facility to a pillow and home décor importer on 16 June. **Why it matters:** This is the quiet backbone of asset-based finance. Small tickets, granular collateral, and the structures that keep working capital moving for businesses banks often pass over on a name-only basis. Food distribution sits at the defensive end, with steady demand, quick turns and short-dated receivables, a useful counterweight to the consumer-discretionary names elsewhere in the book. The apparel and consumer-goods receivables show how factoring and A/R lines fund inventory-heavy, seasonal trading. The Celtic deal is a neat miniature of a blended base, receivables and inventory advanced at different rates inside a single small facility. **Key risks to monitor:** Customer concentration, from a handful of large buyers in food distribution to single-name exposure across the receivables lines. Dilution from returns and credits. Returns and seasonality in apparel and home goods. Thin distribution margins that leave little room for error. Inventory valuation and obsolescence on the Celtic stock advance, modest for a commodity-style product but still dependent on accurate, current data. And the operational reality that a $1M facility is only economic to monitor if you are not doing it by hand. **Takeaways** - Collateral diversity is the baseline, not the exception. In a single week the market financed equipment, receivables, inventory and real estate, with no standard template across nine deals. Each structure carries its own source of repayment and its own thing to watch. - The edge has moved past the close. Origination is no longer the differentiator. Borrowing-base accuracy, covenant discipline and reporting speed are. The lenders who see their collateral most clearly, most often, are the ones who can hold advance rates with confidence as a book ages. - The long tail has to pay for itself. A $1M factoring line deserves the same monitoring rigour as a $30M lease. The managers who can deliver that rigour at a fraction of the cost are the ones who can profitably chase the granular deals others leave on the table. - Defensive and cyclical sit side by side. Food distribution on one end, apparel and auto parts on the other. A manager holding several of these wants to recognise the correlations before the cycle does. **How Cardo AI helps Asset Managers and Lenders** Every facility on this week’s table produces the same raw material. An asset tape. A borrowing base certificate. An ageing report. A lease schedule. They arrive as documents, in different formats, on different cadences. Turning them into structured, comparable, portfolio-level data is the unlock, and for most teams it is also the bottleneck. That is the problem Cardo AI was built to solve. We ingest the messy reality of asset-based and structured finance and turn it into one canonical data model your team can act on. Document intelligence on the way in, so the tape and the certificate become clean data without manual rekeying. A clear portfolio view on the way out, so availability, eligibility and covenant status are visible continuously rather than once a quarter. For a lender, that means catching dilution and eligibility drift early, while there is still time to act. For an asset manager, it means a look-through view across facilities and collateral types, so concentration and correlation are managed at the portfolio level. And for both, it means the small facility becomes as easy to monitor as the large one, which is what makes scaling the long tail worthwhile. From a $30M infrastructure lease to a $1M factoring line, the operational job is identical. Ingest the data. Recalculate availability. Test covenants. Model cash flows. Report at institutional standard. Continuously, and at scale. That is the operating system we have built for asset-based finance and private credit. **Sources** *[ABL Advisor deal tables](https://www.abladvisor.com/deal-tables) (June 15–19, 2026); [CoreWeave](https://investors.coreweave.com/overview/default.aspx) investor relations and SEC filings; company press releases via [Business Wire](https://www.businesswire.com/newsroom), [PR Newswire](https://www.prnewswire.com/news-releases/news-releases-list/) and [Bloomberg;](https://www.bloomberg.com/) [ABF Journal](https://www.abfjournal.com/category/news/); [La Caisse](https://www.lacaisse.com/en/news) press releases and issuer investor relations pages.* If you’re scaling into new asset classes or just tired of stitching data together in spreadsheets, [book a demo.](https://cardoai.com/book-a-demo/) --- ### [Weekly Deal Roundup | June 29–July 3, 2026](https://cardoai.com/article/weekly-deal-roundup-june-29-july-3-2026/) **Published:** July 6, 2026 **Author:** GiadaCardo26 **Content:** Contents [1. Capital keeps pricing structure, not just spread](#introduction) [2. Aquila Air Capital – $400MM](#first-par) [3. 4D Molecular Therapeutics – $200MM](#second-par) [4. GALERIA – €160M](#third-par) [5. Healthcare Services – $90MM](#fourth-par) [6. Digitt – $50MM](#fifth-par) [7. College Bookstore Operator – $55MM](#sixth-par) [8. Shoulder Innovations – $50MM](#seventh-par) [9. Skilled Nursing Deals – $25.5MM](#nine-par) [10. The rest of the spectrum](#nine-par) [11. Takeaways](#ten-par) [12. How Cardo AI helps asset managers and lenders](#eleven-par) **Capital keeps pricing structure, not just spread** $1.11 billion. Fifteen transactions. Nine sectors. A warehouse refinance for an aviation lessor, a non-dilutive facility for a late-stage gene therapy company, an asset-based line for a German department store, a cross-border facility for a Mexican fintech, and a steady tail of healthcare and speciality deals that most roundups ignore. The headline is not the largest cheque. It is how many different categories of lenders competed for the same thing last week: well-structured, well-collateralised books they can value, monitor and price with confidence. Spread still matters. Structure is what got these deals done. Here are the transactions that matter, and why. **ATLAS SP to Aquila Air Capital, $400MM aviation warehouse** ATLAS SP Partners closed a $400 million refinancing and upsizing of the warehouse facility for Aquila Air Capital, the Ireland-based aviation lessor backed by funds managed by Wafra. ATLAS, the warehouse finance and securitised products business majority owned by Apollo funds, acted as structuring agent and lead lender. Aquila runs a diversified portfolio of more than 130 aircraft and engine assets across 25 lease customers. Why it matters: Aviation and equipment finance keeps upsizing while other corners of the market wait for rate clarity. A warehouse facility is designed to scale with the collateral, not the cycle. When a lessor grows its engine book, the facility grows with it, provided the assets, appraisals and cash flows are tracked with precision. This is asset-based finance in its purest form, capital sized to the value and performance of hard collateral. Key risks: Residual value risk on ageing engines and aircraft, lessee concentration and counterparty credit, and the discipline of continuous appraisal against a moving asset base. A warehouse is only as strong as the borrowing base behind it. Opportunity: For structured lenders, aviation remains a category where deep asset expertise and precise monitoring earn a premium. The lessors that can present a clean, well-tracked collateral pool get flexible structures and competitive pricing. The lenders that can monitor at asset level get to underwrite growth rather than fear it. **Hercules Capital to 4D Molecular Therapeutics, $200MM venture debt** 4D Molecular Therapeutics, a late-stage gene therapy company, entered a senior secured facility of up to $200 million with Hercules Capital. The company drew $20 million at closing, with further tranches unlocked by milestones and lender approval, and a maturity in 2031. Leerink Partners advised on the structure. Why it matters: Non-dilutive venture debt has moved from last resort to core balance-sheet strategy for late-stage life sciences. 4DMT already holds a strong cash position. It took the facility to diversify its capital structure and preserve equity while it advances its pipeline. Milestone-linked tranches mean the capital arrives as the science de-risks, aligning funding with clinical progress. Key risks: Clinical and regulatory binary outcomes, cash burn against milestone timelines, and minimum liquidity and market-capitalisation covenants that tie funding availability to performance. The structure protects the lender, but it needs constant covenant and trigger tracking. Opportunity: Venture and growth debt lenders are underwriting an expanding pipeline of biotech and medtech names that want capital without dilution. The winners are the platforms that can monitor covenants, milestones and cash runway continuously, and act early when a trigger is approaching rather than after it breaches. **Gordon Brothers to GALERIA, €160M cross-border retail ABL** Gordon Brothers provided the German department store group GALERIA with an asset-based facility of up to €160 million, roughly $182 million, to fund its store footprint optimisation and business realignment. The structure is built around GALERIA’s inventory and real estate assets. Why it matters: Retail asset-based lending is alive precisely where the collateral can be valued and monitored. A department store in transformation is exactly the kind of complex, asset-heavy borrower that specialist ABL firms understand and that many bank syndicates will not underwrite. Advance rates move with appraised inventory and real estate value, so the facility must be re-based continuously as the business reshapes itself. Key risks: Retail turnaround execution, inventory obsolescence and seasonality, real estate valuation in a soft market, and the operational challenge of tracking a borrowing base that changes as stores close and assortments shift. Opportunity: Cross-border retail ABL rewards lenders who can appraise, advance against and monitor mixed collateral pools in real time. The transformation stories that scare generalist capital are the ones speciality lenders can price, because they can see and value the assets underneath. **Encina Private Credit to healthcare services, $90MM** Encina Private Credit acted as administrative and collateral agent on a $90 million senior secured facility for an undisclosed healthcare services business, structured as a $10 million revolver and an $80 million term loan. Why it matters: Healthcare is one of the most consistent categories in US speciality credit, and it rarely makes headlines. A revolver plus term loan structure funds both working capital and growth in a single facility, with the agent role placing Encina at the centre of collateral control and reporting. Key risks: Reimbursement and regulatory exposure, receivables quality and payer concentration, and covenant headroom across a blended revolver and term structure. Opportunity: Healthcare services financing rewards lenders who can normalise messy operating and collateral data into a clean, monitorable borrowing base. The agent that controls the data controls the relationship. **Victory Park Capital to Digitt, $50MM cross-border lender finance** Victory Park Capital provided a $50 million asset-backed facility to Digitt, a Guadalajara-based fintech that refinances high-interest credit card debt for prime borrowers in Mexico into fixed-rate installment loans. VPC, headquartered in Chicago and a majority-owned affiliate of Janus Henderson, specialises in private credit and asset-backed finance. Why it matters: Lender finance is crossing borders. Every dollar advanced to a speciality originator like Digitt enables multiples of downstream lending. The facility is asset-backed, secured by the underlying loan book, so performance depends on the quality of Digitt’s underwriting and servicing, not just its growth ambitions. Key risks: Double leverage and exposure to both the originator and its end-borrower pool, currency and macro risk in an emerging market, and transparency into a consumer book that sits one layer removed from the lender. Opportunity: Private credit is funding fintech origination well beyond US borders. The edge belongs to lenders who can ingest and monitor an originator’s loan tape at end-borrower level, tracking concentration, delinquency and performance continuously rather than trusting a monthly summary. **Mountain Ridge Capital to a college bookstore operator, $55MM** Mountain Ridge Capital provided a $55 million senior secured credit facility to an undisclosed college bookstore operator. Why it matters: Retail ABL for a seasonal, inventory-heavy operator is classic speciality territory. Bookstore economics swing hard around academic calendars, which makes a well-sized, well-monitored borrowing base essential rather than optional. Key risks: Seasonality and inventory turn, structural pressure on physical retail, and advance-rate discipline against collateral that moves quickly. Opportunity: Lenders who can track inventory eligibility and seasonality in real time can lend confidently into cash-flow patterns that scare generalist capital. **Stifel Venture Banking to Shoulder Innovations, $50MM medtech venture debt** Stifel Venture Banking provided Shoulder Innovations, a medical device company, with a $15 million term loan, a $30 million undrawn line of credit and a $5 million accordion, $50 million of total capacity. Why it matters: Medtech joins biotech in the non-dilutive venture debt story. A staged structure, drawn term loan plus undrawn capacity plus accordion, lets a growth-stage device company fund commercialisation without giving up equity, and gives the lender optionality tied to performance. Key risks: Commercialisation execution, regulatory and reimbursement pathways for devices, and cash runway against undrawn capacity that depends on milestones. Opportunity: Venture lenders who can monitor drawdown conditions, covenants and cash runway continuously can support the full commercial journey rather than a single tranche. **The healthcare and skilled nursing tail** Three more healthcare deals compounded quietly last week. Cambridge Realty Capital arranged a $21.5 million bridge-to-HUD first mortgage for a skilled nursing facility, financing the gap to permanent government-backed funding. SixCap Healthcare Finance closed two skilled nursing facilities in the same week, a $2 million asset-based facility and a $2 million revolving line of credit. Why it matters: Skilled nursing bridge and asset-based lending is among the most under-discussed durable categories in US speciality credit. It is granular, collateral-rich and relentless in volume, week after week. Key risks: Reimbursement and regulatory exposure, facility-level operating performance, and the transition risk inherent in bridge-to-HUD structures. Opportunity: Consistency compounds. Lenders and asset managers who can onboard and monitor many small, collateral-backed healthcare facilities at scale build a durable, diversified book that most of the market overlooks. **The rest of the spectrum** Nine sectors, one week. Alongside the deals above, Rosenthal Capital Group and FGI Finance backed an undisclosed luxury womenswear brand with a $25 million asset-based facility and a $4 million term loan. Wells Fargo provided a $25 million revolving credit facility to consumer health-tech company Owlet. Republic Business Credit funded a $2 million e-commerce ABL facility for an Amazon third-party seller. Franklin Capital arranged a $3 million factoring facility for a global consumer brand and logistics company. Southstar Capital provided an $0.8 million accounts receivable line, in this table an $0.4 million facility, for a commercial printing and embroidery business. Different lenders, different scales, different sectors. The same operational discipline underneath every one. **Takeaways** - Non-dilutive venture debt is now default infrastructure for late-stage biotech and medtech, not a fallback. Hercules and Stifel both funded innovation last week without touching equity. - Structured asset finance keeps upsizing. ATLAS SP’s warehouse refinance for Aquila shows capital chasing quality collateral through the cycle rather than waiting for it. - Lender finance is crossing borders. Victory Park’s facility for Mexico’s Digitt shows private credit funding speciality origination well beyond the US. - Healthcare asset-based finance stays the quiet compounder. Encina, Cambridge Realty and SixCap prove the category delivers durable, collateral-rich volume every single week. - And across all of it, the market is pricing structure, not just spread. The lenders winning these deals are the ones who can see, value and monitor the collateral underneath. **How Cardo AI helps asset managers and lenders** From a $400 million aviation warehouse to an $800,000 working capital line, the operational requirement is identical. Collect, normalise, monitor and report on loan and collateral data, continuously, at scale. That is the operating system we have built. We ingest loan tapes across any source or format, so a rail lessor, a skilled nursing portfolio and a Mexican consumer loan book all live in one place. We compute borrowing base, eligibility and waterfall automatically, so advance rates re-base as the collateral moves. We monitor concentration, covenants and triggers in real time, so a breach is a signal you act on early, not a surprise you find late. We run portfolio analytics and stress testing across asset classes, and we produce institutional-grade investor reporting on the same clean data. The result is simple. Lenders underwrite growth instead of fearing it, and asset managers report with confidence instead of chasing spreadsheets. Whatever the asset class, the discipline is the same, and so is the infrastructure. **Sources** *[ABL Advisor deal tables](https://www.abladvisor.com/deal-tables) (June 29–July 3, 2026); company press releases via [Business Wire](https://www.businesswire.com/news/home/20260625720357/en/), [GlobeNewswire](https://www.globenewswire.com/news-release/2026/06/29/3318886/0/en/), [PR Newswire](https://www.prnewswire.com/news-releases/news-releases-list/) and [Bloomberg;](https://www.bloomberg.com/) [ABF Journal](https://www.abfjournal.com/category/news/); and issuer investor relations pages.* If you’re scaling an asset-based or private credit book, [we should talk. ](https://cardoai.com/company/contact-us/) --- ### [Weekly Deal Roundup | July 6 –10, 2026](https://cardoai.com/article/weekly-deal-roundup-july-6-10-2026/) **Published:** July 13, 2026 **Author:** GiadaCardo26 **Content:** Contents [1. Where the capital flowed](#introduction) [2. Eclipse: $300M ABL Refinance](#first-par) [3. Voyager: $250M Defense Facility](#second-par) [4. Wingspire: $45M Medical ABL](#third-par) [5. The quiet engine](#fourth-par) [6. Takeaways](#fifth-par) [7. How Cardo AI helps Asset managers and Lenders](#sixth-par) **Where the capital flowed: the week in private credit & asset-based finance** Last week produced roughly $620.5 million of announced private credit and asset-based finance activity across seven transactions and the most revealing part isn’t the headline number. It’s the direction of travel. Two of the week’s largest facilities refinanced borrowers out of bank debt and into non-bank ABL structures (one directly off J.P. Morgan), both marquee deals carried accordion features that price optionality straight into the capital stack, and a quiet tail of working-capital facilities quietly did the compounding that specialty finance is actually built on. **Eclipse Business Capital $300M ABL for ProFrac, refinanced off J.P. Morgan** Eclipse Business Capital provided a $300 million asset-based revolving credit facility to ProFrac Holding Corp (NASDAQ: ACDC), a technology-driven energy-services company. The new line replaced ProFrac’s prior $275 million ABL facility that had been arranged by JPMorgan Chase, upsized the commitment, extended the maturity to July 2030, and added a $25 million accordion, with Eclipse acting as agent, collateral agent, lead arranger and book runner. Why it matters: This is the private-credit thesis compressed into one transaction: an independent, non-bank ABL platform winning a large, public-company mandate away from a money-center bank. It’s not distress, it’s a borrower choosing more flexible, collateral-driven capital and a longer runway. For allocators, it’s a live data point on how far into the large-corporate arena specialty ABL has moved. Key risks: Oilfield services is one of the more cyclical corners of the market. Revenue, receivables quality and equipment values all track a volatile commodity cycle, and a borrowing base built on receivables, inventory and specialized frac equipment can contract quickly in a downturn. Fleet utilization, customer concentration and collateral valuation cadence are the numbers that decide outcomes here. Opportunities: An asset-based structure gives strong collateral coverage and downside protection for the spread on offer, and the refinancing dynamic, banks stepping back from cyclical energy exposure, is exactly the gap specialty lenders are positioned to fill. Disciplined, high-frequency borrowing-base monitoring turns that cyclicality from a threat into a manageable, well-priced risk. **Voyager Technologies $250M J.P. Morgan-led facility, accordion to $400M** Voyager Technologies (NYSE: VOYG), a Denver-based defense and space company, closed an upsized $250 million credit facility led by J.P. Morgan. A Fourth Amendment to its existing agreement lifted commitments by $50 million (from $200 million to $250 million) and preserved an uncommitted accordion of up to a further $150 million, a potential total facility of $400 million. Proceeds support liquidity across the company’s space, defense and national-security portfolio. Why it matters: This is the counterpoint to the Eclipse story: where a bank fits the growth narrative, it competes hard. Defense and space have become a scale credit category that money-center balance sheets want exposure to, and the structure shows how optionality is now embedded directly into deals, the accordion lets the borrower add capacity without renegotiating from scratch. Key risks: The credit is tied to government budgets, program execution and long revenue-conversion cycles, with meaningful counterparty concentration in government end-customers. Voyager is also a growth-stage borrower carrying widening losses and finite cash runway, which makes covenant design at least as important as headline spread. Opportunities: Durable secular tailwinds, a marquee lead bank that de-risks diligence, and a liquid, syndicated structure make this an accessible way for managers to gain exposure to the defense-tech theme through participations. The lending edge is understanding contract backlog and milestone-based revenue rather than hard-asset collateral. **Wingspire Capital $45M revolver for a global spinal-surgery device maker** Wingspire Capital provided a $45 million revolving credit facility to a sponsor-backed, global medical-device maker specializing in spinal-surgery products, refinancing the company’s previous capital structure and adding liquidity for growth. Wingspire is a portfolio company of Blue Owl Capital, a reminder of how much institutional weight now sits behind “specialty” lending. Why it matters: Medical devices are one of the most defensive, least cyclical corners of the credit market, and this is a clean, growth-oriented refinancing rather than a rescue. The deal also underlines a structural theme: the capital behind mid-market ABL is increasingly the same institutional capital that funds large private credit, the distinction between “specialty” and “mainstream” is largely one of ticket size. Key risks: Regulatory and reimbursement dynamics, single-product-line concentration, and inventory and R&D build ahead of revenue all shape the collateral. Demand is durable, but FDA and payer decisions can move the risk profile faster than the top line. Opportunities: Resilient, recurring-revenue-style end demand makes this a lower-beta, stabilizing asset within a diversified private-credit book. A revolver structure keeps the lender close to the collateral while giving the borrower room to grow. **The quiet engine: four working-capital facilities that do the compounding** Individually small, collectively instructive, these four deals map the entire working-capital lifecycle, from funding an order before it’s built to advancing against the invoice after it ships. **Entrepreneur Growth Capital** **$15M ABL line** (consumer products). EGC closed a $15 million asset-based line for a 50-year-old, family-owned Wisconsin consumer-products company navigating management turnover and “lender fatigue,” with availability against receivables, inventory, equipment and owned real estate. - Why it matters: ABL is the natural home for turnaround and transition situations that banks exit. - Key risks: execution risk on the turnaround, retail-channel concentration, inventory obsolescence. - Opportunities: multi-collateral coverage and a high-touch structure that can reprice as the business stabilizes. **TradeCap Partners $5M purchase-order funding** (sporting goods). Introduced by the company’s existing senior lender, TradeCap funded 100% of the direct cost of goods so a U.S. recreational sporting-goods manufacturer could fulfill large orders for national big-box retailers, underwritten in about ten business days. - Why it matters: PO funding finances growth that a traditional line can’t, without diluting the owner. - Key risks: performance and completion risk, seasonality, supplier and delivery dependence, single-order concentration. - Opportunities: short-duration, self-liquidating exposure with strong structural control and high velocity. **Amerisource Business Capital $4.5M working-capital facility** (industrial services). Amerisource refinanced a family-owned Michigan pipe-fabrication and millwrighting firm’s bank debt and added working capital, secured by receivables, inventory and commercial real estate. - Why it matters: another borrower re-banking from a traditional lender into a more flexible ABL structure. - Key risks: industrial end-market cyclicality and project-timing swings. - Opportunities: collateral-rich, granular exposure with real-asset support. **Culain Capital $1M accounts-receivable financing** (staffing). Culain converted eligible receivables into immediate liquidity for a California staffing company, funding payroll obligations that fall due long before customer invoices are paid. - Why it matters: staffing lives or dies on payroll timing; A/R financing is the precise tool for that gap. - Key risks: dilution, obligor concentration, verification risk, and sensitivity to the labor cycle. - Opportunities: high-advance, fast-turning collateral with frequent repricing and tight monitoring. **Takeaways** - **Borrowers are re-banking into non-banks.** Multiple facilities last week refinanced away from bank debt, including a $300M public-company ABL taken directly off JPMorgan. Specialty lenders are winning on flexibility and speed, not just filling gaps banks leave behind. - **Where the growth story fits, banks still compete hard.** J.P. Morgan leading Voyager’s upsized defense facility shows the market is bifurcating, not simply shifting one way. - **Optionality is being priced into the structure.** Accordion features on both marquee deals (Voyager to $400M, Eclipse to $325M) give borrowers capacity without commitment and give lenders a longer relationship. - **“Specialty” is institutional.** Blue Owl behind Wingspire, a large credit platform behind Eclipse, a hedge fund behind Culain, a bank behind TradeCap. The tail is not small money. - **One week spanned the full working-capital lifecycle**, purchase-order funding, inventory and working-capital lines, and accounts-receivable financing, alongside large corporate revolvers. That collateral heterogeneity is the defining operational challenge of a modern private-credit book. **How Cardo AI helps Asset managers and Lenders** The through-line across every deal above from a $300 million public-company ABL to a $1 million staffing payroll line is that the operational job is identical: collect, normalize, monitor and report on loan- and collateral-level data, continuously and at scale. As portfolios diversify across sector, structure, collateral type and ticket size, that job is where the edge (and the risk) actually lives. That’s the operating system Cardo AI provides for private credit and asset-based finance: - Loan-tape ingestion across any source or format, one source of truth from fragmented originator and servicer data. - Borrowing base, eligibility and waterfall computation automated, auditable, and refreshed as the collateral moves. - Concentration, covenant and trigger monitoring, with early-warning signals before a breach becomes a loss. - Portfolio analytics and stress testing, scenario analysis across cyclical and defensive exposures alike. - Institutional-grade investor reporting, the transparency LPs and financing partners now expect. - Multi-asset-class onboarding, from oilfield ABL and defense revolvers to purchase-order funding and staffing receivables, in one platform. In a market this heterogeneous, the managers who win aren’t just chasing the highest headline yield, they’re the ones who can see a diversifying book at the loan and collateral level, in real time, and act before the risk moves. **Sources** *[ABL Advisor deal tables](https://www.abladvisor.com/deal-tables) (July 6-10, 2026); company press releases via [Business Wire](https://www.businesswire.com/news/home/20260625720357/en/), [GlobeNewswire](https://www.globenewswire.com/news-release/2026/06/29/3318886/0/en/), [PR Newswire](https://www.prnewswire.com/news-releases/news-releases-list/) and [Bloomberg;](https://www.bloomberg.com/) [ABF Journal](https://www.abfjournal.com/category/news/); and issuer investor relations pages.* If you’re scaling an asset-based or private credit book, [we should talk. ](https://cardoai.com/company/contact-us/) --- ### [Weekly Deal Roundup | May 11–15, 2026](https://cardoai.com/article/weekly-deal-roundup-may-11-15-2026/) **Published:** May 18, 2026 **Author:** RobertaCardo26 **Content:** Contents [1. From AI factories to Pet Food](#introduction) [2. Lambda’s $1.0B Facility](#first-par) [3. Ripple Prime’s $200MM from Neuberger](#second-par) [4. Aquestive Therapeutics and Oaktree, $150MM](#third-par) [5. Phillips Pet Food, Guitar Center, Rango](#fourth-par) [6. McGrath RentCorp’s $725MM Bank Syndicate](#fifth-par) [7. Niche Specialty plays worth noting](#sixth-par) [8. Takeaways for Asset Managers and Lenders](#seventh-par) [9. How Cardo AI helps Asset Managers and Lenders](#eight-par) **From AI Factories to Pet Food** The week of May 11–15, 2026 delivered one of the more telling snapshots of the current private credit and asset-based finance market we’ve seen this quarter. Roughly $2.85 billion in announced facilities crossed the wires, spanning AI infrastructure, pharmaceuticals, crypto prime brokerage, specialty retail, pet food, modular rentals, mining services, and cold storage. The headline is no longer that private credit is growing, it’s that capital is moving toward increasingly specialized, collateral-rich, and milestone-structured transactions that look nothing like a vanilla cash-flow loan. For asset managers, allocators, and lenders trying to keep pace, the deal mix this week is a useful map of where the market is heading and where the operational and risk-management challenges are intensifying. **Lambda’s $1.0B Facility** The week’s largest transaction was J.P. Morgan’s $1.0 billion syndicated senior secured credit facility for Lambda, the AI cloud infrastructure company. The facility upsized Lambda’s prior $275 million line (originally established in August 2025), nearly a fourfold increase in roughly nine months. Why it matters for asset management - This is no longer venture-style risk. It’s a senior secured, GPU-collateralized, multi-tranche facility underwritten on contracted revenue from hyperscaler and enterprise AI customers. The financing economics – committed capital, lower blended cost of capital, oversubscribed syndicate – look much more like a traditional asset-backed lending stack than a tech loan. - The pattern is now established: GPU fleets and data center capacity are being financed like specialty equipment leasing books, with hardware as collateral and customer contracts as cash-flow support. Key risks to monitor - Technology obsolescence: Next-generation NVIDIA accelerators ship on aggressive cycles; collateral value can compress quickly. Underwriting must price residual value carefully. - Customer concentration: Hyperscaler-heavy revenue books are high quality but concentrated. - Power and siting risk: Gigawatt-scale buildouts depend on grid availability and permitting timelines that have begun to slip in several US markets. Opportunity - Lenders willing to build the data and monitoring infrastructure to track GPU residual values, contract performance, and power utilization in near real-time will dominate this asset class for the next cycle. This is exactly the type of deal where a granular, data-driven loan management platform is no longer optional. **Ripple Prime’s $200MM from Neuberger** Neuberger Specialty Finance provided Ripple a $200 million asset-based debt facility to expand margin financing capacity at Ripple Prime – Ripple’s institutional prime brokerage platform acquired from Hidden Road in 2025. Why it matters - This is one of the largest debt facilities ever extended to a crypto-native prime broker, and it sits inside an established global asset manager’s specialty finance arm. The signal, that institutional capital is now structurally comfortable lending against a book of mixed traditional and digital asset margin loans, is significant. - The structure is dynamic: drawable up to $200MM in tranches tied to underlying client borrowing demand. It mirrors the warehouse-style facilities that have long financed consumer and equipment lenders. Key risks - Asset volatility and haircuts: Mixing crypto and traditional collateral inside a single facility requires sophisticated, real-time margining and look-through reporting. - Regulatory perimeter: US crypto rules continue to evolve; covenant design needs to flex with that. Opportunity - Specialty finance teams that can structure dynamically-advanced facilities with strong eligibility frameworks and real-time collateral reporting will lead this fast-growing segment. The line between “prime brokerage” and “asset-based lending” is officially blurred. **Aquestive Therapeutics and Oaktree, $150MM** Oaktree Capital Management refinanced Aquestive Therapeutics’s debt with a new $150 million life sciences facility. The structure is the story: - $55MM funded at close (used to repay a prior $45MM loan). - $20MM tranche unlocking on FDA approval of Anaphylm. - $25MM tranche tied to sales milestones. - Up to $50MM available with mutual consent. - Interest-only through 2031 maturity. Why it matters This is a textbook example of how modern specialty lenders are pricing event-driven optionality directly into the capital stack. The borrower defers principal, lowers near-term cash outflow, and earns access to growth capital as clinical and commercial milestones de-risk. The lender, in turn, gets a structured exposure with embedded performance triggers. Key risks Regulatory binary risk (FDA approval), launch execution, and milestone interpretation disputes are the standard hazards. Documentation precision and milestone monitoring discipline are essential. Opportunity Tranched, milestone-linked facilities are spreading well beyond life sciences. Expect to see this design pattern in fintech, climate tech, and defense tech facilities over the next 12 months. **Phillips Pet Food, Guitar Center, Rango, and more** The week was particularly rich in classic ABL activity – the steady, collateral-driven lending that sits at the heart of the specialty finance market. - Phillips Pet Food and Supplies closed a $175 million senior secured credit facility agented by Siena Lending Group (holding $105MM of the facility), refinancing existing debt and funding the acquisition of certain assets from Central Garden & Pet. A solid example of ABL stepping in to support a strategic M&A move in a defensive consumer category. - Guitar Center amended and extended its existing ABL with Wells Fargo as administrative and collateral agent, maintaining $375 million in total commitments, lowering borrowing costs, relaxing minimum availability covenants, and pushing maturity from December 2027 to May 2031. A constructive sign for specialty retail credit. - White Oak Commercial Finance provided a $25 million senior secured ABL facility to Rango, Inc., a mining services platform operating across 13 states (aggregates, precious metals, frac sand). Classic capital-intensive, working-capital-heavy ABL story. - JPalmer Collective delivered a $3.5MM asset-based line to Pangaea Holdings in consumer products. - Amerisource Business Capital provided a $2.0MM working capital facility to an undisclosed industrial services company. - Culain Capital Funding extended a $5.0MM working capital facility to an undisclosed frozen food producer. Why it matters for asset managers - This is the bread and butter of the private credit market – collateral-rich, monitored, advance-rate-driven facilities to mid-market borrowers. The deal sizes vary, but the workflow is the same: borrowing base certificates, eligibility tests, ineligibles, dilution reserves, dominion accounts, field exams, and continuous monitoring. The lenders winning this market are the ones who can scale operational rigor without scaling headcount linearly. Key risks - Inventory and receivables aging discipline in consumer and retail names. - Customer concentration in middle-market industrial businesses. - Commodity exposure in mining and frozen food collateral. Opportunity - The middle market remains structurally underbanked, and the lenders that can deliver fast underwriting, automated borrowing base monitoring, and clear portfolio analytics are taking share from regional banks every quarter. **McGrath RentCorp’s $725MM Bank Syndicate** McGrath RentCorp completed a $725 million five-year revolving credit facility with Bank of America as Joint Lead Arranger, Sole Bookrunner, and Administrative Agent, with U.S. Bank and Wells Fargo as Joint Lead Arrangers and Co-Syndication Agents. It replaces a $650MM line, upsizes capacity by $75MM, and extends maturity to May 2031. Why it matters McGrath is a 35-year dividend grower with a deep rental asset base (modular buildings, portable storage, test equipment). The deal is a useful data point on bank syndicate appetite for high-quality, asset-rich, recurring-revenue businesses – even as private credit captures most of the headlines. Bank capital still wins where investment-grade-like profiles and large public-company sponsorship are in play. **Niche Specialty plays worth noting** - Post Road Group structured a $25MM tax lease facility for an industrial warehouse storage systems manufacturer, a reminder that tax-advantaged leasing structures remain a quietly powerful tool for equipment-intensive manufacturers. - Encina Private Credit provided a $50MM first-out term loan to an undisclosed technology company. The first-out / last-out unitranche structure continues to be a workhorse for sponsored middle-market deals. - Briar Capital Real Estate Fund delivered a $10.3MM real estate term loan for cold storage facilities, cold storage remains one of the more interesting structural growth themes in industrial real estate credit. **Takeaways for Asset Managers and Lenders** Step back from the individual deals and the week tells a coherent story. 1. Specialization is the new alpha. Lambda, Ripple Prime, Aquestive, Rango, and the cold storage real estate deal each demanded different collateral expertise, different monitoring frameworks, and different covenant designs. Generalist lenders will continue to lose ground to specialists who understand the asset. 2. Structure is doing more work than spread. Tranched commitments, milestone-linked tranches, dynamic advance rates, and amend-and-extend transactions dominated the week. Returns are increasingly being engineered through structure rather than headline pricing. 3. Data and monitoring infrastructure are now the bottleneck. Whether you’re tracking GPU residuals, crypto margin haircuts, FDA milestone triggers, borrowing base eligibles, or modular rental utilization, the firms that can centralize, automate, and report on this data are dramatically more scalable than those that can’t. 4. Bank capital and private credit are coexisting, not competing. McGrath got a bank syndicate. Lambda got a bank syndicate. Aquestive went to Oaktree. Phillips Pet Food went to Siena. Ripple went to Neuberger. The market is segmenting by borrower profile and collateral, not by a single “bank vs. non-bank” axis. 5. Middle-market ABL is alive, deep, and rewarding operational discipline. The $2MM, $5MM, and $25MM ABL deals don’t make headlines, but in aggregate they’re an enormous and growing book of business, and they reward operational excellence more than any other segment. **How Cardo AI helps Asset Managers and Lenders** At Cardo AI, we build the operating system for private credit, asset-based finance, and structured finance – from data ingestion and loan tape standardization, through borrowing base and waterfall computation, through portfolio analytics and investor reporting. Our clients use Cardo AI to: - Onboard new asset classes (from AI infrastructure to consumer ABL) without rebuilding from scratch. - Automate borrowing base monitoring, eligibility testing, and covenant tracking. - Run scenario, stress, and concentration analyses across portfolios in seconds, not days. - Deliver institutional-grade reporting to investors, banks, and rating agencies. The deals from last week are exactly the type of transactions our platform is built to support – at the scale, speed, and granularity that modern private credit demands. **Sources** *ABL Advisor deal tables (May 11–15, 2026); company press releases via BusinessWire, GlobeNewswire, Bloomberg, Yahoo Finance, ABF Journal, Crowdfund Insider, and issuer investor relations pages.* If you’d like to see how Cardo AI can help your team operate at the next level, reach out to us and [book a demo.](https://cardoai.com/book-a-demo/) --- ### [Weekly Deal Roundup | May 18–21, 2026](https://cardoai.com/article/weekly-deal-roundup-may-18-21-2026/) **Published:** May 25, 2026 **Author:** RobertaCardo26 **Content:** Contents [1. $3.6B+ across the capital stack](#introduction) [2. CoreWeave’s $3.1B DDTL 5.0](#first-par) [3. BIG Fiber’s $250MM (+$100MM Accordion) ](#second-par) [4. MidCap and TCW Refinance made by Gather](#third-par) [5. Wingspire backs data platform with $85MM ABL facility](#fourth-par) [6. The Specialty ABL cluster: Where operational discipline pays](#fifth-par) [7. Takeaways ](#sixth-par) [8. How Cardo AI helps Asset Managers and Lenders](#seventh-par) **$3.6B+ across the capital stack: GPU infrastructure goes public, Dark Fiber goes institutional, and the mid-market refinancing wave builds** While the previous week was all about specialization, last week focused on legitimization. A single deal, CoreWeave’s $3.1 billion DDTL 5.0, accounted for roughly 85% of announced volume, but the story wasn’t just the headline number. It was that the facility was the first publicly syndicated HPC infrastructure-backed financing, oversubscribed, with secondary-market trading enabled and ratings from Moody’s and Fitch. AI infrastructure debt is no longer a private bilateral conversation; it is a tradable asset class. Beneath that, last week’s smaller deals, dark fiber from Stonepeak and La Caisse, kitchenware from TCW and MidCap, oil and gas from Eclipse, infrastructure from Encina, and a handful of $1–12MM specialty ABL transactions, filled in a coherent picture: capital moved where the collateral story was concrete and the monitoring infrastructure was in place to underwrite it. Here is what last week told us, deal by deal. **CoreWeave’s $3.1B DDTL 5.0. The public syndication moment for AI infrastructure** CoreWeave closed a $3.1 billion delayed draw term loan facility (“DDTL 5.0”) with Morgan Stanley and Mitsubishi UFJ Financial Group as joint lead arrangers and bookrunners. It builds on CoreWeave’s prior $8.5 billion investment-grade-rated DDTL 4.0 facility from earlier this year, bringing total 2026 debt and equity raised above $20 billion. The defining features: - First publicly syndicated HPC infrastructure-backed financing vehicle meaningfully expanding the addressable investor base and enabling secondary market trading. - Meaningfully oversubscribed, with pricing tightened 50 basis points during syndication. - Ba2 / BB+ rated (Moody’s / Fitch) sub-investment-grade by half a notch, but the public rating itself is the breakthrough. - 5.5-year maturity, issued through a dedicated SPV (CoreWeave Financing DDTL V, LLC), aligned with the deployment schedule and useful life of the underlying GPU infrastructure. - Proceeds support deployment of infrastructure tied to two specific customer contracts. Why it matters for asset management This is the moment GPU-backed financing graduates from a club deal to a syndicated asset class. The implications stack: - Insurance companies, pension funds, and CLO managers now have a path to allocating to AI infrastructure debt without committing to bilateral underwriting. - Secondary trading capability changes the liquidity profile fundamentally and with it, the natural buyer base. - Standardized ratings and disclosure mean comparable analysis across deals is now possible. The asset class is institutionalizing in real time. Key risks to monitor - Concentration risk in customer contracts. The DDTL is tied to specific customer deployments. Look-through to ultimate counterparty credit (Meta, OpenAI, others) is essential. - Asset-life and obsolescence risk. GPU generations turn over fast. Residual-value assumptions in the underwrite are the single biggest swing factor in returns. - Leverage and refinancing risk. CoreWeave’s adjusted leverage was reported at ~6.9x as of Q3 2025; the company is expected to burn cash through 2027. The capital stack works only if AI demand continues at projected pace. - Secondary market depth. A “publicly syndicated” facility still needs active buyers in stress scenarios. The first credit event will reveal whether that depth is real. Opportunity - Asset managers that can build look-through analytics on GPU collateral, customer concentration, residual-value scenarios, and contract performance in near real-time will dominate this asset class. The lenders who treat it as commodity syndicated debt will misprice it. **BIG Fiber’s $250MM from Stonepeak Credit and La Caisse** BIG Fiber, the high-capacity dark fiber infrastructure provider sponsored by Columbia Capital and SDC Capital Partners, closed a $250 million debt facility with a $100 million accordion led by Stonepeak Credit and La Caisse (formerly CDPQ). Why it deserves more attention than its size suggests: - The facility follows BIG Fiber’s 2024 first-ever green loan in the dark fiber sector meaning this borrower is now two-for-two on novel structures. - The $100MM accordion lets BIG Fiber scale the facility without renegotiating documentation, a useful design for an infrastructure builder with a known buildout pipeline (205+ route miles and 165,000 fiber miles being added in Greater Atlanta alone). - The lender combination a major credit fund (Stonepeak) plus a long-duration Canadian institutional investor (La Caisse) is exactly the matched-duration capital that fiber, data centers, and other long-life digital infrastructure assets require. Why it matters Dark fiber is the picks-and-shovels investment underneath the AI infrastructure boom. The CoreWeave deal funds the GPU racks; deals like BIG Fiber’s fund the connectivity that makes those GPU racks useful. Both sides of this barbell are now backed by institutional private credit. Key risks - Construction and permitting risk on new builds increasingly the binding constraint in US digital infrastructure. - Counterparty concentration in dark fiber tends to be high (hyperscalers, telcos, large enterprises). - Long-cycle capital deployment with revenue lagging build completion facility design must accommodate the J-curve. Opportunity - Institutional lenders with the patience and the analytics to underwrite long-cycle infrastructure are taking share from balance sheet banks. The accordion-plus-green-loan structural innovation is a template worth studying. **MidCap and TCW Refinance made by Gather ($95MM)** Made by Gather, the home and kitchenware company behind the Beautiful by Drew Barrymore™ and bella™, brands completed a refinancing led by TCW Private Credit Group (part of an asset manager with $200B+ AUM) alongside MidCap Financial (Apollo-managed, $62B+ in commitments under administration as of Q1 2026), with MidCap holding the $95MM portion. Why it matters - A textbook dual-lender unitranche-style structure for a mid-market consumer brand platform. TCW brings flexible structuring and up to $300MM of underwriting capacity; MidCap brings specialty finance execution. - Celebrity-brand consumer platforms are increasingly a discrete asset class in private credit underwriting. The Drew Barrymore™ brand line is a real and defensible moat, but it also means licensing risk and brand-talent dependency are now material credit factors. - The refinancing replaces a prior structure, establishes long-term capital, and supports product innovation plus retail expansion the classic growth-refi mandate. Key risks - Retail channel concentration kitchenware platforms live and die by mass-merchant relationships. - Brand and talent dependency celebrity-linked consumer brands have outsized upside but real headline risk. - Inventory and working capital intensity consumer goods cycles can swing borrowing-base availability sharply. Opportunity - Specialty consumer lenders with strong inventory monitoring, channel analytics, and brand-IP underwriting capabilities continue to win this space. Generalist credit funds without consumer category depth are at a disadvantage. **Wingspire backs data platform with $85MM ABL facility** Wingspire Capital provided an $85 million revolving credit facility to an undisclosed technology company described as a data platform. The deal sits in the increasingly important “tech but not pure-software” middle ground companies with hard data assets, customer contracts, and predictable enterprise revenue that fit traditional ABL frameworks more cleanly than a pure SaaS borrower would. Why it matters - Wingspire has been an active originator across consumer, retail, industrial, and technology this year. Each transaction looks small in isolation; in aggregate, mid-market specialty ABL of this profile is one of the most reliably growing pockets of the private credit universe. **The Specialty ABL cluster: Where operational discipline pays** The week’s longer tail of smaller transactions is, in many ways, the most representative of where the bulk of private credit value creation actually happens: - Eclipse Business Capital $44MM senior secured credit facility to an undisclosed oil & gas borrower. Energy ABL remains a discipline-heavy segment with strong returns for lenders who know how to underwrite hydrocarbon-linked working capital. - Encina Private Credit $38.8MM first-out revolver and term loan to an undisclosed infrastructure company. First-out / last-out unitranches continue to be the workhorse of sponsored middle-market lending. - Altriarch Asset Management $12.5MM senior secured ABL to an undisclosed manufacturer. Classic asset-based facility for a working-capital-intensive industrial. - The Hedaya Capital Group $7.5MM factoring facility to an undisclosed apparel company. Factoring remains alive and well in cyclical fashion. - InterNex $6.0MM non-notified revolver to an undisclosed marketing services firm. Non-notified structures protect borrower customer relationships while still giving the lender receivables collateral. - Rosenthal Capital Group $1.5MM purchase order financing facility to an undisclosed women’s apparel company. PO financing is the unsung specialty product for fast-growth consumer brands managing import cycles. - SouthStar Capital $0.7MM working capital facility to an undisclosed HVAC company. Small-ticket working capital for a fragmented contractor universe. None of these deals will make a headline. But every one of them requires daily borrowing-base monitoring, eligibility testing, ineligibles reserves, dilution analysis, and concentration tracking. In aggregate, this is the backbone of US specialty finance. The lenders that scale this work without scaling cost are the ones that compound returns. **Takeaways** Step back from the deal-by-deal and the week reveals four themes worth carrying into next week’s pipeline conversations. 1. AI infrastructure debt is now a public market not a private one. CoreWeave’s DDTL 5.0 changes the conversation. Expect more rated, publicly syndicated, secondary-tradeable GPU and HPC infrastructure facilities through the remainder of 2026. Underwriting standardization will follow rapidly. Asset managers that don’t yet have a framework for this asset class are already behind. 2. The digital infrastructure barbell is real and it’s institutionalizing. GPU facilities on one end, fiber/data center/power on the other. The same institutional lenders are showing up across both. Build sector views across the full stack, not silos. 3. Sponsor-backed consumer refis are returning in volume. The TCW + MidCap structure on Made by Gather is a template you’ll see repeated. With private credit dry powder still elevated and dialed back from peak 2024 deployment, refi-and-grow mandates for mid-market consumer platforms are an attractive risk-adjusted pocket. 4. Specialty ABL operational excellence is the durable moat. From $85MM down to $0.7MM, the work is the work. Borrowing bases, eligibles, dilution, concentrations, covenants, field exams, dominion accounts at scale, in real time, with proper reporting. The lender who industrializes this wins the next decade. **How Cardo AI helps Asset Managers and Lenders** Every deal in this week’s brief from CoreWeave’s $3.1B publicly syndicated facility down to SouthStar’s $700K HVAC working capital line shares one operational requirement: the lender or investor needs to collect, normalize, monitor, and report on loan and collateral data continuously, at scale, with institutional rigor. That’s the operating system we build at Cardo AI for private credit, asset-based finance, and structured finance: - Loan tape ingestion and standardization across any data source or format - Borrowing base computation, eligibility testing, and waterfall modeling - Concentration, covenant, and trigger monitoring with real-time alerts - Portfolio analytics, stress testing, and scenario analysis - Investor reporting at the granularity rating agencies, banks, and LPs demand - Multi-asset-class onboarding from GPU infrastructure facilities to consumer ABL to apparel factoring **Sources** *ABL Advisor deal tables (May 18–21, 2026); CoreWeave investor relations and SEC filings; PRNewswire, BusinessWire, Yahoo Finance, ABF Journal, La Caisse press releases, and issuer investor communications.* If you’re scaling into new asset classes or just tired of stitching data together in spreadsheets, [book a demo.](https://cardoai.com/book-a-demo/) --- ### [Weekly Deal Roundup | May 26–29, 2026](https://cardoai.com/article/weekly-deal-roundup-may-26-29-2026/) **Published:** June 1, 2026 **Author:** GiadaCardo26 **Content:** Contents [$2.08B Across the Capital Stack](#introduction) [The Marquee Deal (Triple Flag’s $1.0B Revolver)](#first-par) [The Healthcare ABF Story](#second-par) [Lender Finance is back (RESIDCO’s $450MM Expansion)](#third-par) [Siena + Hilco’s $130MM for Panavision](#fourth-par) [The Specialty ABL Cluster](#fifth-par) [Takeaways ](#sixth-par) [How Cardo AI helps Asset Managers and Lenders](#seventh-par) **$2.08B Across the Capital Stack: Bank Syndicates Reassert, Healthcare ABF Compounds, and Specialty Asset Lenders Quietly Set Records** If the last two weeks belonged to AI infrastructure and digital build-out, this one belonged to the steady, structurally important corners of the credit market: bank syndicate revolvers for high-quality issuers, skilled nursing bridge lending at portfolio scale, lender-finance facilities backing the rail and aviation leasing universe, and a long tail of specialty ABL transactions that week after week quietly compound to billions in aggregate. The headline names are familiar (Triple Flag, Panavision, Fifth Third, Siena, Hilco), but the patterns underneath them are worth pausing on. Where capital was committed this week tells you which lenders are positioned for what comes next. Here’s the breakdown. **The Marquee Deal: Triple Flag’s $1.0B Revolver: An Investment-Grade-Style Syndicate for a Streaming and Royalty Pure-Play** Triple Flag Precious Metals (TSX/NYSE: TFPM) closed an amendment expanding its undrawn revolving credit facility to $1.0 billion, with an additional $300 million uncommitted accordion, up from a prior $700M facility. The syndicate is a heavyweight Canadian-led bank group: National Bank of Canada (Administrative Agent), Bank of Nova Scotia, National Bank Capital Markets and CIBC (Co-Lead Lenders), with Royal Bank of Canada, TD, Bank of Montreal, Bank of America and UBS as participants. **Key terms:** - Pricing: SOFR + 1.325% to 2.75% depending on leverage, a 12.5 bps tightening at the low end of the grid versus the prior facility. - Tenor: 4 years, maturing May 2030. - Undrawn, supporting acquisition and growth flexibility against a 241-asset gold and silver streaming/royalty portfolio. **Why it matters for asset management:** Triple Flag operates an unusual asset model, gold and silver streams and royalties, but the financing reads like a textbook investment-grade revolver. That’s the story: - Streaming and royalty companies have institutionalized as a credit category. Lenders are now pricing this risk like infrastructure cash flow rather than mining equity beta. The non-operating, fixed-cost-per-ounce structure makes for a credit profile that bank syndicates clearly find familiar. - The pricing concession (12.5 bps tighter) is meaningful in a market where every basis point requires real underwriting confidence. - Bank capital still wins where the borrower profile fits. An undrawn $1B revolver against a pure-play royalty book is precisely the relationship banks want, fee income, light capital usage, deep multi-product cross-sell. **Key risks to monitor:** - Commodity price exposure, even with stream/royalty insulation. Long-dated revenue is still tied to gold and silver realizations. - Operator counterparty risk on the 34 producing assets and 207 development-stage projects. - Geographic and asset concentration within the portfolio. **Opportunity:** Lenders building credit frameworks for streaming/royalty, music IP, pharma royalty, and similar “predictable cash flow on non-operated assets” structures are positioning for one of the most interesting growing asset classes in private credit. The diligence and monitoring requirements are very different from operating-company credit and the lenders who develop the infrastructure to do this at scale will lead the category. **The Healthcare ABF Story: MONTICELLOAM’s $435MM Skilled Nursing Bridge** MONTICELLOAM arranged $435 million of financing for an undisclosed sponsor’s 14-facility Maryland skilled nursing portfolio spanning over 1,890 licensed beds. The structure: $400MM acquisition bridge loan + $35MM working capital line of credit. The sponsor owns and operates roughly 115 skilled nursing and assisted living properties across the Eastern US and Sunbelt. **Why it matters:** Skilled nursing bridge lending is a category that almost nobody on the asset management side talks about, and that quietly does hundreds of millions of dollars in deal volume per month. MONTICELLOAM alone has executed multiple $80MM+, $144MM, and $218MM portfolio transactions in 2026 to date. This is a $400MM+ single-portfolio deal, and it’s not an outlier, it’s a category that has matured into one of the most consistent specialty ABF asset classes in the US. **The investment thesis is straightforward and durable:** - Demographic tailwind that doesn’t reverse on any reasonable timeline. - Reimbursement-driven cash flows (Medicare, Medicaid) with operator-level execution risk but real underlying revenue visibility. - Real estate plus operating business, the bridge funds the real estate purchase while the working capital line supports the operating ramp. - Repeat sponsor relationships that compound over time as operators add facilities. **Key risks:** - Reimbursement policy risk (federal and state) is the single largest swing factor. - Operator concentration and execution risk, clinically strong facilities with weak operators fail; the bridge lender carries that risk during the transition. - Labor cost inflation has been a serious headwind across the category. - Survey and compliance risk, clinical compliance issues can disrupt cash flow rapidly. **Opportunity:** This is a textbook case of where operational data and monitoring infrastructure separates lenders who scale from lenders who don’t. Census tracking, payor-mix monitoring, days-of-cash analysis, compliance and survey watch, done across a multi-facility portfolio, daily, with automated exception reporting, is the difference between a $400MM book and a $4B book. **Lender Finance is back: RESIDCO’s $450MM Bank Facility Expansion with Fifth Third** Fifth Third Bank acted as Administrative Agent, Lead Arranger, and Joint Bookrunner on RESIDCO’s renewed and expanded $450 million senior secured revolving credit facility. RESIDCO is a specialty leasing and asset-based investment platform focused on rail and aviation equipment, and the new facility represents an increase over the prior one. The Fifth Third / RESIDCO relationship has spanned over two decades. **Why it matters:** Lender finance banks providing facilities to specialty finance companies that then originate their own loans, is the invisible plumbing of US specialty credit. When a bank like Fifth Third commits an upsized $450MM revolver to a rail/aviation lessor, it signals: - Continued bank appetite for asset-backed lender finance at scale, even as the rest of the bank balance sheet shrinks from regulatory pressure. - Confidence in long-life transportation assets (rail cars, regional aircraft, freight equipment) as collateral, a category that genuinely is durable across cycles. - A multiplier effect on downstream private credit: every dollar of Fifth Third’s facility supports multiple dollars of RESIDCO origination to end borrowers. Same day, related signal: SouthStar Capital provided a $1.5MM ABL facility to another undisclosed leasing provider. Different end of the size spectrum, same theme, lender finance to specialty asset lessors is one of the steadier growth pockets in the market right now. **Key risks:** - Asset value cyclicality in rail and aviation, particularly residual values on aging equipment. - End-customer credit risk flowing through the lessor. - Concentration risk on lessee, asset type, and geography. **Opportunity:** Bank lender finance teams that can monitor end-borrower portfolio quality (via the specialty finance company’s data) in near real time are out-pricing peers who can only see consolidated financials. Transparency at the asset level, pushed up the capital structure, is the moat. **A Niche Worth Watching: Siena + Hilco’s $130MM for Panavision** Siena Lending Group (Agent, Sole Bookrunner, $80MM hold) and Hilco (Documentation Agent, Collateral Monitoring Agent, $50MM hold) closed a $130 million revolving credit facility for Panavision, the global motion picture and television production services and equipment company. The facility refinances Panavision’s prior $130MM revolver (agented by Great Rock Capital in May 2025), with Siena now in the lead role. **Why it matters:** This is a textbook example of specialty ABL where collateral expertise is the entire moat. Panavision’s collateral base is high-precision cinema lenses, cameras, lighting equipment, and post-production assets, hard to value, hard to liquidate, and almost impossible to monitor without genuine sector expertise. Hilco’s role as Collateral Monitoring Agent is the tell: the lender stack has paid specifically for asset-level expertise that a generalist bank syndicate could not provide. **A few additional points worth flagging:** - The structure separates origination/agency (Siena) from collateral monitoring (Hilco). This division of labor is increasingly common in specialty ABL deals where collateral complexity exceeds generalist underwriting capacity. - Production and supply chain dynamics in the film industry remain in flux (post-strike, streaming consolidation, content production rebound). The lender needs to be able to monitor borrower performance across a complex revenue mix. **Key risks:** - Content production cycle volatility, film and TV production volume swings materially with strikes, streaming spend, and macro conditions. - Equipment obsolescence on the camera and post-production side. - Geographic concentration in major production hubs. **Opportunity:** Specialty ABL lenders with genuine collateral expertise in non-commodity assets (cinema equipment, healthcare receivables, music IP, brand IP, scientific instruments) command real pricing premiums. The Siena/Hilco structure is a template for how non-bank specialty lenders are formalizing collateral expertise as a separately-priced service. **The Specialty ABL Cluster: Working Capital Across Eight Sectors** The week’s longer tail demonstrates the underlying diversity that makes specialty finance such a structurally attractive category: - Legacy Corporate Lending $31MM combined ($20MM ABL + $11MM real estate term loan) to Natural Alternatives International in nutritional products. Combined working capital + owned real estate structure is a useful tool for borrowers with both inventory cycles and owned production facilities. - Capteris $14MM loan facility to an undisclosed food & beverage manufacturer. - SixCap Healthcare Finance $10.5MM ABL to an undisclosed group of skilled nursing facilities (a smaller-format healthcare ABF deal complementing the MONTICELLOAM transaction). - Culain Capital Funding $5MM A/R + inventory facility to an undisclosed national frozen produce company. - Sallyport Commercial Finance $4.9MM inventory finance facility to an undisclosed manufacturing technology company. - SouthStar Capital three transactions this week: $3MM PO + A/R facility (critical power supplier), $1.5MM ABL (leasing provider), and $0.8MM working capital (environmental materials company). A useful reminder that smaller-ticket specialty ABL is a high-frequency, high-volume business model. **The aggregate point:** None of these deals will make a Bloomberg headline. But in aggregate, eight different specialty lenders deployed capital across eight different sectors in one week. That’s the structurally durable pattern that makes specialty finance such a compelling allocation for institutional capital. **Takeaways** Four themes worth carrying into next week. - Bank syndicates are increasingly selective and unusually competitive where they fit. Triple Flag’s 12.5 bps tightening on a $1B revolver, and RESIDCO’s $450MM facility expansion with 20+ years of relationship continuity, both tell the same story: banks are walking away from a lot of mid-market credit, but they’re competing hard for the issuers that fit their balance sheet profile. The market is bifurcating. - Healthcare ABF, particularly skilled nursing bridge lending, is one of the most under-discussed durable categories in US specialty credit. MONTICELLOAM closed a $435MM portfolio deal this week. They’ve closed several similar transactions in 2026 already. The category compounds quietly because the underlying demographic and reimbursement story doesn’t move quickly, but the deal volume is substantial and growing. - Lender finance, banks lending to non-bank specialty lenders, is one of the most leveraged ways to scale capital deployment in private credit. Every dollar of bank lender finance enables multiple dollars of downstream origination. RESIDCO’s $450MM facility, and even SouthStar’s $1.5MM ABL to another lessor, are part of the same structural trend. Banks that can underwrite end-borrower portfolios at the data layer, rather than just consolidated lessor financials, outperform. - The division of labor in specialty ABL is formalizing. Siena’s lead role + Hilco’s collateral monitoring agent role on Panavision is part of a broader trend: specialty lenders are increasingly building or buying dedicated collateral expertise (or partnering with firms that have it). The next decade of specialty credit will be defined less by capital and more by collateral monitoring infrastructure. **How Cardo AI Supports Lenders and Asset Managers** Every transaction in this week’s brief, from Triple Flag’s $1B bank syndicate to SouthStar’s $0.8MM environmental services working capital line, has the same operational requirement at its core: the lender needs to collect, normalize, monitor, and analyze loan and collateral data, continuously, at scale, with institutional rigor. For skilled nursing bridge lenders, that means portfolio-level census, payor mix, and compliance monitoring. For lender finance teams, it means end-borrower transparency through the specialty finance company’s loan tape. For specialty equipment ABL, it means asset-level collateral tracking and residual-value analytics. For bank syndicate revolvers, it means covenant and concentration monitoring across a complex obligor profile. **Cardo AI builds the operating system underneath all of it:** - Loan tape ingestion and standardization across any data source or format - Borrowing base computation, eligibility testing, and waterfall modeling - Concentration, covenant, and trigger monitoring with real-time alerts - Portfolio analytics, stress testing, and scenario analysis across asset classes - Investor reporting at the granularity rating agencies, banks, and LPs demand - Multi-asset-class onboarding from rail and aviation lessors to skilled nursing portfolios to cinema equipment ABL If your team is scaling into new asset classes, new structures, or new geographies, or if you’re rebuilding the operational stack underneath existing books, we’d love to show you what’s possible. **Sources** *ABL Advisor deal tables (May 26–29, 2026); Triple Flag Precious Metals 6-K and press release (May 25, 2026); MONTICELLOAM press materials; Siena Lending Group / Hilco Global / Panavision joint announcement (May 28, 2026); RESIDCO / Fifth Third Bank announcement; ABF Journal, Seniors Housing Business, Monitor Daily, BusinessWire, PRNewswire, and issuer press releases.* If you’re scaling into new asset classes or just tired of stitching data together in spreadsheets, [book a demo.](https://cardoai.com/book-a-demo/) --- ### [Weekly Deal Roundup | June 2–5, 2026](https://cardoai.com/article/weekly-deal-roundup-june-2-5-2026/) **Published:** June 9, 2026 **Author:** GiadaCardo26 **Content:** Contents [$2.1B Across the Capital Stack](#introduction) [The Bank-Syndicate Story](#first-par) [Lender Finance Scales](#second-par) [Special Situations at Scale](#third-par) [Esoteric Collateral Institutionalizes](#fourth-par) [Where Collateral Expertise Is the Moat](#fifth-par) [The Specialty ABL Cluster](#sixth-par) [Takeaways](#seventh-par) [How Cardo AI Supports Lenders and Asset Managers](#eight-par) **$2.1B Across the Capital Stack: Bank Syndicates Lean In, Lender Finance Scales, and Specialty ABL Quietly Compounds** If the last few weeks belonged to AI infrastructure and digital build-out, this one belonged to the structurally important corners of the credit market: large bank-syndicate revolvers for high-quality issuers, non-bank special-situations financing at scale, lender-finance facilities backing the specialty-lending universe, and a long tail of asset-based deals that, week after week, quietly compound to billions in aggregate. Roughly $2.1 billion across 16 disclosed transactions crossed the tape, from a $0.8MM purchase-order and receivables line to a $400MM syndicated revolver. The names are familiar (JPMorgan, Goldman Sachs, Silver Point, White Oak, Hilco, Siena, Rosenthal), but the patterns underneath them are worth pausing on. Where capital was committed this week tells you which lenders are positioned for what comes next. Here’s the breakdown. **The Bank-Syndicate Story: Dropbox’s $400MM Senior Secured Revolver** JPMorgan Chase acted as Administrative Agent, Collateral Agent, Joint Lead Arranger, and Sole Bookrunner on a $400 million senior secured revolving credit facility for Dropbox, with Goldman Sachs Bank USA and RBC Capital Markets as Joint Lead Arrangers and Lenders. **Key terms:** - Structure: senior secured revolving credit facility. - Lead bank group: JPMorgan (agent/sole bookrunner) + Goldman Sachs + RBC. - Borrower: Dropbox, a cash-generative software platform. **Why it matters for asset management:** This is the bank-syndicate playbook in its purest form. A large revolver for a profitable, recurring-revenue software business is precisely the relationship banks want: fee income, light capital usage, and deep multi-product cross-sell. - The market is bifurcating. Banks are walking away from a lot of mid-market and complex credit under regulatory and capital pressure, but competing hard for the issuers that fit their balance-sheet profile. A three-house syndicate of JPMorgan, Goldman, and RBC lining up behind a single tech name is a signal of where bank appetite is concentrating. - For asset managers, the read-through is competitive: the highest-quality borrowers will continue to be intensely contested by bank capital, pushing private-credit allocators toward complexity, structure, and asset classes the banks won’t touch. **Key risks to monitor:** - Software revenue concentration and competitive pressure on the borrower’s core product. - Refinancing and maturity-wall dynamics across the broader tech-credit complex. - Covenant headroom if rates stay higher-for-longer. **Opportunity:** The premium names are a bank game. The opportunity for non-bank lenders and asset managers is the inverse of this deal, the borrowers and structures that don’t fit a clean syndicated revolver, where underwriting and monitoring capability, not balance-sheet scale, is the edge. **Lender Finance Scales: $310MM Backing Amerisource Business Capital** Texas Capital acted as Administrative Agent, with BOK Financial and Huntington Bank as Joint Lead Arrangers, on a $310 million leverage facility for Amerisource Business Capital, itself an asset-based lender. **Why it matters:** Lender finance, banks providing facilities to specialty finance companies that then originate their own loans, is the invisible plumbing of US specialty credit. When a bank group commits $310MM to an ABL originator like Amerisource, it signals several things at once: - Continued bank appetite for asset-backed lender finance at scale, even as the rest of the bank balance sheet contracts. - A multiplier effect on downstream origination: every dollar of the facility supports multiple dollars of lending to end borrowers across the real economy. - Confidence in the originator’s underwriting discipline and collateral controls, banks underwrite the lender and the book behind it. **Key risks:** - Double leverage: the facility provider is exposed both to Amerisource and to Amerisource’s underlying loan portfolio. Risk concentrations can build invisibly without look-through reporting. - End-borrower portfolio quality flowing up the structure. - Concentration by obligor, sector, and geography within the underlying book. **Opportunity:** Lender-finance teams that can monitor end-borrower portfolio quality in near real time, through the specialty lender’s loan tape, out-price peers who can only see consolidated financials. Transparency at the asset level, pushed up the capital structure, is the moat. **Special Situations at Scale: WhiteHawk’s $390MM for The Star Entertainment Group** WhiteHawk Capital Partners provided a $390 million senior secured financing facility to The Star Entertainment Group, a large hospitality and gaming operator. **Why it matters:** Asset-heavy hospitality and gaming is classic special-situations territory: large tangible collateral, complex property-level cash flows, regulatory overlays, and frequently a turnaround thesis. Deals like this illustrate a structural shift in the market: - Non-bank specialty and special-situations lenders increasingly own the financings that bank syndicates won’t underwrite, and price the complexity accordingly. - Senior secured positioning against hard assets gives the lender downside protection, but the value of that protection depends entirely on the quality of collateral valuation and ongoing monitoring. **Key risks:** - Execution and turnaround risk through the financing’s life. - Regulatory and licensing exposure specific to gaming. - Property-level cash-flow volatility and seasonality. - Valuation and recovery risk on large, hard-to-liquidate assets. **Opportunity:** Special-situations lenders that can underwrite and continuously monitor complex, asset-heavy credits across property-level performance, covenant triggers, and collateral marks capture outsized economics. This is a category where operational and data infrastructure separates the firms that scale from the firms that don’t. **Esoteric Collateral Institutionalizes: Silver Point’s $200MM into Liberty Puerto Rico** Silver Point Capital provided a $200 million secured term loan to Liberty Puerto Rico subsidiaries in the telecom sector. **Why it matters:** Telecom and digital infrastructure are fast becoming sought-after credit categories, cash-flow-heavy, infrastructure-like, and increasingly financed by credit funds rather than traditional bank syndicates. - Infrastructure-style collateral (networks, towers, spectrum-adjacent assets) demands a different underwriting lens than inventory or receivables, with long-dated cash flows and high capital intensity. - Silver Point, a credit and special-situations manager, taking the lead on a secured term loan is part of a broader trend of institutional credit migrating into infrastructure-backed assets that legacy structured-finance templates were never designed to model. **Key risks:** - Cash-flow durability against competitive and regulatory dynamics in the Puerto Rico telecom market. - Capex intensity and the timing of network investment. - Refinancing and structural subordination considerations within the broader corporate group. **Opportunity:** Lenders building credit and modeling frameworks for telecom, data-center, and other infrastructure-backed assets across the full lifecycle, against live market data are positioning for one of the most interesting growth pockets in private credit. The diligence and monitoring requirements are very different from operating-company credit, and the firms that build the infrastructure to do this at scale will lead the category. **Where Collateral Expertise Is the Moat: White Oak + Hilco’s $192MM for Royal Cup Coffee and Tea** White Oak Commercial Finance acted as lender, with Hilco Global as Administrative Agent, on a $192 million senior secured facility for Royal Cup Coffee and Tea: a $155MM revolver plus a $37MM term loan. **Why it matter:** This is a textbook example of multi-tranche specialty ABL where collateral expertise and structure do the work: - The split between a $155MM revolver (working-capital and inventory cycles) and a $37MM term loan (longer-dated needs) is bespoke structuring tailored to the borrower’s cash-flow profile — flexibility for the borrower, stickiness for the lender. - Hilco’s role as Administrative Agent is the tell. The structure brings dedicated collateral and asset expertise into the deal — a division of labor that is increasingly common in specialty ABL where collateral complexity exceeds generalist underwriting capacity. **Key risks:** - Input-cost inflation and commodity exposure (coffee and tea pricing). - Inventory valuation and turnover across the borrowing base. - Sector concentration in food production and processing. **Opportunity:** Specialty ABL lenders with genuine collateral expertise and the data infrastructure to compute borrowing bases, test eligibility, and monitor concentration daily across multi-tranche structures, command real pricing premiums. The economics of bespoke structures only hold if the back office can keep up. **The Specialty ABL Cluster: Working Capital Across Every Corner of the Market** The week’s longer tail demonstrates the underlying diversity that makes specialty finance such a structurally attractive category. In a single week, more than a dozen lenders deployed capital across travel, real estate, media, food, technology, apparel, steel, and consumer products: - Neuberger Specialty Finance $300MM credit facility to Perk (travel & leisure). A large specialty-finance commitment in the consumer-experience economy. - BHI: $169.5MM construction loan to 767 Third Property Owner LLC (real estate). Project-level construction lending against a defined asset. - Siena Lending Group $35MM senior secured credit facility to an undisclosed media company. Content and IP-style cash flows that demand a non-generalist underwriting lens. - Rosenthal Capital Group $30MM factoring facility to an undisclosed apparel company, one of several Rosenthal transactions this week. - First Business Bank $25MM to an undisclosed nut-processing company: an ABL revolving line of credit paired with a CRE term loan, a combined working-capital-plus-real-estate structure useful for borrowers with both inventory cycles and owned facilities. - BRC Specialty Finance $20MM senior secured term loan to an undisclosed data-center infrastructure provider (technology). Digital infrastructure as financeable collateral, riding the AI-driven compute build-out. - Gibraltar Business Capital $12.5MM revolving credit facility to Sharebite, Inc. (specialty industries). - Rosenthal Capital Group $5MM to an undisclosed food manufacturer, $3MM ABL + $1MM term loan + $1MM accordion bespoke tranching at the smaller end of the market. - Viva Capital Funding $5MM to an undisclosed steel manufacturer: $2MM accounts-receivable line plus a $3MM inventory finance facility. - Rosenthal Capital Group $2MM asset-based loan to an undisclosed retailer. - Southstar Capital $0.8MM PO and A/R financing facility to an undisclosed consumer-products company. A reminder that smaller-ticket specialty ABL is a high-frequency, high-volume business model. The aggregate point: none of these deals will make a Bloomberg headline. But together, more than a dozen specialty lenders deployed capital across eight-plus sectors in one week. That’s the structurally durable pattern that makes specialty finance such a compelling allocation for institutional capital. **Takeaways** Four themes worth carrying into next week. 1. Bank syndicates are selective and unusually competitive where they fit. A three-house syndicate behind Dropbox’s $400MM revolver tells the same story as the broader market: banks are walking from a lot of credit but competing hard for issuers that fit their balance sheet. The market is bifurcating, and that pushes private credit toward complexity and structure. 2. Lender finance is one of the most leveraged ways to scale capital deployment. The $310MM facility for Amerisource Business Capital is the invisible plumbing of specialty credit, every dollar enables multiples of downstream origination. Providers that can underwrite end-borrower portfolios at the data layer, rather than just consolidated financials, will outperform. 3. “Financeable collateral” keeps expanding into esoteric, data-heavy assets. Telecom (Silver Point / Liberty PR), data-center infrastructure (BRC), and media cash flows (Siena) all priced this week. None fit a vanilla inventory template and that’s exactly why they pay a premium. Collateral expertise is becoming a separately-priced service, as Hilco’s agent role on the Royal Cup facility shows. 4. Structure is beating spread. Multi-tranche facilities were everywhere — Royal Cup’s revolver-plus-term, First Business Bank’s ABL-plus-CRE, Viva’s split A/R and inventory lines, Rosenthal’s ABL-plus-term-plus-accordion. Bespoke beats off-the-shelf, but every tranche, accordion, and add-on multiplies the operational load. **How Cardo AI Supports Lenders and Asset Managers** Every transaction in this week’s brief: from JPMorgan’s $400MM syndicated revolver for Dropbox to Southstar’s $0.8MM consumer-products working-capital line, shares the same operational requirement at its core: the lender needs to collect, normalize, monitor, and analyze loan and collateral data, continuously, at scale, with institutional rigor. For lender-finance teams, that means end-borrower transparency through the specialty lender’s loan tape. For special-situations and infrastructure credit, it means asset-level collateral tracking and full-lifecycle cash-flow modeling. For multi-tranche specialty ABL, it means borrowing-base computation and eligibility testing across several facilities at once. For bank-syndicate revolvers, it means covenant and concentration monitoring across a complex obligor profile. Cardo AI builds the operating system underneath all of it: - Loan-tape ingestion and standardization across any data source or format - Borrowing-base computation, eligibility testing, and waterfall modeling - Concentration, covenant, and trigger monitoring with real-time alerts - Portfolio analytics, stress testing, and scenario analysis across asset classes - Full-lifecycle cash-flow modeling for esoteric and structured assets, against live market data - Investor reporting at the granularity rating agencies, banks, and LPs demand - Multi-asset-class onboarding: from telecom and infrastructure credit to skilled-nursing portfolios to cinema and specialty-equipment ABL Cardo AI’s platform manages $100B+ in assets under technology, connects to 160+ external systems, and processes 6 trillion data points. If your team is scaling into new asset classes, new structures, or new geographies, or rebuilding the operational stack underneath existing books, we’d love to show you what’s possible. **Sources** *Deal data sourced from the ABL Advisor Q2 2026 Deal Tables (transactions dated June 2–5, 2026). Disclosed deal detail is limited to lender/role, amount, borrower, industry, and structure as reported; pricing and tenor were not disclosed for most transactions. Source typographical inconsistencies have been standardized.* If you’re scaling into new asset classes or just tired of stitching data together in spreadsheets, [book a demo.](https://cardoai.com/book-a-demo/) --- ### [Weekly Deal Roundup | June 8–11, 2026](https://cardoai.com/article/weekly-deal-roundup-june-8-11-2026/) **Published:** June 15, 2026 **Author:** GiadaCardo26 **Content:** Contents [0. The week in asset-based finance](#introduction) [1. The week in numbers](#first-par) [2. Flatbay Capital](#second-par) [3. Gordon Brothers & Wells Fargo](#third-par) [4. Needham Bank](#fourth-par) [5. MidCap Financial](#fifth-par) [6. Goldman Sachs](#sixth-par) [7. Rosenthal Capital Group](#seventh-par) [8. Raven Capital](#eight-par) [9. CIT Northbridge Credit](#nine-par) [10. The bigger picture: three themes from the week](#ten-par) [11. Takeaways](#eleven-par) [12. How Cardo AI supports Lenders and Asset Managers](#twelve-par) **The week in asset-based finance: $1.3B, 8 sectors, and the operational reality behind the headlines** From meat-processing real estate to a $655M lender-finance refinancing, last week’s deal flow shows just how broad and how operationally demanding “financeable collateral” has become. Eight deals. Eight sectors. More than $1.3 billion of committed capital in a single week. On the surface, the week of June 8 reads like a routine stretch of private-credit and asset-based finance (ABF) activity. Look closer and you see the defining dynamic of this market: the definition of financeable collateral keeps expanding and every new structure adds a layer of data, monitoring, and reporting complexity that legacy systems were never built to handle. Here’s our read on the deals that crossed the tape, what’s notable for asset managers, where the risk sits, and where the opportunity is for lenders willing to do the work. **The week in numbers** - $1.3B in disclosed commitments across 8 transactions - 8 distinct sectors:commercial real estate, luxury retail, cannabis, life sciences, digital infrastructure, consumer products, lender finance, and metals recycling - Structures on display: CRE lines of credit, inventory-backed term loans and revolvers, A/R factoring, growth term loans, and a large specialty-finance refinancing That diversity is the story. Below, deal by deal. **Flatbay Capital: $4.3M CRE line of credit (meat-processing real estate)** A specialized, single-purpose industrial property backing a CRE line of credit. **Why it matters** This is niche CRE collateral that doesn’t fit a standard template. Value is tied to the operating use, cold storage and food processing, not just the underlying land. **Key risk** Single-use real estate is hard to repurpose or liquidate, and food-processing facilities carry environmental and regulatory overhang. **The opportunity** Pricing power for lenders who can underwrite specialized assets that generalists avoid. **Strategic takeaway** Ongoing collateral valuation and monitoring matter more here than the headline loan-to-value. **Gordon Brothers & Wells Fargo: $32.5M term loan + revolver extension (Birks Group, jewelry)** A luxury jewelry retailer backed by an inventory-driven facility, pairing a bank with a valuation and disposition specialist. **Why it matters** A textbook borrowing-base structure, availability moves with the appraised value of jewelry inventory. Combining bank capital with a collateral expert is a model worth watching. **Key risk** Consumer-discretionary cyclicality, seasonal inventory swings, and appraisal volatility on luxury goods. **The opportunity** Pairing bank capital with valuation expertise materially de-risks the structure. **Strategic takeaway** Borrowing-base accuracy and frequent inventory marks are the entire game. **Needham Bank: $50M revolving credit facility (Cresco Labs, cannabis)** Committed bank revolver capital to a multi-state cannabis operator, a sector most banks still won’t touch. **Why it matters** Bank credit to cannabis remains scarce. A committed revolver is a competitive signal as much as a financing. **Key risk** Federal–state regulatory divergence, banking and custody constraints, and an unusually high compliance-monitoring burden. **The opportunity** A scarcity premium and a foothold in an underserved, growing market. **Strategic takeaway** Covenant and compliance reporting have to be airtight and continuous, not quarterly. **MidCap Financial: term loan + revolving loan (Cerus Corporation, life sciences)** Structured growth capital for a blood-safety and medical-technology company. **Why it matters** Life-sciences credit blends cash-burn risk with milestone-driven upside, a different underwriting muscle than cash-flow lending. **Key risk** Commercialization and regulatory timelines, plus liquidity runway against ongoing burn. **The opportunity** Flexible growth capital for a revenue-generating med-tech name, with revolver optionality. **Strategic takeaway** Track liquidity runway and covenant headroom against the burn rate in near real time. **Goldman Sachs (Arranger): $550M revolving credit facility (Applied Digital, digital infrastructure)** The headline number and the most of-the-moment deal: financing for AI and high-performance-compute data-center infrastructure. **Why it matters** Digital infrastructure is among the fastest-growing esoteric collateral classes in private credit, and it’s reshaping what a “financeable asset” looks like. **Key risk** Capital intensity, customer and offtake concentration, technology obsolescence, and power/siting dependencies. **The opportunity** Scale, plus direct exposure to the AI-compute build-out. **Strategic takeaway** These assets break standard ABS cash-flow templates. Modeling the structure across its full lifecycle, through ramp-up and reinvestment, is what separates a confident underwrite from a guess. **Rosenthal Capital Group (Factor): $1.0M A/R factoring facility (DTC beauty brand)** Small-ticket receivables factoring for a fast-growing consumer brand. **Why it matters** This is the working-capital plumbing that keeps high-growth brands liquid between sales cycles. **Key risk:** Customer concentration in the receivables pool, returns and chargebacks, and brand volatility. **The opportunity** Granular, high-velocity, self-liquidating exposure. **Strategic takeaway** Receivables-level visibility and dilution tracking are essential at this ticket size. **Raven Capital (Arranger) $655M refinancing facility (Elevate, lender finance)** The largest deal of the week, and a pure specialty-finance play: financing a lender. **Why it matters** Lender finance, lending to the lenders, is where capital scales fastest. But you’re underwriting a portfolio, not a single borrower. **Key risk** Leverage-on-leverage, underlying-pool performance, and drift in eligibility criteria and advance rates. **The opportunity** Efficient, scalable deployment with collateral-level diversification built in. **Strategic takeaway** Look-through transparency to the underlying assets is non-negotiable. You can only manage what you can see. **CIT Northbridge Credit: $25M credit facility (Huron Valley Steel, metals recycling)** An asset-based facility for a metals recycler, commodity-linked ABL in its purest form. **Why it matters** Collateral value moves with the market, so the borrowing base is a live number, not a static one. **Key risk** Scrap-metal price volatility, inventory mark swings, and sector cyclicality. **The opportunity** Well-collateralized lending against real, liquid assets. **Strategic takeaway** Real-time inventory and commodity-linked borrowing-base monitoring keep this structure safe. **The bigger picture: three themes from the week** 1. Collateral keeps getting more esoteric. Data centers, cannabis inventory, luxury jewelry, scrap metal, DTC receivables, none of these fit a single, standardized model. The market’s growth is coming from exactly the assets that legacy tooling handles worst. 2. Structures are more operationally intensive. Revolvers, borrowing bases, factoring lines, and lender-finance pools all demand continuous monitoring, covenant tests, eligibility checks, advance-rate calculations, not a quarterly glance at a spreadsheet. 3. Capital is selective and increasingly paired with specialists. Gordon Brothers alongside Wells Fargo is the clearest example: banks are bringing in valuation, structuring, and domain expertise to get comfortable. The winners combine capital with data and know-how. **Takeaways** For lenders and asset managers reading the week, five things stand out: - Diversification is widening, not just deepening. Eight sectors in a single week, from a $1M factoring line to a $655M lender-finance refi, is real allocation opportunity, but only for those who can underwrite and monitor across very different asset types. - The edge has moved past the close. Originating and closing are commoditizing. The differentiator now is what happens once the asset is on the books: borrowing-base accuracy, covenant discipline, and reporting speed. - Esoteric is the new normal. Data centers, cannabis inventory, and commodity-linked collateral don’t fit legacy ABS templates. Modeling a deal across its full lifecycle, through ramp-up and reinvestment, against live market data, is becoming a baseline capability. - Partnerships signal where the value sits. Bank-plus-specialist structures (Gordon Brothers with Wells Fargo) and arranger-led syndications show capital pairing with valuation, structuring, and data expertise. Capital alone is no longer the moat. - Institutional and insurance capital raise the reporting bar. As these investors become major holders of private credit, auditable, granular, look-through data shifts from “nice to have” to table stakes. **How Cardo AI supports Lenders and Asset Managers** Every deal above is financeable. The harder question is whether your data and reporting infrastructure can keep up once it’s live, when availability has to be recalculated, covenants tested, cash flows projected, and investors reported to at institutional standard. That’s the gap Cardo AI closes as the end-to-end operating system for asset-based finance and private credit: - Clean, normalized data from anywhere. Ingest loan tapes and connect to source systems across 160+ integrations, so the eight-format reality of an eight-sector week becomes one consistent dataset. - Automated borrowing-base and eligibility logic. Keep availability live for inventory- and receivables-backed structures like Birks, Huron Valley Steel, and the DTC factoring line, not recalculated by hand in a spreadsheet. - Continuous covenant and compliance monitoring. Track tests and triggers in real time for regulated, high-touch credits like Cresco Labs and Cerus, with a full audit trail. - Cash-flow modeling built for esoteric assets. Model data-center and infrastructure deals like Applied Digital across their entire lifecycle, ramp-up, reinvestment, and stress scenarios, against live rate curves, where legacy ABS engines fall short. - Look-through analytics for lender finance. See the underlying pool, not just the top-line facility, on portfolio bets like the Elevate refinancing, the transparency these structures demand. - Institutional-grade investor reporting. Produce auditable, granular reporting from a single source of truth, built for the insurance and CLO capital now anchoring this market. More than $100 billion in assets are already managed on Cardo AI technology, across 160+ connected systems, with backing from Blackstone, FINTOP Capital, and JAM FINTOP. The market gets more complex every week, your operating model shouldn’t be the thing that slows you down. **Sources** *Deal data sourced from the [ABL Advisor Q2 2026](https://www.abladvisor.com/deal-tables) Deal Tables (transactions dated June 8–11, 2026). Disclosed deal detail is limited to lender/role, amount, borrower, industry, and structure as reported; pricing and tenor were not disclosed for most transactions. Source typographical inconsistencies have been standardized.* If you’re scaling into new asset classes or just tired of stitching data together in spreadsheets, [book a demo.](https://cardoai.com/book-a-demo/) --- ### [Weekly Deal Roundup | July 13 – 17, 2026](https://cardoai.com/article/weekly-deal-roundup-july-13-17-2026/) **Published:** July 20, 2026 **Author:** GiadaCardo26 **Content:** Contents [1. Introduction](#introduction) [2. CVC Marathon – $175M Equipment Loan](#first-par) [3. Siena – $105M Food Facility](#second-par) [4. BHI – $100M Capital Call Line](#third-par) [5. MidCap – $27M Bakery ABL](#fourth-par) [6. Haversine – Four Lender Finance Deals](#fifth-par) [7. SouthStar – $0.5M Working Capital](#sixth-par) [8. Takeaways](#seventh-par) [9. How Cardo AI helps asset managers and lenders](#eight-par) Nine asset-based finance deals crossed the ABL Advisor tables between 13 and 17 July, together worth around 446 million dollars across six sectors and six lenders. The largest facility grabbed the attention, yet the more telling pattern sat underneath it. More than half of the week’s capital went to other lenders and funds rather than to operating companies, a clear sign of how deeply private credit is now financing itself. Below we work through the deals that matter, what each one signals for investment and asset management, the risks worth watching and the opportunities they open. **CVC Marathon funds a $175M term loan for a specialty equipment platform** CVC Marathon provided a 175 million dollar secured term loan to Jack Doheny Companies, a Kinderhook-backed provider of specialty equipment rental, sales and service for the sewer, utility and environmental markets. Why it matters: This is one of the first visible asset-based moves under the platform CVC created when it completed its acquisition of Marathon Asset Management on 1 July, forming a multi-asset global credit manager with a serious asset-based lending franchise. A newly scaled manager choosing an equipment-backed term structure over pure corporate risk is a signal in itself. The loan ties repayment to a hard, essential fleet that serves municipal and utility operators. Key risks: A term loan gives the lender less dynamic control than a borrowing-base revolver, so residual value and re-marketability on a niche fleet carry more weight. End-market demand tracks municipal and utility capital budgets, which are steady but not immune to cycles. The borrower is private-equity owned and acquisitive, which layers leverage on an already asset-heavy balance sheet. Opportunities: Equipment-backed lending into essential infrastructure offers durable, contracted cash flow and hard collateral that holds value across cycles. For managers building out asset-based books, specialty rental fleets are an attractive way to pair yield with tangible security. **Siena Lending Group backs a frozen food processor with $105M** Siena Lending Group closed a 105 million dollar package for an undisclosed frozen food processor, split into a 70 million dollar revolver and a 35 million dollar commercial real estate term loan. Why it matters: The blended structure is the point. The revolver flexes with receivables and inventory and resets continuously through the borrowing base, while the term loan sits against cold-storage real estate that is scarce and expensive to replicate. Consumer staples give lenders defensive, non-cyclical demand at a time when many are cautious on discretionary exposure. Key risks: Perishable and seasonal inventory complicates eligibility and advance rates inside the borrowing base. Input cost volatility, energy exposure for frozen storage and food-safety or recall risk all sit close to the collateral. These books reward frequent, granular monitoring rather than periodic review. Opportunities: Blended structures let a single lender capture both working-capital spread and asset-backed term yield. Cold-storage real estate is a differentiated collateral class with real scarcity value, and defensive food demand supports through-cycle resilience. **BHI extends a $100M capital call line to KRE Management** BHI provided a 100 million dollar capital call line of credit to fund manager KRE Management. Why it matters: This is fund-level financing secured by uncalled limited partner commitments rather than operating-company collateral. Capital call lines let managers bridge to capital drawdowns, smooth deployment and manage the timing of investor calls. Their steady presence in the weekly tables shows how much subscription and fund finance now underpins private markets activity. Key risks: The credit rests on the quality of the limited partner base and the fund’s ability to call capital when needed. Facility sizing, advance rates against commitments and any drift towards net asset value based structures all deserve scrutiny. Look-through to the underlying fund and its investors is essential. Opportunities: Capital call and subscription lines are a scalable, relatively low-loss category for banks and specialty lenders, and demand continues to grow alongside private markets fundraising. For asset managers, they are an efficient tool to improve internal rates of return and manage liquidity. **MidCap Business Credit funds a $27M facility for a commercial bakery** MidCap Business Credit provided a 27 million dollar asset-based credit facility to an undisclosed commercial bakery. Why it matters: A second food-production deal in the same week reinforces the defensive tilt. Asset-based facilities let food producers finance receivables and inventory against a revolving line that moves with the business, which suits companies with steady demand and working-capital intensity. Key risks: Perishability and short shelf life constrain inventory eligibility. Ingredient cost inflation and customer concentration among grocers or distributors can pressure margins and dilute the borrowing base. Opportunities: Food staples are dependable working-capital lending. For lenders, the sector offers volume, repeatability and collateral that turns over quickly and predictably. **Haversine Funding backs four specialty lenders in a single week** Haversine Funding, a Dallas-based lender finance specialist, closed four separate facilities. They include a 20 million dollar participation in an accounts receivable facility for an electronic components distributor, a 10 million dollar senior secured facility for a transportation factor, a 7 million dollar factoring facility for an engineering and automation services provider, and a 2 million dollar facility for an early-stage specialty finance platform. Why it matters: This is financing the financiers. Haversine provides senior and junior capital, and participations, to factors and asset-based lenders so they can grow their own books and manage concentration. Four facilities from one lender in a single week captures how quickly the lender finance layer is compounding, and how banks retreating from warehouse and factoring lines have opened space for non-bank capital. Key risks: In lender finance the collateral is someone else’s collateral. Underwriting requires seeing through to the borrower’s own loan book, its underlying obligors, advance rates, dilution and concentration. Layered leverage without genuine look-through visibility is where problems hide, and smaller facilities carry the same monitoring load as large ones. Opportunities: Lender finance gives institutional capital access to diversified pools of granular, self-liquidating receivables through a single relationship. It is one of the fastest-growing corners of asset-based finance, and every dollar to a capable specialty lender enables multiples of downstream origination. **SouthStar Capital funds a $0.5M working-capital line** SouthStar Capital provided a 500,000 dollar purchase order and accounts receivable financing facility to an undisclosed consumer products company. Why it matters: The long tail of specialty ABL matters more than its size suggests. Purchase order and receivables lines are short-duration, self-liquidating and high-velocity, and they are often the entry point for a fast-growing borrower before it reaches larger facilities. Key risks: Operational intensity per dollar is high. Invoice verification, obligor concentration, dilution and fraud controls do not scale down just because the notional is small. Opportunities: Granular working-capital exposure offers attractive margins and short duration, and it builds relationships with growing companies early. Managed with the right infrastructure, the long tail is a genuine source of yield rather than an operational drag. **Takeaways** - Private credit is increasingly financing itself. Five of the nine facilities this week were capital for other lenders or funds, through lender finance and a capital call line. This is where much of the growth now sits, and it raises the premium on look-through reporting. - Newly scaled managers are competing in asset-based finance. CVC Marathon’s arrival, only weeks after the platform was formed, points to more competition for hard-asset, equipment-backed structures in the US market. - Consumer staples remain the defensive core. Two food-production facilities, one of them blended with real estate, show lenders leaning into non-cyclical demand and tangible collateral. - Structure is fragmenting inside a single week. Term loans, revolvers, a CRE term loan, a capital call line, factoring facilities and a participation all appeared in five trading days. Each carries a different monitoring cadence and a different data model, and a portfolio that mixes them needs one operational layer underneath, not six. **How Cardo AI helps Asset Managers and Lenders** The deals this week look nothing alike on the surface. A specialty equipment fleet, a frozen food plant, a fund drawing on its commitments and a set of factors all sit in the same roundup. What they share is an operational reality. Every one of them is a collateral and data problem before it is a credit problem. Cardo AI is the operational infrastructure that lets asset managers and lenders run all of it from a single, continuous view. That means ingesting loan tapes across any source or format, computing borrowing bases, eligibility and waterfalls, monitoring concentration, covenants and triggers, running portfolio analytics and stress testing, and producing institutional-grade investor and look-through reporting across the full spread of asset-based finance. From a 175 million dollar equipment-backed term loan to a 500,000 dollar purchase-order line, the operational requirement is the same. Collect, normalise, monitor and report on loan and collateral data continuously, at scale. That is the layer we are built for, and it is what turns a diversified, multi-structure ABF book into something a manager can actually see and control. **Sources** *[ABL Advisor deal tables](https://www.abladvisor.com/deal-tables) (July 13-17, 2026); company press releases via [Business Wire](https://www.businesswire.com/news/home/20260625720357/en/), [GlobeNewswire](https://www.globenewswire.com/news-release/2026/06/29/3318886/0/en/), [PR Newswire](https://www.prnewswire.com/news-releases/news-releases-list/) and [Bloomberg;](https://www.bloomberg.com/) [ABF Journal](https://www.abfjournal.com/category/news/); and issuer investor relations pages.* If you’re scaling an asset-based or private credit book, [we should talk. ](https://cardoai.com/company/contact-us/) --- ### [Weekly Deal Roundup | July 20 –24, 2026](https://cardoai.com/article/weekly-deal-roundup-july-20-24-2026/) **Published:** July 27, 2026 **Author:** GiadaCardo26 **Content:** Contents [1. Introduction](#introduction) [2. Hillman $1.1B ](#first-par) [3. Strata Clean Energy $450m](#second-par) [4. Kestra Medical Technologies $200m](#third-par) [5. Peachtree $150m](#fourth-par) [6. Bank of Montreal & Evolve Royalties $75m](#fifth-par) [7. MidCap Financial & Keslow Camera $65m](#sixth-par) [8. Eclipse $55m](#seventh-par) [9. The specialty tail](#eight-par) [10. Takeaways](#nine-par) [11. How Cardo AI helps Asset managers and Lenders](#ten-par) Eleven transactions crossed the tape last week, representing roughly $2.11 billion of announced capacity across eleven sectors. The range ran from a $1.1 billion syndicated refinancing for a listed hardware platform to a $1 million factoring line for a staffing firm. Read the week by size and it looks like a normal summer stretch. Read it by structure and something more interesting emerges. In almost every deal, the borrower did not simply pick the cheapest money. It picked the structure that matched the asset, and the structure then determined which lender could actually write the cheque. That distinction matters for anyone allocating to asset-based finance. Cash flow lenders, ABL desks, specialty finance firms and life sciences credit funds were all active in the same five days, and they were rarely competing for the same paper. One caveat worth stating before the detail. Almost every headline number last week is announced capacity rather than funded balance. Kestra’s $200 million facility had $75 million drawn at closing. Hillman’s $375 million ABL revolver sits at zero. Evolve’s $75 million is a $50 million commitment plus a $25 million accordion. Anyone building a market view from press releases is measuring commitments, not exposure. **Hillman Solutions: $1.1 billion across a Term Loan B and an ABL revolver** Hillman Solutions Corp. (Nasdaq: HLMN) closed a refinancing of its existing facilities on 22 July, made up of a $735 million senior secured Term Loan B and a $375 million asset-based revolving credit facility. Jefferies Finance acted as administrative agent on the term loan. U.S. Bank acted as lead arranger and administrative agent on the ABL, with Bank of America, PNC and Fifth Third as joint lead arrangers and First Financial Bank participating. The term loan matures in July 2033 and prices at SOFR plus 200 basis points. The revolver matures in July 2031 at SOFR plus 125 to 150 basis points, is currently undrawn, and splits into $325 million for the US borrower and $50 million for the Canadian borrower, each subject to a borrowing base. **Why it matters:** This is the clearest example of the week’s theme. One borrower, two facilities, two entirely different underwriting logics. The term loan is priced off enterprise value and leverage. The revolver is priced off eligible receivables and inventory, at 75 basis points inside the term loan, and costs almost nothing while it stays undrawn. Pricing on both facilities was consistent with the previous credit agreements, which tells you the market repriced maturity risk rather than credit risk. Extending a 2033 term loan and a 2031 revolver at flat spreads is a strong outcome in a market where many issuers are paying up for duration. **Key risks:** The borrowing base is now a live operational dependency in two currencies and two jurisdictions. Eligibility rules, reserves and dilution assumptions apply differently to the US and Canadian pools, and the availability calculation has to be reproducible on demand. Beyond that, watch inventory eligibility through a retail demand cycle, given Hillman’s exposure to home improvement and hardware retailers. Intercreditor mechanics between the term and revolving lenders also matter, since the collateral packages overlap and the priority split has to hold under stress. **Opportunities:** For ABL lenders, an undrawn $375 million commitment from an investment-grade-style syndicate is annuity income with modest capital intensity. For the borrower, dual-tranche structures create genuine optionality, because working capital swings can be absorbed by the revolver without touching the term loan covenant package. For asset managers, the broader opportunity is the unbundling itself. Every large sponsor-backed or listed issuer that separates cash flow debt from collateral debt creates a second, separately monitored exposure that needs its own data infrastructure. **Strata Clean Energy: $450 million revolving and letter of credit facility** Strata Clean Energy upsized its revolving loan and letter of credit facility by $150 million, taking it from $300 million to $450 million. The upsize closed on 9 July and was announced on 22 July. Nomura Securities International acted as sole bookrunner and coordinating lead arranger, the same role it held on the original $300 million facility in November 2023, with First Citizens Bank as coordinating lead arranger. Proceeds support the development, construction and operation of Strata’s renewable energy and storage projects, plus working capital for its engineering and operations divisions. **Why it matters:** Clean energy platforms have historically raised project-level debt against individual assets. This is corporate-level revolving capacity with a letter of credit line attached, which is a very different instrument. It funds the pipeline rather than a single project, and it puts the lender’s exposure against a rotating set of development-stage assets. The repeat mandate is the second signal. The 2023 arranger came back to lead the upsize, which says as much about information advantage as it does about spread. Lenders who already hold the data on a platform can underwrite an expansion faster than a newcomer can diligence it. **Key risks:** Construction and interconnection timelines dominate. Letter of credit utilisation rises with posted obligations, so the facility’s effective availability moves with commercial activity rather than with the calendar. Policy and tax credit dependencies remain a live variable for US renewables, and concentration by technology vintage and geography can build quietly inside a development pipeline. **Opportunities:** Infrastructure and power finance desks with the capability to monitor a moving pipeline can hold a position that pure project lenders cannot reach. For credit funds, the interesting adjacency is the letter of credit component, which is fee income against contingent exposure and is increasingly a place where non-bank capital can participate. **Kestra Medical Technologies: up to $200 million of non-dilutive term debt** Kestra Medical Technologies (Nasdaq: KMTS), a wearable cardiac device and digital health company, entered a five-year senior secured term loan facility of up to $200 million with funds managed by Pharmakon Advisors, investment manager of the BioPharma Credit funds. The deal was announced on 14 July and appeared on the ABL Advisor tape on 20 July. The structure runs in four parts. A $75 million tranche funded at closing and retired an existing $45 million term loan. A $25 million tranche is available at the company’s option through July 2027. A further $50 million is available through June 2028, conditional on the company generating $150 million of trailing twelve-month revenue. A final $50 million uncommitted tranche is reserved for acquisitions, subject to lender consent. The facility carries 48 months of interest-only payments, extendable by a further twelve months on a revenue milestone, and prices at three-month SOFR plus 5.50% with a 3.25% floor. **Why it matters:** This is a lender underwriting a commercial ramp, not a balance sheet. Kestra reported fiscal 2026 revenue of $95.1 million, up 59% year on year, and is not yet profitable. Traditional cash flow leverage metrics do not apply. What Pharmakon has done instead is convert the growth plan into a schedule. Capital is released as revenue milestones are hit, which caps downside if adoption slows and gives the borrower access to size if it does not. For a listed medtech company trading below where it would want to issue equity, that is a materially cheaper form of growth capital than dilution. **Key risks:** Milestone slippage is the obvious one. If trailing revenue does not reach $150 million by June 2028, a third of the facility never arrives, and the funding plan needs a replacement. A minimum liquidity covenant of $20 million sits underneath, and security extends over substantially all assets including intellectual property. Add single-product concentration and reimbursement policy exposure, and the credit is effectively a bet on one device category’s adoption curve. At SOFR plus 5.50% with a floor, the cost of carry on an unprofitable business is not trivial. **Opportunities:** Milestone-linked, non-dilutive structures are now a genuine competitor to late-stage equity, and the addressable universe extends well beyond biotech into devices, diagnostics and health technology. For specialist credit managers, the barrier to entry is not capital. It is the ability to track revenue, covenant and milestone data at the frequency the structure requires. **Peachtree Group: $150 million hotel portfolio bridge loan** Peachtree Group originated a $150 million bridge loan to refinance a four-hotel, 997-key portfolio in Flamingo Crossings Town Center, adjacent to Walt Disney World Resort in Winter Garden, Florida. The borrower, Doradus Partners, is a repeat client on its fourth transaction with the lender. JLL arranged the deal, which Peachtree closed in fewer than 45 days. It is the largest senior bridge loan in the firm’s history. The properties were developed in 2020 and 2021 and carry a weighted average occupancy of close to 89%. **Why it matters:** Speed closed this deal, not spread. A sub-45-day execution on a nine-figure hospitality refinancing is something most bank credit committees cannot promise, and the sponsor paid for certainty. That is the private credit value proposition in commercial real estate stated as plainly as it gets. The repeat borrower dynamic is equally telling. A fourth transaction means the lender already holds performance history, operating data and asset-level familiarity, which compresses diligence and improves pricing confidence on both sides. **Key risks:** Hospitality cash flow is seasonal and demand-sensitive, and this portfolio’s performance is tied to one destination market. Bridge structures carry refinancing risk at maturity, which depends on where permanent debt is available in eighteen to thirty-six months. Concentration is high by location, brand family and sponsor. **Opportunities:** Well-located, recently built assets with strong occupancy are exactly the collateral private credit wants while banks remain selective on hospitality. For managers, the scalable version of this is a repeat-borrower origination model, where relationship data lowers acquisition cost per deal. That only works if asset-level performance data is captured systematically rather than living in a deal team’s inbox. **Bank of Montreal and Evolve Royalties: $75 million secured revolver** Evolve Royalties Ltd. (CSE: EVR) entered a credit agreement with Bank of Montreal for a secured revolving credit facility of $50 million, with an option to increase to $75 million. The facility matures in three years, is secured by a charge over the company’s assets, and can be used for general corporate purposes and to finance acquisitions. Margins range from 1.50% to 2.50% depending on the company’s net leverage ratio. **Why it matters:** A royalty company’s balance sheet is a portfolio of contractual claims on future production. A bank writing a leverage-based revolver against that portfolio is treating royalty streams as a financeable, monitorable asset class rather than as an equity story. For a small-cap acquirer of base and critical metals royalties, committed revolving capacity is a competitive weapon. It allows the company to close acquisitions without going to the equity market first, which is often the difference between winning and losing a royalty auction. **Key risks:** Royalty income depends on operator performance, commodity prices and mine life, none of which the royalty holder controls. The leverage-based pricing grid means the cost of capital rises exactly when commodity prices fall and leverage climbs. Portfolio concentration in a small number of producing assets amplifies both effects. **Opportunities:** Royalty and streaming finance continues to move towards a recognised specialty credit category, and last week’s transaction sits alongside similar activity in preceding weeks. For lenders, the attraction is contractual cash flow with no operating cost exposure. The requirement is the ability to track production, price and payment data across many operators and jurisdictions on a continuous basis. **MidCap Financial and Keslow Camera: $65 million senior secured facility** MidCap Financial provided a $65 million senior secured credit facility to Keslow Camera, a motion picture and digital cinema camera rental business. Proceeds refinanced existing debt, simplified the capital structure and provided additional liquidity for operations, growth and working capital. **Why it matters:** Cinema equipment is genuinely hard collateral. Cameras, lenses and support gear hold value, have an active secondary market, and can be appraised with reasonable confidence. What makes the asset class demanding is that the collateral moves constantly between productions, locations and countries. This is the second consecutive week in which cinema equipment financing appeared on the tape, following a facility for another rental platform. Content production capex is being refinanced by specialty lenders while generalist capital stays cautious on media. **Key risks:** Utilisation is the whole credit. Rental fleets earn only when deployed, and production volumes are cyclical and exposed to studio spending decisions. Technology obsolescence sits underneath residual values, and asset tracking across a mobile fleet is an operational challenge, not a theoretical one. **Opportunities:** Lenders who can monitor utilisation, location and appraised value at the individual asset level can lend more comfortably, and more cheaply, than those relying on periodic third-party appraisals. That capability gap is where specialty equipment finance margins actually come from. **Eclipse Business Capital and a frozen food supplier: $55 million ABL facility** Eclipse Business Capital provided a $55 million asset-based lending credit facility to an undisclosed frozen food supplier. **Why it matters:** Food distribution is a classic ABL sector, and a classic monitoring challenge. Inventory is perishable, seasonal and cold-chain dependent. Receivables concentrate in a handful of large grocery and food service customers with long payment terms. **Key risks:** Inventory eligibility can move quickly with shelf life and cold storage conditions. Customer concentration in receivables creates single-name exposure. Commodity input costs and freight compress margins with little warning. **Opportunities:** For lenders with real inventory analytics, this is exactly the collateral where advance rates can be set with confidence rather than conservatism. The competitive advantage comes from measuring the borrowing base more frequently and more accurately than the next lender. **The specialty tail** Four smaller transactions completed the week, and they matter more than their size suggests. Utica Equipment Finance wrote a $9.2 million capital lease for a pipe and steel manufacturer. Briar Capital Real Estate Fund provided a $5 million real estate loan to an industrial equipment company. nFusion Capital opened a $5 million factoring facility for a media production company. First Business Bank provided a $1 million factoring facility to a staffing firm. These are four different collateral types, four different structures and four different monitoring cadences. Factoring requires invoice-level verification. Capital leases require equipment schedules and residual tracking. Owner-occupied real estate lending requires valuation and covenant monitoring. Each is operationally distinct, and each demands the same underlying discipline. The point is not the aggregate dollars. It is that a diversified specialty credit portfolio is a collection of these, and the operational burden compounds faster than the assets under management. **Takeaways** - **Borrowers are unbundling their capital stacks.** Hillman separated enterprise debt from collateral debt and priced each with the lender base best suited to it. Expect more issuers to run parallel term and asset-based facilities rather than a single blended structure. - **Non-dilutive credit is competing directly with equity.** Kestra’s milestone-linked facility is a growth financing instrument dressed as a term loan. The structure works because the lender is willing to release capital against operating data rather than against collateral. - **Execution speed is a pricing input.** Peachtree closed a $150 million hospitality bridge in under 45 days for a fourth-time borrower. Certainty of close, backed by existing performance data, is worth real basis points to sponsors. - **Contractual cash flow is now mainstream collateral.** Royalty streams, contracted energy revenue and rental fleet income all cleared bank and specialty balance sheets in a single week. The common requirement is continuous, granular data on the underlying performance. - **Announced capacity is not funded exposure.** Undrawn revolvers, accordions, optional tranches and milestone-gated commitments made up a meaningful share of last week’s headline numbers. Portfolio-level reporting that cannot distinguish committed, available and funded amounts will systematically misstate risk. **How Cardo AI helps Asset managers and Lenders** Look across last week’s eleven transactions and the operational requirement is identical at every scale. A $1.1 billion syndicated refinancing and a $1 million factoring line both need the same things done well, continuously, and without a spreadsheet in the critical path. Cardo AI provides the operating layer for asset-based finance and private credit portfolios, covering the full lifecycle from loan tape ingestion to investor reporting. - Loan tape ingestion across any source or format. Servicer files, originator extracts, agent reports and bank feeds normalised into a single data model, whatever the asset class. - Borrowing base, eligibility and waterfall computation. Advance rates, eligibility criteria, reserves and concentration limits calculated automatically and reproducibly, including across multi-currency and multi-jurisdiction collateral pools. - Covenant, trigger and concentration monitoring. Continuous testing against facility terms, with alerts before a breach becomes a conversation with investors. - Portfolio analytics and stress testing. Scenario analysis across vintages, sectors, geographies and structures, so that concentration is visible before it becomes correlated. - Institutional-grade investor reporting. Consistent, auditable reporting packages produced on schedule rather than assembled under pressure. - Multi-asset-class onboarding. From aviation and rail fleets to skilled nursing portfolios, camera rental equipment to frozen food inventory, new asset classes onboard in weeks rather than quarters. The lenders who took size last week were, in almost every case, the ones who already held the data. Repeat mandates, faster closings and tighter pricing all traced back to information advantage. That advantage is built, not bought. If you are scaling an ABF or private credit platform and your operational infrastructure is becoming the constraint on origination, we should talk. **Sources** *[ABL Advisor deal tables](https://www.abladvisor.com/deal-tables) (July 20-24, 2026); company press releases via [Business Wire](https://www.businesswire.com/news/home/20260625720357/en/), [GlobeNewswire](https://www.globenewswire.com/news-release/2026/06/29/3318886/0/en/), [PR Newswire](https://www.prnewswire.com/news-releases/news-releases-list/) and [Bloomberg;](https://www.bloomberg.com/) [ABF Journal](https://www.abfjournal.com/category/news/); and issuer investor relations pages.* If you’re scaling an asset-based or private credit book, [we should talk. ](https://cardoai.com/company/contact-us/) --- ### [Weekly Deal Roundup | July 27–31, 2026](https://cardoai.com/article/weekly-deal-roundup-july-27-31-2026/) **Published:** August 3, 2026 **Author:** GiadaCardo26 **Content:** Contents [1. Introduction](#introduction) [2. Zeta Global – $1.0B](#first-par) [3. Aperture Investors and Avant – $300M](#second-par) [4. Wells Fargo and Stellify Capital – $75M](#third-par) [5. Eclipse Business Capital – $55M](#fourth-par) [6. BOK Financial and Excedr – $17M](#fifth-par) [7. BizCap and Project Bravo – $12.5M](#sixth-par) [8. Stifel Venture Banking – $50MM](#seventh-par) [9. Also announced](#nine-par) [10. Takeaways](#nine-par) [11. How Cardo AI helps asset managers and lenders](#ten-par) **$1.47 billion in announced private credit and asset-based finance transactions across nine deals and six sectors. One eight-bank club took two thirds of the volume. Almost everything else went to lenders funding other lenders.** Last week produced an unusually clean picture of how capital is being allocated across the credit market right now. At the top, a syndicate of eight banks refinanced a scaled technology borrower with a revolver-heavy, five-year facility at tighter spreads. That is the syndicated bank market competing directly with private credit for the borrowers it wants, and winning. Below it, $392 million moved into lender finance across three separate transactions, from a money-centre bank, a regional bank and an alternative asset manager. Different balance sheets, same product. Each dollar lent to a specialty lender or lessor supports a multiple of downstream origination in the real economy. And underneath that, five facilities ranging from $55 million to $1.8 million kept the working capital machinery of building supplies, government logistics, real estate and energy services running. Here is what happened, and what it means for the people who have to underwrite, fund and monitor this paper. **Zeta Global: $1.0 billion, eight banks, three quarters undrawn** Zeta Global closed a new $1 billion credit facility to refinance an existing $550 million facility and lower its credit spreads, combining a $250 million Term Loan A with a $750 million revolving credit facility that remained undrawn at the time of closing. The five-year senior secured facility was arranged by a consortium led by Bank of America, replacing an August 2024 credit agreement and immediately repaying $200 million of obligations under the prior facility, with interest tied to SOFR or a base rate and margins linked to the company’s consolidated net leverage ratio, subject to a leverage cap that can temporarily increase following sizeable acquisitions. BofA Securities acted as lead arranger and bookrunner, with Citi, JPMorgan, RBC Capital Markets and Truist Securities as joint lead arrangers and bookrunners. Flagstar Bank and Morgan Stanley took co-documentation agent roles, with MUFG participating. Why it matters: The structure is the story. This is a Term Loan A, not a Term Loan B, which means bank-held, amortising, covenanted paper rather than institutional term loan paper distributed to CLOs and credit funds. Three quarters of the commitment is a revolver that was undrawn at close, so the banks are being paid commitment fees to hold capacity rather than deploy it. That is a liquidity instrument, not a leveraged capital structure. For anyone tracking the boundary between syndicated bank lending and private credit, this is a data point on the bank side of the line. A large, cash-generative borrower with roughly $1.3 billion of 2025 revenue went to a club of eight relationship banks and came away with a bigger facility at a lower spread. Direct lenders were not part of that conversation. Key risks: There is no hard collateral here in any asset-based sense. The credit is enterprise value, recurring revenue and covenant compliance, so recovery in a stress case depends on going-concern value rather than liquidation value. The margin grid is tied to consolidated net leverage, which means pricing worsens exactly when performance does. The leverage cap can step up temporarily after sizeable acquisitions, which is standard but does allow reported leverage to run higher through an integration period. And $750 million of undrawn capacity earmarked for acquisitions and share buybacks is optionality that can be used to lever up as easily as to build resilience. Opportunities: For the arranging banks, the real return on a revolver-led club is ancillary: treasury management, foreign exchange, payments and future capital markets mandates. That is why banks will price a revolver aggressively and still make the relationship work. For asset managers, the read-across matters more than the deal. If banks are willing to reclaim borrowers of this size on these terms, spread compression is coming for the upper end of direct lending, and the differentiated return sits further down in size or further into asset-based structures. **Aperture Investors and Avant: $300 million, and an asset manager writing the warehouse** Aperture Investors, an alternative asset manager and part of Generali Investments, led a $300 million asset-based financing facility with Avant, a financial technology company, providing flexible capital to support Avant’s continued growth across its platform and building on a longstanding relationship between Aperture’s Asset-Based Finance team and Avant. Aperture’s ABF strategy launched in 2025 and is led by Global Head of Asset-Based Finance and Portfolio Manager Nick Turgeon. The facility will primarily support the expansion of Avant’s credit card business. Avant, founded in 2012, runs a data-driven, multi-product platform giving middle-income US consumers access to credit cards and personal loans, and has reached over 4.5 million unique customers, supplying more than $13.7 billion in personal loans and upwards of 3.1 million credit cards. Why it matters: This was the largest pure asset-based transaction of the week, and the capital did not come from a bank. An alternative asset manager underwrote and led a facility secured by consumer receivables, for a platform that both originates and services them. Five years ago this was a bank warehouse or an ABS take-out. Now it is a fund product. That migration is the single most important structural shift in credit at the moment, and this deal shows what it requires. Aperture launched the strategy in 2025 and is already writing $300 million tickets against granular consumer collateral. Doing that credibly means having asset-level surveillance capability that looks a lot like a bank’s, on a manager’s timeline. Key risks: Consumer credit performance is the obvious one, particularly on credit card receivables where balances revolve and losses emerge later than in an instalment book. Advance rates have to be set against cumulative net loss curves that may not be stable through a softening labour market. There is originator and servicer concentration, because Avant is both, which raises back-up servicing and operational continuity questions. And eligibility drift is a live risk in a granular, fast-amortising pool: the mix of what is being originated can change materially inside a single reporting period, so borrowing base integrity depends on frequent, accurate data rather than on quarterly certificates. Opportunities: Short-duration, granular consumer pools reprice quickly, so spread capture is fast when the credit box holds. Facilities like this are also pipelines. A performing warehouse is the pre-condition for a term securitisation and eventually a rated exit, which is where the funding cost step-down and the manager’s own return live. For managers with genuine daily monitoring capability, this segment offers a return premium that exists precisely because most managers cannot operate at that data cadence. **Wells Fargo and Stellify Capital: $75 million into the lender, not the assets** Stellify Capital, a specialty finance platform serving the third-party originations channel, closed an up to $75 million credit facility with Wells Fargo Bank, following the company’s announcement in January of a $75 million facility from Sun Life Capital Management and the launch of Stellify in September 2025. Stellify is led by the former full leadership team of Pawnee Leasing. The firm focuses on purchasing small-ticket commercial equipment leases and loans of B, C and start-up credit quality exclusively from third-party originators and other lessors. Why it matters: This is the clearest lender finance transaction of the week. Wells Fargo is not underwriting a fleet of forklifts. It is underwriting the platform that underwrites the forklifts. The bank gets collateral diversification across thousands of small-ticket contracts, structural seniority above the platform’s equity, and a far lower operational cost per dollar deployed than it would achieve lending directly to those obligors. It is also a relationship trade. Wells Fargo’s lender finance team had lent to this management team before, at Pawnee Leasing, and re-underwrote them at a new platform less than a year after launch. Lender finance remains one of the few segments where banks still compete hard on price and where twenty-year relationships genuinely determine outcomes. Key risks: The bank’s true exposure is look-through, to the underlying small-ticket obligors, not to Stellify itself. That exposure sits in credit tiers described as B, C and start-up, which is where loss volatility is highest in a slowdown. There is double leverage in the structure, and borrowing base integrity depends entirely on the platform’s own eligibility definitions, servicing discipline and reporting accuracy. Stellify is roughly ten months old and now has two $75 million facilities from different capital providers, so intercreditor arrangements and collateral allocation between them deserve attention. Opportunities: For the specialty lender, bank capital at the top of the stack lowers blended cost and makes the equity return work. For the bank, the segment offers diversification and stickiness that direct middle-market lending does not. And for asset managers watching the space, every dollar of lender finance is a leading indicator: it funds origination capacity that will show up as loan and lease paper looking for permanent capital six to twelve months from now. **Eclipse Business Capital: $55 million against working capital, into a soft cycle** Eclipse Business Capital provided a $55 million asset-based lending credit facility to a regional building materials supplier. The facility, secured by the company’s working capital assets, refinanced the company’s existing bank debt and provides additional liquidity and flexibility for ongoing working capital needs. Eclipse, founded in 2016, is one of the country’s leading independent asset-based lending platforms, providing revolving lines of credit and term loans secured by collateral such as accounts receivable, inventory, equipment, real estate and intellectual property. Why it matters: This is textbook asset-based lending, and the timing is the point. A regional building materials supplier is a cyclical distributor facing a soft construction market. A cash-flow lender looks at that and sees compressing EBITDA. An asset-based lender looks at the same company and sees receivables from contractors and inventory in stores, and lends against the collateral instead. The facility also refinanced existing bank debt, which is the classic pattern of a bank stepping back and an independent ABL platform stepping in. Key risks: Inventory valuation is the crux. Net orderly liquidation value on building materials moves with commodity prices, particularly lumber and steel, and appraisals can lag reality in a falling market. Seasonality affects both sales and inventory build. Receivables from contractors carry real dilution risk through disputes, retainage and slow pay, and customer concentration among a handful of large contractors amplifies that. Borrowing base drift is the thing to monitor, because eligibility erodes quietly through ageing and cross-ageing well before a covenant is tripped. Opportunities: Asset-based lending is counter-cyclical by design. It wins market share exactly when cash-flow lenders retreat, and it prices best when alternatives are scarce. For an independent platform with committed capital, a construction slowdown is an origination opportunity rather than a threat, provided the collateral monitoring is genuinely continuous rather than a monthly certificate exercise. **BOK Financial and Excedr: $17 million into life sciences equipment** Excedr, a provider of full-service laboratory equipment leasing solutions for the life sciences and healthcare industries, closed $25 million in growth capital, including a $17 million senior credit facility from BOK Financial and $8 million in equity from existing investors and global single-family offices. The BOK facility consists of a revolving line of credit and a term loan structured to support the continued expansion of Excedr’s equipment leasing portfolio. Excedr serves both emerging biotech companies and established industry players, supported by long-standing relationships with OEMs including Danaher, Sartorius and Bio-Rad. BOK Financial’s asset-based lending group specialises in structuring flexible credit facilities of $5 million to $50 million supported by accounts receivable, inventory and equipment. Why it matters: Small ticket, instructive structure. This is lender finance again, this time from a regional bank into a lessor, and it demonstrates that the product is not confined to large-cap platforms. The collateral is laboratory equipment with identifiable OEM provenance and a real secondary market, which is precisely the kind of hard asset that supports a blended revolver and term loan structure. It is also a signal about how life sciences companies are funding themselves. Leasing mission-critical equipment rather than buying it preserves equity and working capital, and that preference strengthens when biotech equity markets are tight. Key risks: Residual value on specialised laboratory instruments is the primary exposure, and re-marketability varies enormously between a benchtop centrifuge and a highly configured analytical system. Lessee credit quality is the second: emerging biotech companies are funding-dependent counterparties whose ability to pay is tied to capital markets access rather than to operating cash flow. Concentration by lessee, by OEM and by therapeutic area all need monitoring, and equipment redeployment costs can materially erode recovery assumptions. Opportunities: Equipment leasing into life sciences pairs hard collateral with a structural demand tailwind. For the lessor, deeper OEM partnerships improve both acquisition cost and remarketing outcomes, which is what makes the residual assumptions defensible. For lenders, a facility of this size with real asset coverage and a defined secondary market is exactly the profile that scales into a larger warehouse if the platform performs. **BizCap and Project Bravo: $12.5 million structured around the operating cycle** BizCap, a national commercial finance and debt advisory firm, closed a $12.5 million senior secured revolving credit facility for Project Bravo, a government-contract fulfilment and distribution firm providing sourcing, procurement and logistics solutions to government agencies and government-related customers, and a portfolio company of TEEDUP. According to the company, the transaction required a structure built around the full operating cycle rather than a conventional accounts receivable-only solution, because the company pays suppliers before collecting from its government customers. The facility increases borrowing capacity as the business grows following its 2024 acquisition, and restructures existing debt obligations to provide greater financial flexibility. Why it matters: The interesting detail is why an accounts receivable line was not enough. Government contract fulfilment involves paying suppliers well ahead of collection, so the funding gap sits in inventory and in-transit goods rather than in billed receivables. Structuring around that means giving credit for stages of the working capital cycle that a standard formula ignores, which is harder to underwrite and much harder to monitor. Key risks: Government receivables have excellent ultimate credit quality and poor payment timing, so the exposure is liquidity rather than loss. Appropriations risk, contract renewal risk and administrative delay all extend days sales outstanding. Inventory eligibility in a fulfilment model requires clear title, location control and evidence of contract allocation. And a sponsor-owned platform integrating a 2024 acquisition adds execution risk on top of the operating cycle risk. Opportunities: Facilities that credit the whole working capital cycle command better economics than plain receivables lines because fewer lenders can underwrite them. For lenders able to monitor inventory and in-transit collateral with the same rigour as receivables, this is a defensible niche with a counterparty, the US government, that ultimately pays. **Stifel Venture Banking to Shoulder Innovations, $50MM medtech venture debt** Stifel Venture Banking provided Shoulder Innovations, a medical device company, with a $15 million term loan, a $30 million undrawn line of credit and a $5 million accordion, $50 million of total capacity. Why it matters: Medtech joins biotech in the non-dilutive venture debt story. A staged structure, drawn term loan plus undrawn capacity plus accordion, lets a growth-stage device company fund commercialisation without giving up equity, and gives the lender optionality tied to performance. Key risks: Commercialisation execution, regulatory and reimbursement pathways for devices, and cash runway against undrawn capacity that depends on milestones. Opportunity: Venture lenders who can monitor drawdown conditions, covenants and cash runway continuously can support the full commercial journey rather than a single tranche. **Also announced** Three smaller transactions rounded out the week and are worth noting for the shape of the market rather than the size of the cheques. Modern Banking Group provided a $4.3 million recapitalisation loan on a real estate asset. Briar Capital Real Estate Fund provided a $2.1 million real estate loan to a beverage products company, the kind of owner-occupied property financing that often sits alongside a working capital revolver. National Business Capital provided a $1.8 million term loan to Interstate Industrial in energy services. Small facilities, but the same underlying requirement: collateral to value, monitor and report on. **Takeaways** - Lender finance was the week’s real story. Three facilities totalling $392 million went to lenders and lessors rather than to operating companies, funded by a money-centre bank, a regional bank and an alternative asset manager. That is the same product being written from three completely different balance sheets, and it is one of the clearest indicators of where origination capacity is being built. - The syndicated bank market is defending the top end. An eight-bank club, a Term Loan A structure, a mostly undrawn revolver and tighter spreads than the facility it replaced. For scaled, cash-generative borrowers, banks are competing hard and private credit is not always in the room. Managers should expect that competitive pressure to work its way down the size curve. - Asset managers are now writing warehouse lines directly. The Aperture and Avant facility is a bank product on a fund balance sheet. This is where asset-based finance is heading, and it raises the operational bar considerably. Underwriting granular consumer collateral at $300 million scale requires asset-level surveillance that most fund infrastructure was never built to deliver. - Structure is diverging from size. A $12.5 million facility structured around the full working capital cycle is operationally more demanding than a $1 billion revolver. Complexity and ticket size have decoupled, which means resourcing and monitoring cannot be allocated by deal size alone. - The tail keeps working. Five facilities from $55 million down to $1.8 million, across building supplies, government logistics, real estate and energy services, in five working days. This is the part of the market that never makes headlines and never stops compounding. **How Cardo AI helps Asset Managers and Lenders** Every transaction above creates the same operational obligation, regardless of ticket size. Someone has to collect the data, normalise it, test it against the structure, monitor it continuously and report it to investors and lenders in a form they trust. A $1 billion syndicated revolver requires covenant and utilisation tracking across eight institutions. A $300 million consumer warehouse requires daily eligibility testing against a pool that turns over constantly. A $75 million lender finance facility requires look-through into thousands of small-ticket contracts that the borrower, not the lender, originated. A $12.5 million government logistics revolver requires inventory and in-transit collateral to be tracked with the same discipline as billed receivables. Cardo AI is the operating system for that work: - Loan tape ingestion across any source or format, so data from originators, servicers and trustees arrives in one normalised model rather than in a hundred spreadsheets. - Borrowing base, eligibility and waterfall computation, automated and auditable, with the calculation logic transparent rather than buried in a template. - Concentration, covenant and trigger monitoring, tested continuously rather than at month end, so eligibility drift and covenant pressure surface while there is still time to act. - Portfolio analytics and stress testing, including cash flow projection and scenario analysis at loan and pool level. - Institutional-grade investor and lender reporting, generated from the same underlying data rather than reconstructed manually each period. - Multi-asset-class onboarding, from equipment lease books and consumer card receivables to inventory-heavy ABL facilities and commercial real estate. The lenders in this roundup operate across consumer credit, equipment leasing, building materials, government logistics, life sciences and technology. The asset classes have almost nothing in common. The data problem is identical. **Sources** *[ABL Advisor deal tables](https://www.abladvisor.com/deal-tables) (July 27–31, 2026); company press releases via [Business Wire](https://www.businesswire.com/news/home/20260625720357/en/), [GlobeNewswire](https://www.globenewswire.com/news-release/2026/06/29/3318886/0/en/), [PR Newswire](https://www.prnewswire.com/news-releases/news-releases-list/) and [Bloomberg;](https://www.bloomberg.com/) [ABF Journal](https://www.abfjournal.com/category/news/); and issuer investor relations pages.* If you’re scaling an asset-based or private credit book, [we should talk. ](https://cardoai.com/company/contact-us/) --- ### [Weekly Deal Roundup | August 3 – 7, 2026](https://cardoai.com/article/weekly-deal-roundup-august-3-7-2026/) **Published:** August 10, 2026 **Author:** GiadaCardo26 **Content:** Contents [1. Introduction](#introduction) [2. Avantus $1.05B](#first-par) [3. Outside Interactive $50M](#second-par) [4. Endeavour Silver $25M](#third-par) [5. Garrington Capital $20M](#fourth-par) [6. The specialty tail](#fifth-par) [7. Takeaways](#nine-par) [8. How Cardo AI helps Asset managers and Lenders](#ten-par) Eight transactions were announced across asset-based finance and private credit in the first full week of August, totalling roughly $1.16 billion. One of them accounted for ninety per cent of that figure. That distribution is the story. A single utility-scale solar developer pulled eleven lead arrangers into a $1.05 billion corporate credit facility. In the same five days, a Midwest commercial printer lost its bank relationship and replaced it with a specialty finance package, a mid-tier silver producer put a revolver in place it has no intention of drawing, and a small-business lender that had been turning away originations finally got the capital to fund them. For asset managers and lenders, the interesting question is not which deal was biggest. It is what each structure tells you about where credit capacity is concentrating, what is being accepted as collateral, and how much of the underwriting now depends on the quality of somebody else’s reporting. Here is what closed, why it matters, and what it means for portfolio construction and monitoring. **Avantus: $1.05 billion corporate credit facility** Avantus closed an upsized $1.05 billion corporate credit facility, roughly doubling the $522 million line put in place in July 2024. The capital supports the company’s transition from developer to independent power producer across a 24GW pipeline in California and the Desert Southwest, including 13GW of solar paired with 44GWh of storage. KKR Capital Markets and EIG Capital Markets acted as placement agents. **Why it matters:** Six of the eleven lead arrangers are new to this credit. That is the signal worth extracting. Bank appetite has not disappeared, it has become highly selective, and it is clustering around contracted, infrastructure-like cash flows with visible asset backing and an experienced sponsor group. This is also corporate-level credit doing a job that project finance used to do alone. Rather than financing each asset in its own ring-fenced SPV, Avantus is funding development and construction velocity at the platform level, then terming out individual projects separately. For credit investors, that changes where the risk sits and how it should be monitored. **Key risks:** - Development-stage execution. Pipeline value depends on interconnection queues, permitting, and equipment delivery. Very little of a 24GW pipeline is contractually certain. - Merchant exposure. Uncontracted capacity carries power price risk that a PPA-heavy portfolio does not. - Geographic concentration. California and the Desert Southwest dominate the portfolio, so regulatory and grid conditions in a narrow set of markets drive outcomes. - Structural subordination. Corporate-level lenders sit behind project-level debt at the assets themselves. - Refinancing dependency. The strategy assumes projects can be termed out on schedule at the project level. **Opportunities:** Energy transition credit is maturing into a category with repeatable structures and a widening lender base. For managers building infrastructure-adjacent private credit exposure, this is a chance to underwrite platform-level risk at spreads that still reflect development uncertainty, with hard assets and long-dated contracted revenue behind them. The arrival of six new arrangers suggests secondary and participation opportunities will follow. **Outside Interactive: $50 million senior secured credit facility** Vector Velocity completed a $50 million senior secured credit investment in Outside Interactive, the outdoor media and technology platform behind Outside, Gaia GPS, Trailforks, MapMyFitness, Pinkbike, Inntopia and roughly twenty other brands. The business reaches more than 70 million consumers a month and serves over one million paying subscribers. GP Bullhound advised Outside. **Why it matters:** This is a senior secured facility where the collateral base is subscription revenue, app engagement and brand equity rather than receivables, inventory or equipment. It sits at the boundary between asset-based lending and recurring revenue lending, and that boundary is where a lot of private credit growth is happening. The underwriting question is no longer “what can we liquidate” but “how predictable is the cash flow and how quickly does it decay if the business stops investing”. Answering that requires cohort-level data on churn, renewal rates and revenue concentration, refreshed continuously. **Key risks:** - Subscriber churn. Consumer subscription businesses show meaningful attrition, and a small change in monthly churn compounds quickly through a facility’s life. - Advertising cyclicality. Media revenue is discretionary spend and moves with the ad cycle. - Brand and app concentration. A small number of properties are likely to drive a disproportionate share of profitable revenue. - Intangible-heavy collateral. Enforcement value in a downside case is thin compared with hard assets. - Integration risk. Twenty-five brands means twenty-five sets of reporting, and consolidated visibility is not automatic. **Opportunities:** Technology-enabled businesses with recurring revenue are increasingly willing to take senior secured debt rather than dilute equity. For lenders able to underwrite and monitor revenue quality with the same discipline traditionally applied to a borrowing base, this is a growing origination channel with limited bank competition. The lenders who win it will be the ones who can ingest product-level revenue data, not just monthly financial statements. **Endeavour Silver: $25 million revolving term credit facility** Endeavour Silver executed a secured revolving term credit facility for up to $25 million, with an accordion of up to a further $75 million subject to lender consent, for a maximum facility size of $100 million. The initial term runs three years to August 2029, extendable in one-year increments. Initial drawdown requires all material subsidiaries to guarantee the facility and grant first-lien security over present and after-acquired personal property. Pricing is Term SOFR or base rate plus a margin set by reference to covenant performance. The company has said it does not intend to draw immediately, establishing the facility to create flexibility as the Pitarrilla project advances. **Why it matters:** This is a liquidity option rather than a funding event, and it deserves attention for that reason. A committed but undrawn facility with a 4x accordion gives a mid-tier producer the ability to move on acquisitions or cover a production shortfall without issuing equity at a weak price. For credit investors, undrawn commitments create a different risk profile from funded loans. Utilisation is negatively correlated with borrower health, which means exposure tends to appear exactly when the credit is deteriorating. **Key risks:** - Commodity price volatility. Silver and gold prices drive covenant headroom and therefore pricing. - Jurisdictional concentration. Operations sit in Mexico and Peru, with the associated permitting, tax and community risk. - Adverse utilisation. Drawdown is most likely under stress. - Accordion is not committed. The path from $25 million to $100 million depends on lender consent at the time. - Development capital needs. Pitarrilla will require capital well beyond this facility. **Opportunities:** Miners with producing assets and undrawn capacity are attractive counterparties for lenders who can price optionality properly. Commitment fees on undrawn balances offer yield with no funding drag, provided the manager has the systems to track utilisation triggers, covenant headroom and pricing grid movements in real time rather than at quarter end. **Garrington Capital: $20 million revolving credit facility** Garrington provided a $20 million revolving line of credit to a US company that makes short-term loans to small and mid-sized businesses. The borrower had been capital constrained, at times declining opportunities it could not fund. Garrington structured the facility around the borrower’s collateral, using its reporting to calculate availability with precision. **Why it matters:** Lender finance is the highest-leverage category in the roundup. Every dollar advanced here funds multiples of downstream origination to small businesses that will never appear in a deal table. It is also the category where operational infrastructure is most directly a credit term. Garrington’s own explanation of the deal makes the point: the facility was structured around the borrower’s collateral because the borrower’s reporting was detailed and data-driven enough to size availability with confidence. Specialty lenders that cannot produce clean, granular, frequent collateral data do not get the facility, or they get a materially worse advance rate. **Key risks:** - Look-through credit quality. The real exposure is the underlying small-business obligors, not the borrower. - Eligibility and advance rate drift. Definitions loosen quietly as portfolios grow. - Reporting integrity. If availability is calculated from borrower-supplied data, data quality is credit risk. - Correlation. Short-term small business lending concentrates in the same macro shocks across a whole pool. - Rapid asset turn. Short-duration receivables mean the collateral pool is substantially different every 60 to 90 days. **Opportunities:** Lender finance offers diversified exposure to hundreds or thousands of underlying obligors through a single facility, with structural protection at the borrower level. For managers with the ability to ingest and reconcile loan tapes continuously, it is one of the more efficient ways to build granular credit exposure. The constraint on growth in this category is operational capacity, not deal flow. **The specialty tail** $15.8 million, four lenders, four collateral types. Four smaller transactions closed alongside the headline deals, and together they describe the working end of the market. - **nFusion Capital, $8.0 million, commercial printing.** A Midwest printer serving publishers nationwide needed more liquidity than its bank was prepared to provide. When the bank elected to exit the relationship, a referral brought the company to nFusion, which structured an accounts receivable factoring line alongside loans secured by inventory and equipment, on an accelerated timeline. - **Salem Five Business Credit, $4.0 million, consumer products.** A revolving credit facility for NorEast Outdoors, a Massachusetts designer and distributor of outdoor recreation products, funding inventory purchases, overseas manufacturing deposits and seasonal working capital needs. - **Utica Equipment Finance, $3.3 million, broadcast.** A capital lease secured by broadcast trailers, trucks and production support equipment, funding a live event production company’s acquisition of additional production assets. - SouthStar Capital, $0.5 million, chemicals. An invoice factoring facility for a chemical supplier. **Why it matters:** The nFusion transaction is the clearest expression of the week’s theme. A viable business with real assets was exited by its bank and refinanced within days by a specialty lender willing to look at accounts receivable, inventory and equipment together rather than separately. Bank retrenchment at the smaller end is not a hypothetical, and it is creating consistent origination for asset-based lenders. The Salem Five and Utica deals show the two classic ABF collateral patterns still working exactly as designed. One flexes with a seasonal working capital cycle. The other attaches to identifiable hard assets with a resale market and supports an acquisition. **Key risks:** - Multi-collateral complexity. Blended AR, inventory and equipment structures require three different eligibility frameworks and three appraisal cadences in one borrowing base. - Seasonality and import exposure. Inventory-led revolvers for consumer products carry timing risk on overseas production and tariff sensitivity. - Residual value. Broadcast and production equipment depreciates on a technology curve, not a straight line. - Dilution and concentration in factoring. Small facilities can carry disproportionate obligor concentration. - Speed versus diligence. Deals done on accelerated timelines rely on strong monitoring after close. **Opportunities:** Small-ticket specialty ABL is where relationship and speed still command a premium, and where returns are least compressed by bank competition. The economics only work if the cost of monitoring each facility is low. That is an infrastructure question rather than a credit question, and it is the reason platform scale matters more in this segment than anywhere else in the market. **Takeaways** - Bank capacity is concentrating, not contracting. Eleven arrangers competed for one $1.05 billion energy credit in the same week a printer lost its bank facility. Both facts are true simultaneously, and both create opportunity for private credit at different ends of the market. - Collateral is being redefined. A senior secured facility against subscription revenue and app engagement sat alongside one secured by broadcast trailers. Managers need frameworks that can price both, and monitoring systems that can track both. - Undrawn capacity is a strategy in its own right. Endeavour Silver built $100 million of optionality it does not plan to use. Facilities like this need utilisation, covenant and pricing-grid monitoring even when the balance is zero. - Lender finance turns reporting quality into a credit term. Garrington sized availability off the borrower’s own collateral data. In this category, the lender that can ingest and validate a loan tape fastest wins the deal. - Operational discipline does not scale down. A $1.05 billion syndicate and a $500,000 factoring line require the same functions: collect the data, normalise it, test eligibility, compute availability, monitor triggers, report to investors. Only the number of zeros changes. **How Cardo AI helps Asset managers and Lenders** Every transaction in this roundup, from an eleven-arranger energy syndicate to a half-million-dollar factoring line, generates the same operational requirement. Loan and collateral data has to be collected from wherever it lives, standardised, tested against eligibility rules, and reported on continuously. Cardo AI is the infrastructure layer that makes that possible across asset classes: - Loan tape ingestion from any source or format, with validation and exception handling built in, so onboarding a new originator takes days rather than quarters. - Borrowing base, eligibility and waterfall computation, modelled to the facility documents and recalculated as data arrives. - Concentration, covenant and trigger monitoring, with alerting before a breach rather than after it. - Portfolio analytics and stress testing across sectors, vintages, geographies and collateral types. - Institutional-grade investor and regulatory reporting, generated from a single source of truth. - Multi-asset-class coverage, from utility-scale energy portfolios and equipment leases to lender finance facilities, factoring lines and consumer receivables. The managers growing fastest in asset-based finance are not the ones taking the most risk. They are the ones whose operating platform lets them underwrite a new collateral type without adding headcount, and monitor a hundred facilities as rigorously as they monitor ten. **Sources** *[ABL Advisor deal tables](https://www.abladvisor.com/deal-tables) (August 3-7, 2026); company press releases via [Business Wire](https://www.businesswire.com/news/home/20260625720357/en/), [GlobeNewswire](https://www.globenewswire.com/news-release/2026/06/29/3318886/0/en/), [PR Newswire](https://www.prnewswire.com/news-releases/news-releases-list/) and [Bloomberg;](https://www.bloomberg.com/) [ABF Journal](https://www.abfjournal.com/category/news/); and issuer investor relations pages.* If you’re scaling an asset-based or private credit book, [we should talk. ](https://cardoai.com/company/contact-us/) --- ### [Weekly Deal Roundup | August 17 – 21, 2026](https://cardoai.com/article/weekly-deal-roundup-august-17-21-2026/) **Published:** August 24, 2026 **Author:** RobertaCardo26 **Content:** Contents [1. Introduction](#introduction) [2. Flexential: $800 million credit facility](#first-par) [3. EXL: $1.0 billion senior secured credit facility](#second-par) [4. Hadrian: $360 million revolving credit facility](#third-par) [5. Lifetime Brands: $260 million blended facility](#fourth-par) [6. Universal Technical Institute: $200 million ](#fifth-par) [7. The rest of the week](#nine-par) [8. Take-aways](#ten-par) [9. How Cardo AI helps Asset Managers and Lenders](#eleven-par) Fourteen transactions were announced across asset-based finance and private credit in the third week of August, totalling roughly $2.92 billion. Two of them accounted for more than sixty per cent of that figure. That distribution is the story. An eleven-institution syndicate backed a data center platform with an $800 million credit facility, and a four-bank club wrote a $1.0 billion senior secured line for a technology borrower, both inside the same five days that an apparel factor advanced $1.0 million against a single undisclosed borrower’s invoices. In between, an eight-lender group financed an advanced manufacturing platform, a vocational education company drew a six-party revolver tied to federal aid eligibility it doesn’t control, and a consumer products company layered a second-lien term loan underneath a $200 million ABL facility in the same deal. For asset managers and lenders, the interesting question is not which deal was biggest. It is how many counterparties now sit inside a single credit, what happens to monitoring discipline when a syndicate grows past ten names, and how much of the risk in a “Technology” or “Specialty Industries” tag is actually hiding underneath the label. Here is what closed, why it matters, and what it means for portfolio construction and monitoring. **Flexential: $800 million credit facility** RBC Capital Markets and TD Securities led an eleven-institution group, including J.P. Morgan Bank, Goldman Sachs, ING, SMBC, Bank of America, KeyBanc, Flagstar Bank, Citibank and Investec, in an $800 million credit facility for Flexential, a data center and digital infrastructure operator. **Why it matters**: Eleven institutions across lead arranger, bookrunner, administrative agent and co-documentation agent roles is a syndicate size that changes the operating problem, not just the size of the check. Every amendment, waiver and compliance certificate now has to clear eleven separate credit and legal functions before it’s final. The deal itself is a strong data point on continued institutional conviction in digital infrastructure, the number of parties willing to underwrite it is arguably the more interesting signal than the facility size. **Key risks**: Syndicate reconciliation. Allocations, amendments and reporting have to reconcile across eleven counterparties without drift. Capex intensity. Data center buildouts are capital-hungry and sensitive to construction timelines and equipment delivery. Demand concentration. Utilization and lease-up assumptions for digital infrastructure depend on a customer base that can be concentrated in a small number of large tenants. Sector mislabeling. “Technology” as a tag does not distinguish data center infrastructure from software or services credit with a very different risk profile. Refinancing dependency. Facilities of this size typically assume take-out or refinancing options remain available on schedule. **Opportunities**: Digital infrastructure is one of the more actively financed categories in the market right now, and a syndicate this large signals genuine institutional depth behind it. For managers building infrastructure-adjacent exposure, that depth means secondary and participation opportunities are more likely to follow. **EXL: $1.0 billion senior secured credit facility** PNC Bank served as administrative agent alongside Bank of America, TD Bank and JPMorgan Chase Bank as joint lead arrangers on a $1.0 billion senior secured credit facility for EXL, a technology platform. **Why it matters**: This was the single largest facility of the week, and combined with Flexential, it means $1.8 billion, well over half of the week’s total volume, went to two borrowers both tagged “Technology.” For a portfolio with exposure to both facilities, whether directly or through fund-of-fund structures, that concentration is easy to miss unless sector classification is granular enough to separate the underlying business models. **Key risks**: Sector concentration. Two large facilities under one sector tag can understate true single-name and single-theme exposure across a portfolio. Syndicate coordination. A four-bank club still requires disciplined reconciliation, even at a smaller lender count than Flexential’s. Technology cyclicality. Enterprise technology revenue is sensitive to corporate IT budgets and can compress quickly in a downturn. Refinancing and rate exposure. A billion-dollar facility carries meaningful rollover and rate-reset risk over its term. **Opportunities**: Facilities of this size from a four-bank club, rather than a much larger syndicate, suggest the lender group had high conviction in the credit rather than needing to spread the exposure widely. That’s a useful signal for managers assessing where genuine underwriting confidence sits in the market. **Hadrian: $360 million revolving credit facility** Morgan Stanley Senior Funding led an eight-lender group, Western Alliance Bank, J.P. Morgan Bank, First Citizens Bank, Customers Bank, HSBC, Axos Bank and Texas Capital Securities among them, in a $360 million revolving credit facility for Hadrian, an advanced manufacturing platform. **Why it matters**: Eight arrangers backing a single manufacturing borrower is a meaningful vote of confidence in the U.S. advanced-manufacturing and reshoring theme. It’s also a smaller-scale version of the same operational question raised by Flexential: covenant and reporting consistency across eight separate institutions doesn’t happen automatically, and any lag in reconciliation shows up as noise in portfolio-level risk data. **Key risks**: Syndicate complexity. Eight lenders means eight sets of expectations for reporting cadence and covenant interpretation. Execution risk. Advanced manufacturing platforms often carry ramp-up and production-scaling risk that doesn’t show up in a revolver’s headline terms. Customer concentration. Manufacturing platforms serving a small number of large end customers carry outsized single-customer risk. Capital intensity. Manufacturing scale-up typically requires capital beyond what a revolving facility alone can support. **Opportunities**: A facility this size, with this many arrangers, at this stage of the reshoring narrative, indicates banks are willing to commit real capital to the theme rather than just talk about it. That’s useful evidence for managers deciding how much conviction to put behind advanced manufacturing as a lending category. **Lifetime Brands**: **$260 million blended facility** Pathlight Capital LLC and JPMorgan Chase Bank provided a blended facility for Lifetime Brands, a consumer products company, combining a $60 million second lien term loan with a $200 million asset-based lending facility. **Why it matters**: This is a single deal that behaves like two different credits. The ABL tranche is collateral-driven, tied to inventory and receivables eligibility, and sits first in the capital structure. The second lien tranche sits behind it, with materially different recovery expectations in a downside scenario. A portfolio system that records this as one blended exposure, rather than two tranches with different collateral bases and waterfall positions, will misstate the risk on both sides. **Key risks**: Waterfall and intercreditor mechanics. Recovery outcomes diverge sharply between the ABL and second lien tranches in a stress scenario. Collateral valuation drift. The ABL tranche’s borrowing base depends on inventory and receivables eligibility that can move with consumer demand. Consumer discretionary exposure. Lifetime Brands sits in a category directly exposed to shifts in consumer spending. Subordination risk. The second lien position is structurally exposed to erosion in the senior tranche’s collateral coverage. **Opportunities**: Blended structures like this let a lender match financing precisely to a borrower’s working capital cycle and its need for subordinated growth capital, in a single relationship. For managers comfortable underwriting tranche-level risk rather than deal-level risk, structures like this offer differentiated return profiles within one facility. **Universal Technical Institute: $200 million revolving line of credit** Fifth Third Bank served as administrative agent, joint lead arranger and sole bookrunner alongside JPMorgan Chase Bank, Truist Bank, Truist Securities, Citibank and PNC Bank in a $200 million revolving line of credit for Universal Technical Institute, a vocational and technical education platform. **Why it matters**: Education-sector lending carries a risk driver that sits largely outside the borrower’s operating control: revenue for institutions like this is often linked to federal financial aid eligibility and accreditation status. A six-party facility of this size suggests the lender group is comfortable with that regulatory dependency, but it’s a variable that needs its own monitoring track, separate from the borrower’s day-to-day financial performance. **Key risks**: Regulatory dependency. Federal aid eligibility and accreditation status can shift independent of enrollment trends or operating performance. Enrollment cyclicality. Vocational education enrollment moves with labor market conditions and can be counter-cyclical in ways that complicate forecasting. Syndicate coordination. Six parties across lender, agent and arranger roles require consistent reporting and covenant tracking. **Opportunities**: Specialty industries with regulatory-linked revenue are underwritten differently than ordinary corporate credits, and lenders who build monitoring specifically for that regulatory variable, rather than treating it as a footnote, are better positioned to price and manage the category over time. **The rest of the week** $302.7 million, eight lenders, seven collateral types. The remaining transactions closed alongside the week’s headline deals, and together they describe the working end of the market: PNC Bank’s $125.0 million revolver for TWFG, an insurance distribution platform; Macquarie Group’s $75.0 million corporate credit facility for Nightpeak Energy; Bank of Texas’s $87.9 million credit facility for CMC Railroad; The Hedaya Capital Group’s $1.0 million factoring facility for an undisclosed apparel company; SouthStar Capital’s $1.5 million A/R facility for an undisclosed specialty contractor and separate $1.0 million PO and A/R facility for an undisclosed logistics company; nFusion Capital’s $6.0 million asset-based lending facility for an undisclosed vaporization hardware wholesaler; Republic Business Credit’s $2.8 million factoring facility for an undisclosed textile wholesaler; and Flatbay Capital’s $2.5 million CRE bridge loan for an undisclosed commercial fleet repair shop. **Why it matters**: The TWFG and Nightpeak facilities show that mid-size, single-lender corporate credit is still getting done alongside the week’s mega-syndicates, in sectors, insurance distribution, energy, with more stable, recurring cash flow profiles than the headline deals. The smaller factoring and ABL facilities show the opposite end of the same market still functioning: specialty lenders financing niche, often undisclosed borrowers against receivables, purchase orders and hard collateral, on facility sizes that will never appear in a syndicated loan index but represent real, recurring origination. **Key risks**: Regulatory exposure in niche categories. The vaporization hardware wholesaler financed by nFusion operates in a category subject to shifting state and federal nicotine and vaping regulation. Rail and energy cyclicality. CMC Railroad and Nightpeak Energy are both exposed to freight volumes and commodity dynamics outside the borrower’s control. Obligor concentration in factoring. Small facilities can carry disproportionate exposure to a single underlying obligor or customer. Disclosure gaps. Several of these borrowers are undisclosed, meaning monitoring depends more heavily on lender-supplied data than on independent verification. **Opportunities**: Small-ticket specialty ABL and factoring is where relationship and speed still command a premium, and where returns are least compressed by competition from large banks. Insurance distribution and single-lender corporate credit, meanwhile, offer more stable, recurring cash flow exposure for managers who want ballast alongside larger, more cyclical syndicated positions. **Takeaways** Syndicate size is becoming a risk factor in its own right. Eleven institutions on Flexential and eight on Hadrian mean reconciliation discipline, not just credit quality, now drives how cleanly a facility performs operationally. Sector tags are hiding real concentration. $1.8 billion went to two borrowers both labeled “Technology” this week alone, a reminder that headline sector classification is too coarse for real portfolio risk management. Blended structures need tranche-level monitoring. Lifetime Brands’ second lien and ABL tranches carry different collateral bases and different recovery expectations; recording them as one exposure understates the risk. Regulatory-linked revenue needs its own monitoring track. Universal Technical Institute’s dependency on federal aid eligibility sits outside ordinary financial-performance monitoring and has to be tracked separately. Operational discipline does not scale down. An eleven-lender, $800 million syndicate and a $1.0 million factoring line require the same functions: collect the data, normalise it, test eligibility, compute availability, monitor triggers, report to investors. Only the number of zeros changes. **How Cardo AI helps Asset Managers and Lenders** Every transaction in this roundup, from an eleven-institution data center syndicate to a $1.0 million factoring line, generates the same operational requirement. Loan and collateral data has to be collected from wherever it lives, standardised, tested against eligibility rules, and reported on continuously. Cardo AI is the infrastructure layer that makes that possible across asset classes: Loan tape ingestion from any source or format, with validation and exception handling built in, so onboarding a new originator takes days rather than quarters. Borrowing base, eligibility and waterfall computation, modelled to the facility documents and recalculated as data arrives, including tranche-level tracking for blended structures like second lien plus ABL facilities. Concentration, covenant and trigger monitoring, with alerting before a breach rather than after it. Syndicate and multi-agent reconciliation, keeping allocations, amendments and compliance reporting consistent across lender groups of any size. Portfolio analytics and stress testing across sectors, vintages, geographies and collateral types, with sub-sector granularity that goes beyond headline classification. Institutional-grade investor and regulatory reporting, generated from a single source of truth. The managers growing fastest in asset-based finance are not the ones taking the most risk. They are the ones whose operating platform lets them underwrite a new collateral type without adding headcount, and monitor a hundred facilities, an eleven-lender syndicate or a single-lender factoring line, as rigorously as they monitor ten. **Sources** *[ABL Advisor deal tables](https://www.abladvisor.com/deal-tables) (August 17-21, 2026); company press releases via [Business Wire](https://www.businesswire.com/news/home/20260625720357/en/), [GlobeNewswire](https://www.globenewswire.com/news-release/2026/06/29/3318886/0/en/), [PR Newswire](https://www.prnewswire.com/news-releases/news-releases-list/) and [Bloomberg;](https://www.bloomberg.com/) [ABF Journal](https://www.abfjournal.com/category/news/); and issuer investor relations pages.* If you’re scaling an asset-based or private credit book, [we should talk. ](https://cardoai.com/company/contact-us/) --- ### [Weekly Deal Roundup | August 24–28, 2026](https://cardoai.com/article/weekly-deal-roundup-august-24-28-2026/) **Published:** August 31, 2026 **Author:** GiadaCardo26 **Content:** Contents [1. Introduction](#introduction) [2. Lambda – $926mm](#first-par) [3. The Ensign Group – $800mm](#second-par) [4. Core Scientific – $600mm](#third-par) [5. Bond Street REIT – $300mm](#fourth-par) [6. Perma-Pipe International Holdings – $139mm](#fifth-par) [7. Lender finance – $50mm](#sixth-par) [8. The specialty tail](#seventh-par) [10. Takeaways](#nine-par) [11. How Cardo AI helps asset managers and lenders](#ten-par) **Fourteen transactions. Ten sectors. Just over $2.85 billion announced.** The headline number is not the interesting part. The interesting part is the distance between the two ends of the week. On one side, a $926MM syndicated term loan B for an AI compute platform, arranged by Morgan Stanley with MUFG, Citizens, Crédit Agricole and Wells Fargo alongside. On the other, three separate $1MM factoring lines to a staffing company, a hospital services provider and an infrastructure startup. Different lenders, different underwriting, different documentation. Identical operational requirement underneath: know what the collateral is, know what it is worth today, and know it before the borrower does. Here is what stood out, and what it means for managers and lenders allocating to this market. **Lambda: $926MM senior secured term loan B** Morgan Stanley Senior Funding acted as left lead arranger and joint bookrunner. MUFG joined as joint bookrunner, with Citizens Bank, Crédit Agricole and Wells Fargo as documentation agents. Why it matters: This is a term loan B, not a bilateral facility or a private credit club deal. That distinction carries weight. A TLB means the transaction is structured to be syndicated and traded, which means an institutional loan desk has satisfied itself that AI compute revenue and the hardware behind it can be priced by a market rather than negotiated by a single relationship lender. Twelve months ago, most GPU and data centre financing sat in venture debt, vendor finance or bespoke private credit structures. A near-billion-dollar TLB moves the category several steps closer to mainstream leveraged finance. For asset managers, that is a signal about where the next generation of asset-based opportunity is forming. Key risks: Residual value is the central question. GPUs depreciate on a technology curve, not a straight line, and a refresh cycle can reprice a collateral pool faster than an annual appraisal can capture it. Beyond that: customer concentration in contracted compute revenue, dependency on power availability and site leases that sit outside the borrower’s balance sheet, and refinancing risk at maturity in a market that has not yet been tested through a full cycle. Opportunities: Lenders who build genuine competence in compute collateral will price it better than generalists for the next several years. That competence is largely a data problem: tracking hardware vintage, utilisation, contract tenor and counterparty quality at the asset level rather than the borrower level. There is also an adjacency worth watching. Where a TLB market forms, a warehouse and forward-flow market usually follows. **The Ensign Group: $800MM revolving credit facility upsize** Truist Bank acted as administrative agent, joined by Citibank, The Huntington National Bank, U.S. Bank, Wells Fargo, Bank of America, BMO Bank, PNC Bank and Synovus Bank. Why it matters: Nine banks on a single healthcare revolver is a liquidity statement. Skilled nursing and senior care have spent several years being described as difficult to underwrite, and yet a scaled operator with owned real estate and predictable government and commercial receivables still assembled a full club at $800MM on amendment terms. The lesson is not that healthcare risk has disappeared. It is that scale, transparency and operating history still clear the bank market, even in sectors that specialist lenders have been quietly dominating. Key risks: Reimbursement policy remains the dominant variable, and it is set outside the borrower’s control. Occupancy and labour cost pressure continue to compress operating margin. Facility-level regulatory action can impair individual assets in ways that portfolio-level covenants do not always capture. And an upsized revolver by definition consumes covenant headroom. Opportunities: For private credit, the read-across is in the middle market. Where the bank club serves operators of this size, sub-scale operators still need bridge, acquisition and working capital capital, and the specialist lenders active there have been compounding steadily. For managers, healthcare ABF remains one of the more durable specialty categories in the US, precisely because it is unglamorous and data-intensive. **Core Scientific: $600MM revolver and letter of credit facility** Morgan Stanley Senior Funding acted as left lead arranger and joint bookrunner. JPMorgan Chase Bank took administrative agent and collateral agent roles alongside joint lead arranger duties, with Goldman Sachs and TD Securities as joint lead arrangers and bookrunners. The structure splits into a $100MM revolver and a $500MM letter of credit facility. Why it matters: The letter of credit tranche is five times the size of the revolver. That ratio is the story. Data centre operators need to post credit support against power purchase agreements, grid interconnection commitments, equipment orders and site obligations long before they draw operating cash. Financing the pivot from crypto mining to AI hosting is therefore as much about providing collateral for contractual obligations as it is about funding capital expenditure. This structure is a useful template for anyone underwriting energy-intensive infrastructure transitions. Key risks: Contingent exposure behaves differently from drawn debt. It can convert quickly and correlate badly, typically at the point when the borrower is already under pressure. Add power price and contract exposure, capex timing against milestone commitments, site and grid concentration, and residual revenue volatility from the legacy business during the transition. Opportunities: Letter of credit provision is a fee-generating, capital-efficient product that many non-bank lenders have not yet built for. As infrastructure transitions multiply, so does demand for credit support facilities. The monitoring requirement is the barrier to entry, and also the moat. Contingent exposure has to be tracked continuously, by counterparty and by trigger, or it is not really being managed. **Bond Street REIT: $300MM revolving credit facility upsize** JPMorgan Chase Bank led as lead lender, with BMO Capital Markets and KeyBanc Capital Markets as joint lead arrangers, Banco Santander participating and SouthState Bank as lender. Why it matters: Real estate revolvers are being upsized again, but selectively. Bank appetite has returned for borrowers where the unencumbered asset pool is transparent, the sponsor is known and the borrowing base can be evidenced rather than asserted. That selectivity is the point. This is not a broad reopening. It is a reopening for borrowers with clean data. Key risks: Cap rate movement combined with appraisal lag can leave a borrowing base overstated for months. Tenant credit and lease rollover concentrate risk in ways that portfolio averages disguise. Floating rate exposure on drawn balances remains live. Opportunities: For managers, the gap between borrowers who can evidence a borrowing base in near real time and those who cannot is becoming a pricing gap. Lenders reward reporting quality, and increasingly they price it. **Perma-Pipe International Holdings: $139MM** J.P. Morgan Bank provided a structure comprising a $75MM revolver, a $14MM term loan and $50MM of incremental capacity. Worth noting for the shape rather than the size. Half the facility is uncommitted incremental capacity, which gives an industrial borrower room to fund growth or acquisitions without returning to the market. Accordion and incremental structures shift the diligence burden forward, because the lender is underwriting a future borrower profile as much as the current one. Risk: Industrial cyclicality, project concentration and working capital swings tied to contract timing. Opportunity: Incremental capacity is a relationship-retention tool, and lenders who can model the borrower’s forward profile accurately will win the follow-on. **Lender finance: $50MM across three facilities** Pier Asset Management provided $15MM to an undisclosed small business lender and $10MM to an undisclosed patient finance platform. Pinnacle Financial Partners extended a $25MM line of credit to Loeb Term Solutions. Why it matters: None of these deals is large. Together they are among the most consequential of the week, because every dollar lent to a specialty lender funds a multiple of downstream origination. Lender finance is the quiet transmission mechanism of private credit. When it is open, specialty origination expands across dozens of sectors simultaneously. Three facilities in a single week, none above $25MM, is a healthier signal about market breadth than any single large transaction. Key risks: The lender is underwriting a portfolio it does not originate. Advance rate discipline, eligibility criteria and the quality of the borrower’s own servicing and reporting are what stand between the facility and the underlying credit. Correlation is the hidden exposure, because a downturn hits the specialty lender’s book and its own funding at the same moment. Opportunities: Lender finance offers diversified exposure to specialty credit without building origination capability. The constraint is operational. It requires the ability to ingest and interrogate the borrower’s loan tape continuously rather than accepting a monthly summary at face value. **The specialty tail: seven lenders, seven sectors** Mountain Ridge Capital provided $15MM to a brick and mortar retailer. eCapital extended an $11MM healthcare receivables facility to a specialty pharmacy provider. Prestige Capital Corp. put a $3MM invoice factoring facility behind a bed frame manufacturer. Republic Business Credit, Versant Funding and TowerCap each provided $1MM lines to an infrastructure startup, a hospital service provider and a staffing company respectively. Why it matters: This is the part of the market that does not make headlines and does not stop. Seven lenders, seven sectors, one week. Retail, pharmacy, manufacturing, infrastructure, healthcare services and staffing, all financed against receivables and inventory rather than earnings multiples. Aggregate this activity across a year and it is a meaningful asset class. Aggregate it across a manager’s portfolio and it is a data management problem. Key risks: Dilution and credit quality in the receivables pool. Customer concentration at the obligor level. Verification risk in factoring, where fraud is rare but expensive. Seasonality and obsolescence in retail inventory. In healthcare receivables specifically, payer mix and reimbursement timing drive collections more than borrower performance does. Opportunities: Spreads in the specialty tail have held up better than in the syndicated market, and the collateral is short-dated and self-liquidating. For managers building diversified ABF exposure, this is where duration is short, granularity is high and pricing power still sits with the lender that can monitor properly. **Takeaways** - Bank syndicates are back at the top of the market, and they are clubbing up. Nine lenders on Ensign, five on Lambda, four on Core Scientific. Banks are not competing on price everywhere. They are concentrating balance sheet on borrowers that fit, and syndicating the rest. - Compute is being collateralised. A $926MM TLB for AI infrastructure changes what counts as financeable. Residual value, contract quality and power access are becoming standard diligence lines, and most lenders do not yet have the data models to assess them. - Letters of credit are doing the work of debt. Core Scientific’s $500MM L/C tranche dwarfs its revolver. Contingent exposure is growing faster than drawn balances across infrastructure credit, and it demands the same monitoring discipline. - Lender finance is compounding under the radar. Three facilities, none above $25MM, all of it origination capacity for the specialty market. - Data quality is now a pricing input. Across every deal this week, from the $926MM syndication to the $1MM factoring line, the borrowers who cleared fastest were those able to evidence collateral, eligibility and performance rather than assert it. **How Cardo AI helps Asset Managers and Lenders** The distance between a $926MM term loan B and a $1MM factoring line is enormous in credit terms. Operationally, it is almost nothing. Both require someone to collect loan and collateral data, normalise it, test it against eligibility rules, compute a borrowing base, monitor covenants and triggers, and report to investors on time and in a defensible format. Most teams do this across spreadsheets, servicer portals and PDFs. That works until portfolio complexity outruns headcount, which is usually the moment the opportunity set gets interesting. Cardo AI provides the infrastructure layer for private credit, asset-based finance and structured finance: - Loan tape ingestion across any source, format or servicer, with validation at the point of entry - Borrowing base, eligibility and waterfall computation, automated and auditable - Concentration, covenant and trigger monitoring, continuous rather than monthly - Portfolio analytics and stress testing at loan level, not pool averages - Institutional-grade investor and regulatory reporting - Multi-asset-class onboarding, from data centre platforms and equipment lessors to healthcare receivables and factoring books The result is that origination capacity stops being limited by operational capacity. If you are building ABF exposure, financing specialty lenders, or trying to monitor a portfolio that has outgrown its reporting stack, we should talk. **Sources** *[ABL Advisor deal tables](https://www.abladvisor.com/deal-tables) (August 24–28, 2026); company press releases via [Business Wire](https://www.businesswire.com/news/home/20260625720357/en/), [GlobeNewswire](https://www.globenewswire.com/news-release/2026/06/29/3318886/0/en/), [PR Newswire](https://www.prnewswire.com/news-releases/news-releases-list/) and [Bloomberg;](https://www.bloomberg.com/) [ABF Journal](https://www.abfjournal.com/category/news/); and issuer investor relations pages.* If you’re scaling an asset-based or private credit book, [we should talk. ](https://cardoai.com/company/contact-us/) --- ### [Weekly Deal Roundup | September 1–4, 2026](https://cardoai.com/article/weekly-deal-roundup-september-1-4-2026/) **Published:** September 7, 2026 **Author:** GiadaCardo26 **Content:** Contents [1. Introduction](#introduction) [2. Digi International – $350MM](#first-par) [3. JetZero – $100MM](#second-par) [4. Wingspire Capital – $45MM](#third-par) [5. Legacy Corporate Lending – $40MM](#fourth-par) [6. Pinnacle Financial Partners – $25MM](#fifth-par) [7. The specialty tail](#seventh-par) [8. Takeaways](#nine-par) [9. How Cardo AI helps asset managers and lenders](#ten-par) **$586.9MM across twelve transactions and ten sectors. The largest deal was 233 times the smallest. Every one of them turned on how it was built.** Last week produced a smaller headline number than the fortnight before it. That makes it more instructive, not less. Strip out one $350MM syndicated revolver and the remaining eleven deals total $237MM. Ten of the twelve came in under $50MM. Half came in under $5MM and together accounted for less than 3% of the week’s volume. This is what the private credit and asset-based finance market actually looks like most weeks. A handful of large, well-documented bank transactions sitting on top of a long tail of specialty facilities that never make the front page but do most of the work. What connects them is not size. It is that each was won on structure: how the collateral was defined, how the borrowing base was drawn, how the risk was tranched, and how performance will be measured after close. **Digi International: $350MM revolver, refinanced tighter** BMO Bank acted as administrative and collateral agent on an expanded senior secured revolving credit facility for Digi International, the Nasdaq-listed IoT products and services business. BMO Capital Markets and Bank of America served as joint bookrunners, with MUFG joining them as joint lead arrangers. The facility replaces an existing $250MM revolver and carries an accordion for the greater of $130MM or one hundred percent of trailing twelve-month adjusted EBITDA, taking total potential capacity to $480MM, plus an uncapped incremental amount subject to a pro forma total net leverage test of 2.50x. It matures in August 2031. The detail that matters is the pricing grid. SOFR margins now run from 125 to 262.5 basis points depending on net leverage, against a prior range of 135 to 310 basis points. Why it matters: That is a tightening of 10 basis points at the top of the grid and 47.5 at the bottom. Banks are not withdrawing from asset-light technology credits. They are repricing the ones they want and passing on the ones they do not. The bifurcation narrative in bank lending is real, but it cuts both ways, and this is the competitive side of it. Key risks: Digi has completed two acquisitions in the past eighteen months, so integration execution now sits on a five-year tenor. Covenant headroom against the 2.50x incremental leverage test governs how much of that $480MM ceiling is genuinely reachable. IoT hardware carries live tariff and supply chain exposure. And the entire structure is floating rate on drawn balances. Opportunities: For lenders, a repriced revolver with a large uncommitted accordion is an efficient way to hold a relationship while retaining the option to scale. For asset managers holding bank paper or fund-finance exposure to these syndicates, the tightening grid is a leading indicator worth tracking systematically rather than anecdotally. Pricing grids are where bank risk appetite is disclosed before it appears in volume data. **JetZero: $100MM underwritten against milestones, not earnings** Pinegrove Credit Partners, backed by Brookfield and HRTG Partners, led a senior secured term loan facility of up to $100MM for JetZero alongside Silicon Valley Bank, a division of First Citizens Bank. JetZero is building a blended-wing commercial aircraft. Proceeds fund technology maturation, manufacture of the Jet1 full-scale demonstrator, and build-out of manufacturing and test facilities in Greensboro, North Carolina. The demonstrator was roughly 40% complete as of June and is scheduled to fly in late 2027. Pinegrove was explicit that the facility was structured around the programme’s real milestones rather than as a conventional corporate loan, and indicated appetite to expand capacity as the programme advances. Why it matters: This is project finance logic applied to a corporate borrower. There is no EBITDA to lend against and no operating history to underwrite. The credit is the programme schedule, the tooling and facilities collateral, and the ability to size draws against verifiable progress. Private credit has been moving in this direction for two years. A senior secured term loan to a pre-revenue aerospace manufacturer is a clear marker of how far it has gone. Key risks: Milestone slippage is the dominant one, and aerospace programmes slip. Engine selection is still ahead. Certification risk sits beyond the demonstrator flight. Collateral value in part-built aircraft and bespoke tooling is difficult to mark and harder to realise. The exposure is concentrated in a single programme with a single point of failure. Opportunities: Milestone-based structures give lenders something a covenant package cannot: the ability to stop funding at a defined checkpoint rather than after a breach. Capital deploys in tranches tied to observable events. For managers who can monitor programme progress at the same cadence they monitor financials, this is a category with limited competition and structural downside protection. The constraint is operational, not analytical. **Wingspire Capital: $45MM inside a $135MM unitranche** Wingspire Capital provided $45MM in first-out secured financing to a metal surface finishing business serving mission-critical components. The commitment splits into a $10MM first-out revolver and a $35MM term loan, carved from a broader $135MM senior secured unitranche facility. Proceeds refinanced existing debt and added liquidity. Wingspire is a portfolio company of Blue Owl Capital Corporation. Why it matters: The unitranche was originally sold as a simplification: one lender, one document, one blended rate. It is now being tranched internally. First-out and last-out lenders sit in the same credit agreement with materially different risk positions, different economics and different information requirements. That reintroduces the complexity the structure was meant to remove, but with a critical difference. The tranching is contractual and private rather than public and rated. There is no external agent producing a standardised report on who holds what. Key risks: Intercreditor mechanics govern everything in an enforcement scenario, and first-out priority is only as good as the agreement drafting. Metal finishing is cyclical and typically carries customer concentration into aerospace, defence or automotive supply chains. The first-out revolver rides a working capital borrowing base that moves with the cycle. The last-out tranche carries its own refinancing risk at maturity. Opportunities: For a lender, a first-out position gives senior secured risk with better-than-senior economics. For an allocator, the ability to underwrite and monitor a position inside a private tranche structure is becoming a genuine differentiator. Most of the market can price the credit. Fewer can track their actual position through the waterfall on an ongoing basis. **Legacy Corporate Lending: $40MM against a seasonal borrowing base** Legacy Corporate Lending provided a $40MM revolving asset-based credit facility to National Christmas Products. Legacy is a portfolio company of Bain Capital Specialty Finance and Bain Capital Private Credit, and $40MM sits at the top of its stated commitment range. Why it matters: Consider the shape of this borrowing base. Inventory builds through spring and summer. Receivables spike in the autumn as retailers take delivery. Both collapse in January. A facility sized on an annual average would be useless. A facility sized on the peak would be badly over-advanced for seven months of the year. Seasonality of this severity is not a credit weakness. It is an underwriting and monitoring specification. The advance rates, eligibility criteria, ineligible categories and reporting frequency have to be designed for a base that moves by multiples within a single fiscal year. Key risks: Unsold seasonal inventory is the obvious one, because obsolescence is not gradual. Revenue concentration in a single selling season leaves no second chance if a year goes badly. Retailer credit quality sits inside the receivables pool, so the facility carries downstream counterparty risk. And advance rates at peak build are where the structure is most exposed. Opportunities: Seasonal ABL is persistently underserved because it demands operational intensity that many lenders will not commit to. The lenders that can compute eligibility weekly rather than monthly, and can model the base forward rather than report it backward, capture a category with limited competition and reliable pricing power. **Pinnacle Financial Partners: $25MM of lender finance, and the multiplier behind it** Pinnacle Financial Partners, through its Pinnacle Bank subsidiary, provided a $25MM senior secured revolving line of credit to Loeb Term Solutions. Loeb funds equipment term loans for manufacturers and businesses that do not clear traditional bank credit standards, lending largely on the appraised value of machinery and equipment rather than on borrower credit, and doing so without financial covenants. Pinnacle is a $129.1 billion asset regional bank that combined with Synovus earlier this year. Why it matters: This is the most capital-efficient transaction of the week and the one with the longest reach. Pinnacle is not underwriting machinery. It is underwriting Loeb’s underwriting. Every dollar advanced becomes several dollars of downstream equipment loans to borrowers Pinnacle would never approve directly, at yields it could not otherwise access, with Loeb absorbing the first loss. Lender finance is where the boundary between bank and non-bank credit is genuinely being negotiated. Banks are choosing to buy access to a market rather than build the origination and asset valuation capability to serve it. Key risks: The bank’s exposure runs through an intermediary whose underwriting rests on equipment valuation methodology it does not control. Downstream loans are covenant-free, so early warning signals have to come from collateral performance and payment behaviour rather than from financial reporting. And visibility depends entirely on the quality and frequency of the loan tape the intermediary provides. Opportunities: For banks, lender finance delivers diversified specialty exposure with structural subordination beneath them. For specialty lenders, bank leverage is the difference between a balance sheet business and a scalable platform. For both sides, the binding constraint is data. Facilities where the borrower can deliver a clean, granular, frequent loan tape get better advance rates and better pricing than facilities where they cannot. That gap is widening. **The specialty tail: seven deals, seven sectors, under 3% of volume** Seven transactions closed between $1.5MM and $10MM. Porter Capital factored a $10MM facility for a seafood processor. Rosenthal closed three deals in a single day: $4.5MM to a wellness company, $3.5MM to a skincare brand, and $1.5MM to a pawn shop. InterNex provided a $4MM revolving line to an AI-powered logistics provider. Flatbay Capital wrote a $1.9MM CRE bridge loan for a senior living community. Utica Equipment Finance closed a $1.5MM capital lease with a construction management company. Why it matters: Two observations. First, an AI company took a $4MM working capital line the same week that AI infrastructure was raising nine figures elsewhere in the market. The AI credit story is not only about compute. Second, the pawn shop facility is lender finance in miniature. A secured lender borrowing against its own secured loan book, at $1.5MM. Key risks: Small-ticket facilities carry proportionally higher servicing cost, thinner collateral verification, and concentration in businesses with limited financial reporting infrastructure. Opportunities: This is where spread lives, and it is where portfolio diversification is genuinely achieved rather than claimed. The obstacle has never been credit selection. It is that a hundred $3MM facilities cost far more to administer than three $100MM facilities, unless the administration is automated. Managers who solve the operating cost problem can hold the tail. Managers who cannot are structurally pushed up-market into a more crowded segment. **Takeaways** 1. Bank pricing is tightening for the credits banks want. The Digi grid moved up to 47.5 basis points tighter on a refinancing. Read bank selectivity as a two-sided story, not a retreat. 2. Milestone-based underwriting has crossed from project finance into corporate private credit. JetZero is a senior secured loan with no earnings underneath it. Expect more structures where funding is gated on verifiable operational events rather than financial covenants. 3. The unitranche is fragmenting internally. First-out and last-out positions inside one agreement mean two risk profiles, two economics, and no external agent producing a standard report on either. 4. Lender finance keeps compounding. A $25MM bank line to a specialty equipment lender generates multiples of downstream origination. This is the fastest-growing bridge between bank and non-bank credit, and it is entirely dependent on loan tape quality. 5. Seasonality and small tickets are underserved because they are operationally expensive. Both are attractive on a risk-adjusted basis. Both require the ability to compute eligibility and monitor collateral at a frequency most platforms cannot sustain. 6. The operational requirement does not scale with deal size. A $1.5MM capital lease requires the same collect, normalise, monitor, report cycle as a $350MM syndicated revolver. That is precisely why the tail is underserved, and precisely where the opportunity sits. **How Cardo AI helps Asset Managers and Lenders** Every deal above generates the same operational obligation from the day it closes: collect the data, normalise it, monitor it against the structure, and report it to investors and lenders. That obligation does not get smaller when the facility does. Cardo AI is the operating system for that work. - Loan tape ingestion across any source or format. Servicer files, originator extracts, spreadsheets, APIs. Normalised into a single data model regardless of what arrives or how often. - Borrowing base, eligibility and waterfall computation. Advance rates, ineligible categories, concentration caps and cash waterfalls calculated automatically, at whatever frequency the facility demands. Seasonal bases and first-out tranches included. - Concentration, covenant and trigger monitoring. Continuous testing against documented terms, with alerts before a breach rather than after a reporting cycle. - Portfolio analytics and stress testing. Scenario analysis at loan and portfolio level, so that milestone slippage, cap rate movement or a seasonal shortfall can be sized before it happens. - Institutional-grade investor reporting. Consistent, auditable reporting that meets LP and lender expectations without a manual assembly process each quarter. - Multi-asset-class onboarding. Equipment leases, factoring facilities, seasonal inventory ABL, CRE bridge loans, lender finance facilities and unitranche tranches on one platform. **Sources** *[ABL Advisor deal tables](https://www.abladvisor.com/deal-tables) (August 31–September 4, 2026); company press releases via [Business Wire](https://www.businesswire.com/news/home/20260625720357/en/), [GlobeNewswire](https://www.globenewswire.com/news-release/2026/06/29/3318886/0/en/), [PR Newswire](https://www.prnewswire.com/news-releases/news-releases-list/) and [Bloomberg;](https://www.bloomberg.com/) [ABF Journal](https://www.abfjournal.com/category/news/); and issuer investor relations pages.* If you’re scaling an asset-based or private credit book, [we should talk. ](https://cardoai.com/company/contact-us/) --- ### [Cardo AI is DORA compliant](https://cardoai.com/article/cardo-ai-is-dora-compliant/) **Published:** March 25, 2025 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#introduction) [Why DORA matters?](#first-par) [Our commitment to security & DORA compliance](#second-par) [What this means for financial institutions](#third-par) Introduction The financial industry is more digital than ever, and with that comes growing cybersecurity risks and regulatory expectations. Banks, asset managers, and other financial institutions rely on technology providers like us for mission-critical services, from investment management to data processing. But with cyber threats constantly evolving, regulators are stepping in to ensure that both financial firms and their tech partners are prepared for disruptions. That’s where the [EU’s Digital Operational Resilience Act (DORA)](https://www.eiopa.europa.eu/digital-operational-resilience-act-dora_en) comes in. Taking effect in January 2025, DORA introduces a unified framework for managing IT risks across the financial sector — including its third-party technology providers. In other words, financial institutions aren’t just responsible for their own resilience; they must also ensure that their vendors meet strict security, incident reporting, and risk management standards. Why DORA matters? DORA strengthens the operational resilience of financial institutions by requiring a structured approach to risk management and cybersecurity. Here’s what it covers: - **Stronger ICT risk management**: Financial institutions and their tech providers must proactively identify and mitigate cyber risks through structured governance frameworks. - **Strict incident reporting requirements**: Any cybersecurity incidents must be detected, classified, and reported to regulators within tight deadlines. - **Operational resilience testing**: Regular security tests, including Threat-Led Penetration Testing (TLPT), ensure financial firms and their technology partners can withstand real-world cyber threats. - **Third-party oversight**: Critical technology providers (like cloud platforms, software vendors, and security firms) are now directly regulated under DORA, reinforcing accountability. - **Stronger sector-wide collaboration** – DORA encourages financial firms and tech providers to share threat intelligence to strengthen industry-wide security. For financial institutions, this means that the security posture of their technology partners directly impacts their own regulatory compliance. That’s why at Cardo AI, we’ve built security and resilience into the core of our asset based finance software. Our commitment to security & DORA compliance As a technology provider serving financial institutions, we take our role in ensuring resilience seriously. Here’s how we align with DORA’s standards: **Proactive risk management & compliance** We don’t just meet compliance requirements—we anticipate them. Our platform is designed with built-in security controls that align with global best practices, including: - SOC 2 Type II certification: Ensuring data security, availability, and confidentiality. - GDPR compliance: Protecting sensitive financial data with strict privacy controls. - Continuous security audits: Regular third-party assessments validate our adherence to industry standards. We also provide DORA Readiness Statements and detailed security documentation during RFPs, making it easier for our clients to assess our compliance. **Security at every level** At Cardo AI, we embed security at every layer of our infrastructure: - Access controls & identity management: Enforcing least privilege access to protect sensitive investment data. - Encryption by default: All data, both at rest and in transit, is encrypted using top-tier security standards. - Single-tenancy for high-security clients: Offering dedicated infrastructure for organizations with specialized security needs. **Continuous threat monitoring & AI-driven security** Cyber threats evolve fast—so do our defenses. Our security framework includes: - 24/7 security monitoring: Real-time system oversight for instant threat detection. - AI-powered anomaly detection: Identifying suspicious activity based on real-world attack patterns. - Automated threat containment: Isolating security incidents before they escalate. **Real-world testing & resilience** We don’t just assume our security measures work—we put them to the test. - Threat-Led Penetration Testing (TLPT): Simulating sophisticated cyberattacks to assess platform resilience. - Regular security drills: Ensuring our teams and systems are always ready for potential threats. - DDoS mitigation strategies: Protecting against large-scale attacks that could disrupt financial operations. What this means for financial institutions By choosing a **DORA-compliant** technology provider, financial institutions can confidently focus on their core business without worrying about operational disruptions or regulatory gaps. Our high-availability infrastructure ensures business continuity by preventing downtime, allowing investment strategies to stay on track. With advanced encryption, access controls, and continuous monitoring, we safeguard sensitive financial data from potential breaches. Our compliance with DORA’s standards also simplifies regulatory reporting and third-party risk management, making the process more seamless for our clients. Additionally, our AI-driven security analytics and real-time monitoring provide financial institutions with a proactive defense against emerging cyber threats, ensuring they remain one step ahead in an ever-evolving digital landscape. See how Cardo AI’s DORA-aligned infrastructure protects your operations and simplifies third-party risk management. [Book a demo](https://cardoai.com/book-a-demo/) --- ### [Echoes of the past: FinTech lending, usury laws, and the shadow of the mortgage crisis](https://cardoai.com/article/echoes-of-the-past-fintech-lending-usury-laws-and-the-shadow-of-the-mortgage-crisis/) **Published:** January 26, 2026 **Author:** sviluppatore **Content:** Contents [Introduction](#introduction) [Modern Usury and the “True Lender” Risk](#first-par) [The Looming Secondary Market Crisis](#second-par) [Case Study](#third-par) [Cardo AI Solution](#fourth-par) Introduction FinTech lending today enjoys minimal oversight. It mirrors the mortgage market before 2007. State laws already existed to curb abuse. They blocked predatory loan features. They required proof of repayment ability. They forced brokers to act in the borrower’s interest. These rules were ignored. The focus was on volume and access. North Carolina banned negative amortization and balloon payments in 1999. Georgia capped high-cost loans in 2002. California had long-standing broker standards. All were clear, enforceable, and decades old. They gathered dust. Regulators and lenders paid no attention. Modern Usury and the “True Lender” Risk Defaults arrived. Homes were lost. The fallout was massive. Regulators needed scapegoats. They dusted off the old laws. Courts applied them in waves. Each case made the rules stronger. Penalties applied retroactively. Lenders learned the past was not closed. FinTech lending operates under the same playbook. State usury laws set civil limits (10 to 36% APR) and criminal triggers (25 to 45%+), depending on the state. These caps have controlled consumer credit for years. Rent-a-bank structures avoid them by using charter banks in permissive states to export high rates nationwide. The true lender test now functions like assignee liability and fiduciary breach claims did against mortgage investors. The Looming Secondary Market Crisis A surge in defaults will expose weak high-rate loans. State attorneys general and courts will move fast. FinTech companies, investors, and enabling banks all face exposure to judiciary interpretations of “true lenders”. The perfect storm is again, unleashed. Loans will be declared void. Borrowers will demand refunds. Investors in securitized pools backed by usury-tainted collateral will face buyback demands and frozen markets. This sequence repeats the MBS meltdown once state laws were finally enforced. Public sentiment will force regulators and lawmakers into action. After the mortgage crisis, government shifted from advocates of liquidity to champions of accountability focusing on banks and rating agencies. FinTech is the next visible target. One major default cycle can spark joint state actions, CFPB rules, and Congressional hearings. Case Study Recently, we had the opportunity to support one of our clients in a unique challenge: assessing potential usury exposure across a portfolio of consumer FinTech loans. The client’s objective was to analyze and understand how state-specific usury caps could impact their portfolio, taking into account the complex variations and ambiguities present in U.S. usury laws. Many state statutes are either poorly defined or contain numerous exceptions, making it difficult for lenders to anticipate the full scope of downside risk. To address this, we worked with the client to develop a dynamic matrix mapping the potential implications of usury across different states. This approach allowed for a detailed, automated analysis of the portfolio, highlighting areas where exposure could be highest and where regulatory nuances could materially affect outcomes. Through this process, we discovered that while some portions of the portfolio fell comfortably within legal thresholds, others were close to, or in certain scenarios exceeded, state-specific limits. Beyond identifying risk points, the exercise provided valuable insights into the composition of the portfolio showing which loans carried higher sensitivity to regulatory enforcement and enabling the client to make more informed decisions ahead of formalizing their operating model or approving investment decisions. Cardo AI Solution Cardo AI is designed to handle the full life cycle of ABF transactions. Heavy scrutinization is applied to the data to ensure the true and accurate reflection of the investment is given proper due diligence. Collateral characteristics and performance is tracked and monitored and flexible transaction management can allow for three primary mitigating steps that can be taken on potential State and criminal breaches starting with running tests for identification. We see three primary mitigation options set forth below: 1. Follow the intent of state usury laws and fully exclude violated collateral. 2. Risk-adjust advance rates based on state and level of infraction (Clean or none applied/civil/criminal). 3. Explicitly enumerate and track the collateral in funding for granular itemization in the event issues arise. Move beyond the shadow of the mortgage crisis. Leverage Cardo AI to automate your mapping and turn regulatory risk into a competitive edge. [Book a demo](https://cardoai.com/book-a-demo/) --- ### [Cardo AI proudly contributes to the MSCA Industrial Doctoral Network on Digital Finance](https://cardoai.com/article/cardo-ai-proudly-contributes-to-the-msca-industrial-doctoral-network-on-digital-finance/) **Published:** February 19, 2024 **Author:** GiadaCardo26 **Content:** Contents [Introduction](#introduction) [Objectives of the program](#first-par) [Collaborative network and impact](#second-par) [Why is this important ](#third-par) Introduction We are thrilled to announce that, as an industrial partner and beneficiary, Cardo AI will be contributing to **DIGITAL,** a European research and PhD training program within the prestigious **[Marie Skłodowska-Curie Actions](https://marie-sklodowska-curie-actions.ec.europa.eu/)** (MSCA). This program aims to foster research talent in industrial training and improve the competitiveness of European financial market. Objectives of the program MSCA Digital Finance aims to integrate advanced research and training in the field of digital finance. The goal is to bridge the gap between academia and industry, fostering a new generation of digital finance professionals. DIGITAL will train young researchers in R&D topics essential for the rapidly changing landscape of digital finance, encompassing diverse applications such as data quality, Artificial Intelligence (AI), Machine Learning (ML), Explainability of AI (XAI), blockchain applications, and sustainable finance, the focus extends well beyond traditional Finance PhD education. Collaborative network and impact DIGITAL consists of a consortium of top European universities and industry partners on a mission to reshape the landscape of digital finance: - Eight leading European Universities (WU Vienna University of Economics and Business, Humboldt-Universität zu Berlin, University of Twente, Bucharest University of Economic Studies, Babes-Bolyai University, Bern Business School, Kaunas University of Technology and University of Naples), all ranked among the top 200 universities globally in their fields. - Five major international corporations (Deloitte, Swedbank, Intesa Sanpaolo, Bank for International Settlements, and Raiffeisen Bank) with a significant Research & Development presence across Europe. - Two SMEs (Cardo AI and Royalton Partners) being some of the most innovative ones in their field. - Three large and internationally renowned research centers (Athena Research and Innovation Centre, EIT Digital, and Fraunhofer Institute). - Two governmental agencies: the European Central Bank and the Bank for International Settlements. Why is this important for Cardo AI MSCA is renowned for its competitive selection process, emphasizing not only the scientific excellence but also the societal relevance of the project proposals. Being an industry innovation contributor to the MSCA consortium shows how committed our company is to making a positive impact on digital finance and explainable AI for financial markets. This achievement places us in the company of some of amazing research institutions across Europe, each with a remarkable history of pushing the boundaries in science and technology. Stay tuned for updates as we dive into this journey with the MSCA! For more details on the consortium and the proposal, please visit the following website: Cardo AI Solution Cardo AI is designed to handle the full life cycle of ABF transactions. Heavy scrutinization is applied to the data to ensure the true and accurate reflection of the investment is given proper due diligence. Collateral characteristics and performance is tracked and monitored and flexible transaction management can allow for three primary mitigating steps that can be taken on potential State and criminal breaches starting with running tests for identification. We see three primary mitigation options set forth below: 1. Follow the intent of state usury laws and fully exclude violated collateral. 2. Risk-adjust advance rates based on state and level of infraction (Clean or none applied/civil/criminal). 3. Explicitly enumerate and track the collateral in funding for granular itemization in the event issues arise. --- ### [Cardo AI ranked in Chartis BuySide Risk50 2025](https://cardoai.com/article/cardo-ai-ranked-in-chartis-buyside-risk50-2025/) **Published:** May 6, 2025 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#introduction) [Why this matters?](#first-par) [Driving innovation in Asset Based Finance](#second-par) Introduction Cardo AI has been ranked in the prestigious [Chartis BuySideRisk50 2025](https://www.chartis-research.com/buy-side/7947442/buysiderisk50-2025-ranking-and-award-winners). This recognition highlights our commitment to delivering cutting-edge technology for the buy-side, specifically within the complex landscape of asset-based finance (ABF) and private credit. Why this matters? The Chartis BuySideRisk50 is a globally recognized benchmark. It identifies the world’s leading technology vendors serving the buy-side community. To determine the rankings, Chartis analysts evaluate vendors across five core pillars: - **Breadth of functionality**: The completeness of the platform across the entire investment lifecycle. - **Impact**: The vendor’s influence on the market and the accessibility of its tools for clients. - **Computational infrastructure**: The performance, scalability, and modern architecture of the technology. - **Strategy**: How well the company’s roadmap aligns with the evolving needs of asset managers. - **Innovation**: The use of original models and AI to solve complex financial challenges. By scoring highly in categories like **Innovation** and **Computational Infrastructure**, Cardo AI demonstrates that our “engine” is built to handle the most complex data, waterfalls, and reporting requirements in the industry. ![Chartis-buysiderisk50-cardo](https://cardoai.com/wp-content/uploads/2026/05/Screenshot-2026-05-29-at-12.57.02.png) Driving innovation in Asset Based Finance For institutional investors, the private credit market has historically been slowed down by messy spreadsheets and disconnected data. Being included in this ranking validates Cardo AI’s mission to replace those manual workflows with a unified, AI-powered platform that is accountable for: - Real-time data integration: No more waiting for monthly reports - Automated waterfalls: Precise, automated cash flow modeling across every structure - Transparent reporting: Clear insights that satisfy both internal risk teams and external regulators Explore our award-winning platform today. [Book a demo](https://cardoai.com/book-a-demo/) --- ### [Cardo AI Named “Debt Portfolio Management Technology of the Year”](https://cardoai.com/article/cardo-ai-named-debt-portfolio-management-technology-of-the-year-by-the-drawdown-awards-2025/) **Published:** June 16, 2025 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#introduction) [What is this award about?](#first-par) [Why Cardo AI](#second-par) Introduction Cardo AI has been awarded **“Debt Portfolio Management Technology of the Year”** at the [2025 Drawdown Awards](https://the-drawdown.com/article/the-drawdown-awards-2025-winners-announced). This recognition affirms Cardo AI’s leadership in end-to-end private credit technology and portfolio intelligence. What is this award about? Drawdown awards celebrate outstanding achievements by advisers and solution providers supporting private fund operations. Cardo AI was recognized for its innovative role in helping institutional investors manage complex credit portfolios with greater intelligence, automation, and scale. Our solution responds to the growing demand for tools that can navigate data complexity while improving risk-adjusted returns, operational efficiency, and portfolio transparency. As the first technology built to cover the entire illiquid credit market through a multi-strategy approach, Cardo AI enables investment teams to manage asset-based finance, structured credit, direct lending, private credit CLOs, and more, all within one connected environment. Why Cardo AI Cardo AI supports the full investment lifecycle, from origination to settlement, with capabilities spanning real-time data transformation, performance analytics, and fund finance management. Investors can track loan-level cash flows, oversee facilities, and generate reports through a seamless, fully digitized workflow. Designed for decision-making, it enables investment teams to move from passive monitoring to actively driving portfolio performance. The platform also offers integrated fund finance capabilities, including NAV facility tracking and subledger management, providing greater transparency and control across the investment lifecycle. *“At Cardo AI, we are committed to setting a new standard for data management, modelling, and intelligence in portfolio management,”* said **Altin Kadareja**, **CEO and Co-Founder of Cardo AI**. *“This award reflects the incredible work of our team in understanding the needs of asset based finance and private credit investors and delivering tailored, scalable solutions.”* Explore our award-winning platform today. [Book a demo](https://cardoai.com/book-a-demo/) --- ### [Cardo AI Named “Service Provider of the Year”](https://cardoai.com/article/cardo-ai-named-service-provider-of-the-year/) **Published:** October 22, 2025 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#introduction) [What is this award about?](#first-par) [Why Cardo AI](#second-par) Introduction Cardo AI, a leading global fintech in asset-based finance and private credit, has been awarded **“[Service Provider of the Year](https://cardoai.com/press/cardo-ai-named-service-provider-of-the-year-by-scis-risk-sharing-awards-2025/#:~:text=Cardo%20AI%2C%20a,evolving%20regulatory%20requirements.)”** at the [SCI’s Risk Sharing Awards 2025](https://www.events-sci.com/risk-sharing-awards-2025). This recognition affirms our leadership in end-to-end private credit technology and portfolio intelligence. What is this award about? SCI’s Risk Sharing Awards cover the global capital relief trades market and the US credit risk transfer market. The Cardo AI platform was recognised as driving greater efficiency, accuracy, and insight throughout the transaction lifecycle – from data ingestion to portfolio optimisation – for banks, asset managers, and credit institutions. In particular, its modular nature allows offerings to be adapted to the specific needs of each SRT transaction, across different asset classes and evolving regulatory requirements. Why Cardo AI In the evolving world of SRT and synthetic risk transfer, banks face numerous challenges in managing portfolio de-risking. Traditional methods of managing repetitive tasks, such as data collection, document management, and eligibility checks, are time-consuming and prone to errors. Cardo AI addresses these inefficiencies by automating these tasks, freeing up valuable resources and significantly reducing operational costs. Banks also struggle with optimising portfolio construction, determining the right tranche thickness and tailoring SRT strategies to align with specific risk-return objectives. Cardo AI offers precise tools to optimise these processes, ensuring that mezzanine tranche attachments match the institution’s desired risk profile, thereby enhancing the risk-return dynamics of their portfolio. Another pressing issue for banks is the ability to meet regulatory compliance while maintaining confidence in the accuracy of their risk assessments. Cardo AI provides powerful data analysis and risk modelling tools, enabling banks to conduct regulatory-mandated portfolio analysis quickly and with greater confidence. With the ability to generate real-time insights and perform comprehensive quantitative and qualitative assessments, banks can ensure they are fully aligned with the latest regulatory frameworks, maximising the benefits of synthetic risk transfer as a proactive risk management strategy. Explore our award-winning platform today. [Book a demo](https://cardoai.com/book-a-demo/) --- ### [Cardo AI named “Best Data Management Solution for Unstructured Data”](https://cardoai.com/article/cardo-ai-named-best-data-management-solution-for-unstructured-data-by-at-the-data-management-insight-awards-europe-2025/) **Published:** November 3, 2025 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#introduction) [What is this award about?](#first-par) [Why Cardo AI](#second-par) Introduction Cardo AI has been nominated for Best Data Management Solution for Unstructured Data at the [Data Management Insight Awards – Europe 2025](https://a-teaminsight.com/app/uploads/2025/12/A-Team-Group-Data-Management-Insight-Awards-Europe-2025-Special-Report.pdf). This nomination marks a significant milestone in our mission to modernize the private credit and asset-based finance markets. In an industry where over 80% of data is trapped in fragmented PDFs, emails, and legacy spreadsheets, being recognized for our ability to turn this “dark data” into institutional-grade intelligence is a testament to our team’s dedication and the power of our AI-driven technology. What is this award about? Now in its fourth year, the Data Management Insight Awards – Europe, hosted by the A-Team Group, recognizes the “cream of the industry.” These awards acknowledge the leading providers of data management solutions, services, and consultancy for capital markets participants across Europe. From established global vendors to innovative newcomers, the awards highlight those who are revolutionizing how the financial industry handles risk, governance, and technology. Why Cardo AI This nomination hits at the core of what we do. For institutional investors and originators, the biggest bottleneck to scaling isn’t a lack of data – it’s the difficulty of using it. Cardo AI’s platform was built specifically to solve this. By leveraging advanced machine learning, we enable firms to: - Automate extraction: Seamlessly pull critical data points from complex legal documents and servicer reports. - Ensure integrity: Transform unstructured “messy” data into a standardized, “single source of truth” for reporting and risk modeling. - Accelerate decisions: Reduce manual data entry errors, allowing risk teams to focus on high-level analysis rather than data cleaning. As the private markets continue to grow in complexity, the ability to master unstructured data has become a competitive advantage. Explore our award-winning platform today. [Book a demo](https://cardoai.com/book-a-demo/) --- ### [Building trust through transparency: introducing the Cardo AI Trust Centre](https://cardoai.com/article/building-trust-through-transparency-introducing-the-cardo-ai-trust-centre/) **Published:** April 10, 2026 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#introduction) [Why a Trust Centre matters](#first-par) [What you’ll find in our Trust Centre](#second-par) [Designed to support our customers](#third-par) Introduction For organisations operating in regulated and data-sensitive environments, trust isn’t built through promises alone. It comes from transparency, clarity, and the ability to independently assess how security and risk are managed in practice. That’s why we’ve launched the Cardo AI Trust Centre. Why a Trust Centre matters As our platform and customer base have grown, so too has the need to provide clear, consistent, and accessible information about our security and governance practices. Our customers, regulated institutions managing complex portfolios, need more than reassurance. They need: - clear answers to common security and risk questions; - independent assurance they can rely on; - transparency into how controls are designed and operated; - information that supports their own due diligence and risk assessments. What you’ll find in our Trust Centre The Cardo AI Trust Centre brings together key security and assurance information in one place: - SOC 2 Type II reports and supporting assurance materials - Security whitepapers and governance documentation - Information on how we protect customer data - Answers to frequently asked questions Designed to support our customers Security should enable progress, not slow it down. The Trust Centre is built to: - Speed up onboarding and procurement processes - Reduce duplication during security reviews and audits - Enable more informed conversations between teams - Support customers in meeting their own regulatory and compliance obligations Curious to see what our security programme looks like in practice? Visit our [Trust Center](https://trust.cardoai.com/). --- ### [Buy vs. Build: why building ABF operating systems internally is harder than it looks](https://cardoai.com/article/why-building-abf-operating-systems-internally-is-harder-than-it-looks/) **Published:** May 13, 2026 **Author:** GiadaCardo26 **Content:** Contents [Introduction](#introduction) [The hidden complexity of building internally](#first-par) [Key decision points: when to Build vs. Buy](#second-par) [Why Cardo AI](#third-par) [The bottom line](#fourth-par) Introduction The rise of AI has made many financial institutions ask a fair question: *should we build this ourselves?* At first glance, building an internal platform for data management, collateral monitoring, reporting, transaction management, and analytics may seem achievable. Most institutions already have data teams, cloud infrastructure, analysts, and access to AI tools. But in structured finance, private credit, and asset-backed finance, the challenge is rarely just building software. The real challenge is building a controlled operating environment that can ingest imperfect data, normalize it across asset classes and counterparties, apply transaction-specific logic, reconcile outputs, support auditability, and scale across hundreds of deals without becoming a permanent internal maintenance burden. And that’s just for the first version. A successful internal build does not end at go-live. It creates a permanent obligation to support new deals, new asset classes, new reporting requirements, new counterparties, new data formats, new users, new controls, and new regulatory expectations. What starts as a technology initiative can quickly become a long-term product, engineering, operations, and support function. That is where the build decision becomes far more complex. The hidden complexity of building internally Building a Cardo-like platform is not simply a matter of connecting data sources and applying AI models. The core difficulty lies in the financial logic, operating controls, data quality framework, and institutional workflows required to manage complex credit portfolios. Internal teams typically underestimate the effort required to: **Standardize inconsistent data from many sources Borrowers, servicers, trustees, agents, originators, and internal systems all provide data in different formats, definitions, frequencies, and levels of quality. **Translate deal documents into operational rules Eligibility criteria, concentration limits, waterfalls, borrowing bases, covenants, triggers, and reporting obligations vary by transaction and require precise configuration. **Maintain data quality at scale and over time Validation checks, reconciliations, exception handling, duplicate detection, historical tracking, and audit trails must work continuously, not just during implementation. Borrowers, servicers, trustees, agents, originators, and internal systems change file formats, data fields, naming conventions, delivery schedules, and reporting practices. Every change creates a maintenance burden. **Support multiple asset classes and structures Consumer loans, SME loans, auto loans, real estate, infrastructure, fund finance, CLOs, forward flows, warehouses, and ABS structures all introduce different data and calculation requirements. **Keep pace with business change New deals, new reporting templates, new counterparties, new asset classes, and new internal requirements constantly create new development and maintenance needs. **Ensure governance and auditability Financial institutions need controlled workflows, traceable calculations, permissioning, approval processes, and defensible outputs – especially when results affect funding, compliance, investors, or risk decisions. AI can accelerate parts of this process, but it does not eliminate the need for domain expertise, clean data architecture, financial engineering, implementation experience, and continuous product maintenance. ![Two-column infographic: Build Successes on the left and Build Failures on the right, with example notes under each.](https://cardoai.com/wp-content/uploads/2026/05/buy-vs-build-2.png) Key decision points: when to Build vs. Buy The right question is not simply “can we build it?” The better question is: “what should our internal teams spend time building, and do we want to own the full lifecycle of maintaining, upgrading, and supporting this platform indefinitely?” A build decision may make sense when the workflow is narrow, highly proprietary, and unlikely to change. But buying is often the better path when the platform needs to support multiple transactions, counterparties, reporting formats, asset classes, and control requirements. Prospects should evaluate: **Time to value Can the internal build deliver usable results in months, or will it become a multi-year program? **Total cost of ownership Beyond initial development, who will maintain integrations, data checks, calculations, reporting logic, releases, and user support? **Domain expertise Does the internal team have deep experience across ABF, private credit, securitization, CLOs, waterfalls, borrowing bases, and collateral monitoring? **Scalability Will the platform work for one deal, or can it support hundreds of facilities, millions of assets, and evolving transaction structures? **Operational risk What happens if calculations are wrong, data breaks, reporting is delayed, or key developers leave? **Opportunity cost Should internal teams spend time recreating infrastructure, or focus on proprietary investment, risk, and client-facing capabilities? ![Three-column comparison table: left labels (Time to Production, Dedicated Team, Abandonment Risk, 5-Year Direct Cost); middle column Internal Build with 18–36 months, 12–20 FTE, 25–35% risk, $10.5M–$17.5M; right column CARDO AI with 8–16 weeks, 1–3 FTE,](https://cardoai.com/wp-content/uploads/2026/05/buy-vs-build-1.png) Why Cardo AI Cardo gives institutions a proven platform built specifically for private credit, structured finance, and asset-backed finance. Instead of starting from scratch, clients can leverage a production-ready environment for data ingestion, validation, reconciliation, collateral monitoring, transaction management, analytics, reporting, and workflow control. Cardo is not a one-off implementation. It is a continuously evolving platform supported by dedicated product, engineering, data, and structured finance experts. As market requirements change, Cardo continues to improve – across integrations, analytics, reporting, AI capabilities, asset class coverage, and operational workflows. With Cardo, institutions avoid the burden of building and maintaining an internal platform alone. They benefit from a specialized solution that has already been tested across hundreds of complex transactions, large datasets, institutional workflows, and real-world operating requirements. The bottom line Building internally may appear cheaper and more flexible at the start. In practice, institutions often discover that the true cost includes years of development, ongoing maintenance, specialist hiring, deal-by-deal customization, control remediation, and operational risk. The real cost comes after go-live: maintaining integrations, updating deal logic, improving workflows, supporting users, fixing edge cases, scaling infrastructure, and keeping pace with market and business changes. Buying Cardo allows teams to accelerate transformation while preserving internal resources for the areas where they can create the most differentiated value. **Want to compare build vs. buy with real numbers? Reach out to Cardo AI for a dedicated case study showing how the cost, time, and complexity of building internally compare to deploying the proven Cardo AI solution. [Book a demo](https://cardoai.com/book-a-demo/) --- ### [AI Manifesto](https://cardoai.com/article/ai-manifesto/) **Published:** June 17, 2026 **Author:** GiadaCardo26 **Content:** How the buy-side will be rebuilt. Six perspectives from the Cardo AI leadership team on infrastructure, product, agents, data, and the future of work in private credit. [Download Manifesto](https://share.hsforms.com/1KL_-0WgmSKick8sZ7L80Kg59ao2) [Watch Video](https://youtu.be/MJtnAMIedWU) Contents [Preface](#introduction) [I. The buy-side imperative](#first-par) [II. The data and infrastructure layer](#second-par) [III. The research frontier](#third-par) [IV. Software, data, and intelligence as one product](#fourth-par) [V. Agentic workflows for credit](#fifth-par) [VI. Tabular foundation models and alternative data](#sixth-par) #### PREFACE Why we are writing this People sometimes ask when Cardo decided to add the “AI” to its name. The answer is that we never added it. The AI has been there since day one. When we founded the company seven years ago, plenty of people told us we were crazy, that the AI part was a marketing flourish on a fintech idea, that the asset-based finance and private credit market would not be ready for it for a generation, that the buy side would never trust a machine with anything that mattered. We disagreed then, and we disagree now, and what was a conviction in 2018 has become reality in 2026. Asset-based finance and private credit are no longer niche. Together they represent one of the largest pools of risk capital in the world, financing everything from small-business invoices to consumer auto loans, from houses and solar installations to the GPU clusters powering the AI economy itself. The market is now measured in tens of trillions of dollars. ![Professional headshot of a man with short dark hair and glasses, wearing a navy suit and blue striped tie, in a bright office background.](https://cardoai.com/wp-content/uploads/2026/05/Jon-Gray-President-COO-Blackstone.png)> *Investment-grade private credit is increasingly filling the financing gaps left by traditional lenders, funding everything from energy and transportation to digital infrastructure and consumer finance.* > > Jon Gray, President & COO, Blackstone · WSJ Invest Conference, February 2026 And yet, the way this market is operated has not kept pace with what it has become. Decisions worth hundreds of millions of dollars are still made on the back of spreadsheets emailed between counterparties. Loan tapes arrive in dozens of incompatible formats. Covenants live as paragraphs of legal prose rather than as executable rules. Risk teams discover deterioration months or quarters after the data was already there to see it. We are writing this manifesto for two reasons. First, because we believe this transition will be the defining competitive event of the next decade in private markets, and we want to be honest about both the opportunity and the risks. Second, because we have spent the last eight years at Cardo AI building, deploying, and operating exactly this kind of infrastructure with some of the most demanding investors and lenders in the world, and what we have learned is worth saying out loud. #### PART I The buy-side imperative By **Altin Kadareja**, Co-founder & CEO ![](https://cardoai.com/wp-content/uploads/2026/05/Altin-Kadareja.png)> *Private markets did not become large because they were efficient. They became large because they were the only place where certain kinds of capital could go. Efficiency is the next chapter, and AI is how it gets written.* > > Altin Kadareja, Co-founder & CEO, Cardo AI **Is ABF still niche? A simple test** Before we talk about what is broken in this market, it is worth pausing on what this market actually is, because the question I am asked most often by allocators new to the asset class is whether ABF is still niche or whether it has crossed over into mainstream. My answer is that it is rapidly mainstreaming, and the easiest way to see it is to walk through an ordinary day. You wake up in the morning, and there is a roof over your head, that is mortgage finance. You go to work, and you use the car to get there: auto finance. You order something online: revenue-based finance for the merchants on the e-commerce platforms, and equipment finance for the fleets of trucks. You use AI tools at work: data centres and GPU financing. You pick up your phone: cell-tower financing. The day you have just lived was financed, in some non-trivial part, by asset-based finance. ![](https://cardoai.com/wp-content/uploads/2026/05/Doug-Ostrover-Co-CEO-Blue-Owl.png)> *Asset-based lending is the next big boom in private credit. Whether it’s equipment, rail, aerospace, consumer funding, the total addressable market is way bigger than the private credit market.* > > Doug Ostrover, Co-CEO, Blue Owl **What is actually broken** The honest description of the current state of the market is not flattering. Most asset-based finance and private credit operations today are running on a stack that looks like this: a credit agreement in PDF, a loan tape in Excel arriving via email or SFTP each month, a borrowing base computed in another spreadsheet, a covenant tracker maintained by an analyst, a waterfall that lives in a fourth spreadsheet, and an investor reporting pack assembled in PowerPoint at the end of each quarter. Between these artefacts sit human beings reconciling numbers that should never have been allowed to disagree in the first place. What unites these failures is that none of them are really failures of judgement. They are failures of plumbing. The credit officer is not making the wrong call; the credit officer simply does not see the evidence in time. ![Altin Kadareja, Co-founder & CEO, seated at a table in a modern open-office with a gray mug in front.](https://cardoai.com/wp-content/uploads/2026/05/cover-altin-kadareja-ai-manifesto.png) **Three classes of value, in order of how quickly they arrive** Across the deployments we have lived through with our clients, the value of AI in private credit and ABF arrives in three waves. They are sequential and they are cumulative. Skipping ahead does not work. **Wave one:** time-to-decision compression. A loan tape that used to take three days to crack now takes three hours. A credit memo first draft that used to take a week now takes a morning. In a market where the manager who delivers a credible term sheet first wins the deal, time compression is a direct revenue lever. **Wave two:** surveillance density. Once data flows through a system rather than an inbox, you do not have to wait for month-end to know how the portfolio is doing. You can watch all credits, all the time, against the same set of indicators. The financial value of surveillance density is asymmetric, small in good times and very large in bad times. **Wave three:** structural intelligence. The wave in which the AI is not running specific tasks but is the operating model itself. Every deal a firm has done becomes a queryable precedent. The firm stops being a collection of analysts who happen to share a logo, and becomes a single learning system that improves with every deal it touches. **What we believe at Cardo AI** **First: AI in this market is not a feature. It is a layer.** It runs underneath the workflows that already matter (onboarding, eligibility, covenants, monitoring, reporting) and it makes them faster, denser, and more honest. If your AI strategy starts with a chatbot, you are starting in the wrong place. **Second: the buy-side problem is fundamentally a data problem.** There is no useful AI on top of bad data. Most of the work, and most of the differentiation, is in the layer that turns messy, multi-source, multi-format inputs into a single, validated, traceable source of truth. **Third:** **the future of credit work is not human-versus-machine.** It is human-with- machine, with the human spending their time on the parts of the job that actually require judgement, and the machine handling the parts that never should have been done by humans in the first place. The firms that get this balance right will produce more deals, better decisions, and lower operational risk than the firms that do not, and the gap will widen each year. **Fourth:** **governance is not a brake on AI. It is the steering wheel.** The firms that build proper AI governance: model inventories, change-management, traceability, human override at every consequential decision will move faster, not slower, than the firms that try to skip it, because they will be the ones that supervisors, allocators, and auditors trust **Fifth, and finally: the window in which AI is a competitive advantage is shorter than people think.** Within two years, every serious firm in this market will have some version of this stack. The question is who builds the muscle now, while the playing field is still uneven, and who waits until parity becomes the price of admission. #### PART II The data and infrastructure layer By **Euron Metaliaj**, Co-founder & CTO ![](https://cardoai.com/wp-content/uploads/2026/05/Euron-Metaliaj.png)> *There is no good AI on top of bad data. The hardest, least glamorous, and most valuable work in private credit right now is in the plumbing. Get it right and everything downstream gets easy. Get it wrong and nothing – not the model, not the dashboard, not the talent – can save you.* > > Euron Metaliaj, Co-founder & CTO, Cardo AI **The infrastructure problem, stated plainly** Every credit business is, when you strip away the strategy and the marketing, a data business. The loan tape is data. The credit agreement is data. The covenant is data. The borrower’s financials are data. What separates a manager who can grow from one who cannot is whether their data architecture can absorb new asset classes, new originators, new structures, and new reporting requirements without each of those additions costing six months of engineering. The brutal reality of asset-based finance and private credit infrastructure today is that almost no one has solved this. When you look inside even very large managers, you find loan tapes arriving in dozens of bespoke formats (Excel files with merged headers, fixed-width text, password-protected PDFs, SFTP drops with no schema attached), fields named differently across originators and dates encoded in incompatible standards. > *Often, there is one set of systems for public markets and another for private markets. Integrating these is challenging, and often the results are not optimal… The imperative to break free from this multi-system framework into something that brings it together in a scalable, integrated infrastructure platform is becoming increasingly urgent.* > > BlackRock Aladdin, Global Insurance Report 202518 **The four-layer architecture behind modern ABF tech** Most credit platforms are tools stitched together. The better approach is one opinionated system with four layers. - **Ingest and normalise:** Every tape arrives differently. The right layer maps fields deterministically, uses AI for harder semantic matches, and validates instantly. Every record carries full provenance, making everything auditable. - **Canonical model:** One representation of what a loan, borrower, and cash flow are, regardless of asset class. Built around invariants, so auto loans, CLOs, and solar leases are specialisations of the same model, not separate silos. - **Rules engine:** Eligibility tests, waterfalls, covenant calculations must run deterministically. AI belongs at the edges, digitising agreements in, explaining results out. If a covenant test depends on which AI model ran it, you don’t have a covenant test; you have a liability. - **Analytics and reporting:** Where AI drafts memos, commentary, and dashboards. But a beautifully written IC memo built on an unverified tape is just a faster way to make a worse decision. One principle cuts across all four: observability. The system must be loud when something changes: unexpected rows, broken mappings, shifting ratios. Silent failures are how data incidents become regulatory incidents. ![Two men sit at a dark conference table in a modern office; the foreground man in a red polo looks to the side, with a caption in the lower-left reading “Euron Metalaj, Co-founder & CTO.”](https://cardoai.com/wp-content/uploads/2026/05/cover-euron-Metaliaj-ai-manifesto.png) **What good looks like, operationally** If your infrastructure is working, you should be able to do all of this without writing a ticket: - Onboard a new originator in 1–2 days: mapping their tape is a configuration exercise, not an engineering project - Run eligibility tests against last night’s data: not last month’s close - Add a new covenant and back-test it overnight: because it’s a rule, not a script - Answer complex exposure queries in seconds: because the canonical model already knows what an obligor, leverage, and DSCR are If you cannot do these things today, you do not have an AI problem. You have an infrastructure problem. AI on top of broken infrastructure is faster broken infrastructure. #### PART III The research frontier in Private Credit AI By **Gennaro Di Brino**, Head of Research and Data Science · 8 minute read ![](https://cardoai.com/wp-content/uploads/2026/05/Gennaro-Di-Brino.png)> *Three years ago, credit research was a tuning problem: which gradient-boosted tree wins on this dataset? Today it is a positioning problem: which firms recognise that training is no longer the central act, and the implications are larger than they look* > > Gennaro Di Brino, Head of Research and Data Science, Cardo AI **The state of the art** The techniques that dominated the past decade, gradient-boosted trees, regularised regressions, hand-engineered features, still work. But three developments are changing what “good” looks like: the maturation of LLMs for unstructured-data extraction, the arrival of tabular foundation models, and the practical viability of multi-agent architectures. Ignoring any of them has already started to be a competitive mistake. **Tabular foundation models: the quiet revolution** TabPFN and its successors are transformers pre-trained on millions of synthetic tabular datasets. Rather than training from scratch on each new problem, they perform classification or regression in-context, the way a language model is shown a prompt. On the public TabArena leaderboard, TabPFN 2.6 wins roughly 83% of head-to-head comparisons against tuned XGBoost. For private credit, this matters for two reasons: these models excel on small datasets (exactly what most ABF portfolios look like), and they produce well-calibrated probability outputs without post-hoc calibration work. The limits are real. The current generation works well up to roughly fifty thousand rows. And a 7M-parameter transformer pre-trained on synthetic data is a harder defence in front of a model-risk committee than a decade-proven XGBoost. We are doing the validation work. But anyone claiming it is straightforward has not deployed one in a regulated context. ![](https://cardoai.com/wp-content/uploads/2026/05/Frank-Hutter-CEO-and-Co-Founder-Prior-Labs.png)> *Tabular data, structured rows and columns that run the majority of enterprise systems, forms the backbone of financial records, supply chains and customer databases, but progress in tabular AI has lagged fields such as vision and language because the datasets share few consistent patterns.* > > Frank Hutter, CEO and Co-Founder, Prior Labs (December 2025) **Language models in credit research** The pattern that works is consistent: the language model proposes; the deterministic system disposes. LLMs are excellent at first-pass extraction and flagging. They are dangerous as systems of record. Putting an LLM on the path of an authoritative number is an unforced error. Putting one on the path of an interpretation a human will then verify is one of the highest-leverage things you can do in this market. ![Gennaro Di Brino, Head of Research and Data Science, sits at a table with a white mug during a panel discussion.](https://cardoai.com/wp-content/uploads/2026/05/cover-gennaro-di-brino-ai-manifesto.png) **What we are researching now** **Thin-data segments:** Revenue-based finance, BNPL, legal claims, public administration receivables – asset classes with short histories where traditional ML struggles most. Tabular foundation models are purpose-built for this regime. **Early-warning indicators:** Leading signals that fire before the financial statements show deterioration: covenant-headroom trajectories, customer-concentration drift, working-capital-cycle elongation, management-commentary tone shifts. **Causal modelling:** Predictive models tell you what tends to happen. Causal foundation models let you ask what would happen, “what is the effect on my book if rates rise 200bps?”, without months of econometric setup. **Institutional knowledge retrieval:** Every credit firm has years of decisions, memos, and post-mortems that, if queryable, would be its most valuable competitive asset. The technology to embed and retrieve this corpus responsibly is now mature. The alternative, firm history scattered across laptops and email, is not actually a strategy. #### PART IV Software, data, and intelligence as one product By **Marco Masotto**, Head of Product ![](https://cardoai.com/wp-content/uploads/2026/05/Marco-Masotto.png)> *The market has spent ten years debating whether the answer is software or data, with AI just recently positioning itself as yet another alternative. But you don’t need to choose: the answer is all three, fused into a single product. Anything less leaves the user reconciling outputs across tools that should never have been separate.* > > Marco Masotto, Head of Product, Cardo AI **The category problem** If you ask a buyer in private credit today what they need, you will get three different answers depending on who you ask. The CTO will say “we need better infrastructure.” The Head of Risk will say “we need better models.” The Head of Investments will say “we need more data.” Each of them is right. None of them is right alone. The reason the market has under-served them for so long is that the vendors have organised themselves around these three answers rather than around the underlying problem. There are software companies that build excellent workflow tools but rely on the customer to bring their own data. There are data companies that aggregate excellent data but expect the customer to bring their own software. There are AI companies that build excellent models but assume the customer has already solved both data and software. In each case, the customer is left as the systems integrator, doing the work that should have been done by the product. The thesis of the Cardo AI product is that the three are not separable. Software without data is empty; data without software is unusable; AI without both is a parlour trick. The product the market actually needs is a single platform in which the three are designed together – where the workflows speed up execution and reduce risk, the data are properly managed the workflows, and the intelligence layer is built on top of both. ![Marco Masotto, Head of Product, sits at a conference table in a white shirt and dark vest.](https://cardoai.com/wp-content/uploads/2026/05/cover-marco-masotto-ai-manifesto.png) **What “one product” actually means in practice** It is easy to claim integration in a marketing document. It is harder to design for it. Let me describe what it actually means in the day-to-day experience of a user. When a credit analyst uploads a new loan tape, the platform does not just store it. The ingestion layer normalises the file against an asset class specific canonical model, the validation layer flags any rows that fail integrity checks, the rules engine immediately re-runs eligibility tests on the updated portfolio, the covenant tracker updates the headroom calculations, the analytics layer refreshes the dashboards, and the AI layer surfaces any borrowers whose metrics have drifted in a way that warrants attention. None of this requires the analyst to do anything beyond the upload. The system understands what the file means and what to do with it, because the workflow, the data model, and the intelligence are the same product. ![](https://cardoai.com/wp-content/uploads/2026/05/Ali-Ghodsi-CEO-and-Co-Founder-Databricks.png)> *Enterprises are rapidly reimagining how they build intelligent applications, and the convergence of generative AI with new coding paradigms is opening the door to entirely new workloads…we’re deepening our commitment to help every organization innovate with AI on their own data.* > > Ali Ghodsi, CEO and Co-Founder, Databricks (December 2025) **The product principles we have learned** **1) Determinism for numbers, AI for everything else** Numbers – borrowing-base calculations, eligibility tests, covenant headroom – are deterministic. They have to produce the same answer every time. AI does not belong on this path; rules engines do. Everything else, drafting commentary, extracting covenants, generating IC memos, is where AI dramatically outperforms the human-with-spreadsheet status quo. **2) Traceability is a feature, not an afterthought** Every output must be traceable to its inputs. The IC memo links to the loan tape. The covenant test links to the credit agreement clause. The risk score links to the model version. This cannot be added later. It is also what makes the product work with regulators — firms that built it in have a much shorter path to EU AI Act compliance than firms retrofitting it. **3) Configurability over customisation** Every credit firm thinks its workflows are unique. The covenants and structures are, but what a covenant is, what a borrowing base is, are the same across firms. Express the invariants as platform, the specifics as configuration. One-offs become technical debt. The discipline of asking “is this a new invariant or a configuration of one we have?” is what allows new clients to onboard in weeks, not months. **4) Engineering and domain expertise as a single team** You cannot build for this market with engineers alone, nor with structured finance specialists alone. The engineer who hasn’t lived through a quarter-end close doesn’t know what an asset manager needs. The specialist who can’t write code can’t solve complex problems. The product is what emerges when that gap has already been bridged inside the team, not left for the customer to bridge. **Why rebuilding it yourself is slower, not faster** The most common CTO pushback: why can’t we build this ourselves? You can. But consider what you are actually building: a canonical data model you will discover is missing five fields in year three; a waterfall model that works for the first structure and breaks on the second; an eligibility engine that has never seen the edge cases that arrive in year four. The time cost of those discoveries is not a technology cost. It is an opportunity cost measured in deals not done and deterioration not caught. When you use Cardo, you are buying eight years of decisions about what a canonical data model should contain, what an eligibility test should handle, and what happens when an originator sends something you have never seen before – because we have seen it before, across hundreds of deals and dozens of asset classes. #### PART V Agentic workflows for credit By **Stefano Penazzi**, Head of Agentic AI Solutions ![](https://cardoai.com/wp-content/uploads/2026/05/Stefano-Penazzi.png)> *Agentic AI is a game changer for asset-based finance, turning complex asset analysis, structuring, and risk decisions into a faster, smarter, and scalable process.* > > Stefano Penazzi, Head of Agentic AI Solutions, Cardo AI **Beyond the copilot: our vision for Agentic AI in ABF** When most people talk about AI in credit, they imagine a single intelligent assistant, a chatbot, perhaps, or a copilot embedded in a spread-sheet, that helps with one task at a time. That model has its uses. It is not what we believe the future of work in this market looks like, and it is not what the most ambitious firms are deploying. The challenge with a single-agent model is not just that private credit and ABF involve many tasks; it is that the work is inherently iterative, stateful, and interdependent. A deal is not processed in a straight line from ingestion to output. Data is extracted, normalized, checked, challenged, re-run, compared to policy and precedent, and often revised as new issues emerge. Reading a data room, cleaning a loan tape, computing eligibility, generating sensitivities, drafting an IC memo, and benchmarking against comparable transactions are not simply consecutive steps; they are connected parts of a workflow that loops back on itself until the analysis is robust enough for decision-making. Asking one general-purpose assistant to manage all of this is asking it not only to do many different jobs, but to coordinate them, remember them, and refine them in context. That is why the architecture that works is multi-agent: a coordinating layer that breaks the problem into components, routes each to the right specialist, and manages state across the process. In ABF, those capabilities are what turn AI from an assistant into an operating model. > *Even the most sophisticated models are constrained by their isolation from data, trapped behind information silos and legacy systems. Every new data source requires its own custom implementation, making truly connected systems difficult to scale.* > > Anthropic, on launching the Model Context Protocol (November 2024) **The architecture, in plain language** The **coordinator** reads the request, decomposes it into sub-tasks, and routes them. It is the difference between a system that reacts to commands and one that executes plans. **Specialised agents** each do one job well – ingestion, logic parsing, spreading, precedent search, drafting. The orchestrator coordinates them; the human reviews the result. **Shared state and memory** logs every step, every input, every output – making the system fully auditable and every decision reproducible. **Human checkpoints** sit at the points of consequential judgement: before a credit decision is recommended, before a covenant is committed to the rules engine, before an IC memo is finalised. Between those points, the system runs autonomously. **The agents we have built, and the ones we are building** In production today, our multi-agent stack covers the workflows that consume the most analyst hours in a typical credit firm. None of them replaces a human; all of them change what the human does. Origination & screening: monitors deal flow, scores fit against underwriting criteria, and drafts a preliminary brief before an analyst opens the email. Data room & loan tape: classifies documents, flags missing or draft materials, normalises the loan tape, and surfaces validation failures. Covenant extraction & monitoring: reads credit agreements, extracts covenants, proposes mappings for human sign-off, then monitors compliance continuously. Borrower monitoring: processes each financial package on arrival, compares it to prior periods and the original underwriting model, and flags material drift. IC memo & sensitivity: drafts the financial section of the credit memo, runs standard stress scenarios, and links every assertion to its source. LP & investor reporting: assembles the reporting pack, drafts commentary, tailors views by mandate, and presents for sign-off rather than manual assembly. **The risks specific to multi-agent systems** 1. **Error compounding:** Small errors propagate and amplify across agents. The fix is deterministic validation at every handoff, the next agent doesn’t start until the previous agent’s output passes integrity checks. 2. **Observability:** A multi-agent system is a small distributed system. We invest heavily in per-agent logging, tracing, and replay tooling, agents whose behaviour can’t be reconstructed after the fact are incompatible with regulated deployment. 3. **Governance:** Every consequential action needs a designated human owner, a full log of the inputs that produced it, and the ability to reverse it. “The agent is accountable” is not an answer. 4. **Autonomy:** When the system works well, the temptation is to remove human checkpoints. Resist it. The checkpoints are not friction, they are the accountability mechanism. Firms that removed them had to walk back deployments because no one could explain what the system did or why. **Where this goes next** The near-term frontier is cross-firm interoperability, agents coordinating with agents at counterparty firms, compressing deal cycles, and institutional learning loops built on the firm’s own memos, declines, and outcomes. That second capability, over five years, becomes a moat no competitor can replicate. The more consequential shift is from agents that assist humans to agents that work for them. Users won’t watch agents do work; they’ll delegate outcomes. The agent surfaces what matters and asks for help when it needs it. I believe this is how credit firms will operate within five years. ![Video thumbnail: Stefano Penazzi, Head of Agentic AI Solutions, seated at a conference table with a colleague in a glass-walled office.](https://cardoai.com/wp-content/uploads/2026/05/cover-stefano-penazzi-ai-manifesto.png) #### PART VI Tabular foundation models and alternative data By **Tommaso Guerrini**, Head of Tabular AI ![](https://cardoai.com/wp-content/uploads/2026/05/Tommaso-Guerrini.png)> *New data without new modelling is just more information to manage. New modelling without new data is just a better way to extract the same signal. The real opportunity lies where the two meet.* > > Tommaso Guerrini, Head of Tabular AI, Cardo AI **Data and models: why they only work together** Better data without better models is just more spreadsheet rows. Better models without better data are faster ways to reach the same conclusions everyone else already has. The edge is at the intersection, applying modern modelling techniques to data sources the rest of the market is still treating as marketing material. **What alternative data actually means in private credit** The data that moves the needle isn’t satellite imagery or social sentiment. It’s cash flows from bank accounts and payment processors, already improving predictive accuracy by over 40% in some consumer credit applications. It’s the operational data sitting inside origination, servicing, and collections platforms that has always existed but was too messy to use: how long underwriting took, how often borrowers called servicing, the seasonality of payments. It’s the asset-class-specific signals that never appear on the loan tape, obligor payment behaviour for receivables, platform revenue data for RBF, production readings for solar lease pools. And it’s the firm’s own memos, declines, and post-mortems, sitting unread in documents for years, now indexable through language models. That last one is the data source no competitor can replicate. ![Tommaso Guerrini, Head of Tabular AI, sits at a conference table with a mug in front, two colleagues in the background in a modern office setting.](https://cardoai.com/wp-content/uploads/2026/05/cover-tommaso-guerrini-ai-manifesto.png) **How tabular foundation models change the equation** Traditional credit modelling resets from scratch with every new data source – collecting, cleaning, engineering features, training, validating, deploying. The cycle takes months. Tabular foundation models collapse the training end of that cycle dramatically. Fitting and evaluating a candidate model takes minutes instead of days. Asset classes too small or too young for conventional ML, BNPL portfolios, revenue-based finance, public administration receivables, become viable. And when experimentation is cheap, the calculus of which data sources are worth acquiring changes entirely. Sources that would never have justified a multi-month modelling project are now worth a one-week pilot. That changes data strategy, which changes what deals a firm can credibly underwrite. ![](https://cardoai.com/wp-content/uploads/2026/05/Daniel-Pietrzak.png)> *In many cases, it’s almost like surgery to rip these systems out (referring to entrenched software at borrowers)* > > Daniel Pietrzak, Global Head of Private Credit, KKR (April 2026) **The honest caveats** These models work best on small and medium datasets, they are not a replacement for well-tuned models where you have millions of rows, and we use them as a fast experimentation layer and in ensembles. Every data source needs proper consent and documentation; cash-flow data without authorisation is a regulatory liability, not an asset, and this is non-negotiable under GDPR and the EU AI Act. The asset classes where alternative data adds the most value are often the youngest, with no track record through a downturn, models that look strong in benign conditions can be confidently wrong when the cycle turns, and anyone claiming otherwise is overselling. Finally, richer data does not mean fewer fair-lending obligations. Cash-flow features and behavioural signals can correlate with protected characteristics in ways that aren’t obvious: payment timing can carry geographic signal, transaction patterns can carry demographic signal. We run bias testing on every model we ship and treat the results as hard constraints, not a compliance checkbox. Ready to see how we build the AI rails and rules engines that help credit teams multiply their output? [Book a demo →](https://cardoai.com/book-a-demo/) Not ready fo a demo? Tell us how you are currently handling messy data, and we’ll share what we’ve learned. [Let’s talk →](https://cardoai.com/company/contact-us/) --- ### [The EU AI Act now has teeth. Here's what ABF teams should know](https://cardoai.com/article/eu-ai-act-now-has-teeth/) **Published:** June 19, 2026 **Author:** GiadaCardo26 **Content:** Contents [Introduction](#introduction) [What “high-risk” actually means](#first-par) [December 2027 is closer than it looks](#second-par) [How we think about this at Cardo AI](#third-par) [What comes next](#fourth-par) Introduction For the past two years, the [EU AI Act](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai) has felt like a distant obligation, something to monitor, not act on. That’s changing. The European Commission just published its most detailed guidance yet on which AI systems fall into the high-risk category, and a political agreement in May shifted the key compliance deadline to December 2027. The rules are getting real. The question now is whether your organisation is treating that date as a deadline or a starting point. What “high-risk” actually means Most of the confusion around the AI Act comes down to one question: does it apply to me? [Article 6](https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-6) gives two answers. The first covers AI embedded in physical products that already require third-party safety certification under EU law, think medical devices or industrial machinery. The second, and more relevant for financial services, is a defined list of use cases that carry high-risk status regardless of the product they sit in. That list includes credit assessment, access to essential financial services, and employment. Categories that cover a significant portion of what AI is actually being used for in ABF and private credit today. The draft guidelines published on 19 May don’t expand that list. What they do is explain, with concrete examples, how the Commission expects firms to assess whether their specific systems fall inside or outside each category. For anyone who has been waiting for that level of clarity before starting their assessment, the waiting is over. If your systems touch any of those categories, the time to assess is now. December 2027 is closer than it looks The original deadline for Annex III obligations was August 2026. The Digital Omnibus agreement pushed that to December 2027, a meaningful extension, and one that gives organisations real room to get this right. But high-risk classification under the Act isn’t a box-ticking exercise. Conformity assessments, technical documentation, human oversight mechanisms, transparency requirements, ongoing monitoring. Done properly, this takes time. Firms that start in 2027 will be rushing. Firms that start now will be ready. How we think about this at Cardo AI We’re an optimisation and data intelligence platform. Our job is to give ABF and private credit teams better information, faster, not to make decisions for them. The human stays in the loop. That’s not a compliance workaround; it’s how we think good AI in finance should work, and it’s been built into the platform from the start. That design is central to how we approach Article 6. A platform built around human decision-making, auditability, and transparency starts from a fundamentally different position than one that doesn’t. What we’re not doing is starting from scratch. Our AI framework has been aligned to the AI Act, GDPR, and ISO 42001 since before compliance was the reason to do it. The governance, auditability, and oversight controls our clients rely on aren’t a response to regulation, they’re what makes the platform work in environments where getting it wrong has real consequences. For clients who want the detail, it’s all in our [Trust Centre](https://trust.cardoai.com/). What comes next The Commission’s consultation on these draft guidelines runs until 23rd July. Final guidance is expected before the end of the year, and we’ll continue to share how we’re thinking about these developments as they unfold. If you have questions about what any of this means for your use of the Cardo AI platform, [get in touch.](https://cardoai.com/company/contact-us/) --- ### [Beyond the Data Wall: Redefining Credit Strategy with Foundation Models](https://cardoai.com/article/landscape-analysis-of-tabular-foundation-models/) **Published:** July 22, 2026 **Author:** GiadaCardo26 **Content:** Contents [The data was always the wall](#introduction) [What actually changed](#first-par) [Cheap models change what you can underwrite](#second-par) [If the model is the commodity, the edge is the context](#third-par) [What this looks like in production](#fourth-par) The data was always the wall Building a credit model has never really been about the algorithm. It has been about the data you needed before you could start. A PD model wants enough realised defaults to learn from, and defaults accumulate slowly, over years and across a full cycle. For an established mortgage or consumer book, that history exists, but for a new product, a young originator, or an asset class no one has lent into at scale, it does not. You either wait until enough borrowers default to model the ones who will, which is a strange thing to have to wait for, or you do not underwrite the segment at all. That was the wall. Not which gradient-boosted tree won, but whether you had enough matured cohorts to build anything a risk committee would sign. It is why the newest and fastest-growing corners of asset-based finance have been the hardest to model, and why capital has stayed on the sidelines of the very asset classes where borrower demand is growing quickest. Tabular foundation models move that wall. A model pre-trained on millions of datasets arrives already knowing what tabular structure looks like, so it needs far less of your own history to produce a calibrated score, and it produces it in minutes instead of months. Once the data requirement falls that far, the question changes. It stops being “do we have enough defaults to model this” and becomes “which data is worth acquiring, and what can we now underwrite.” The model turns into the commodity. The context becomes the product: the data you feed the model, the signals you engineer into it, the asset classes you can bring into scope, and the governance that lets a committee trust what comes out. We have spent years building that layer for asset-based finance, and it is already in production. What actually changed A tabular foundation model like TabPFN is a transformer pre-trained on millions of synthetic tabular datasets. It has already learned what tabular structure looks like, so it does not learn your problem by training on it. It reads your labelled rows as context, the way a language model reads a prompt, and predicts the rest in a single forward pass. No gradient descent on your data, no hyperparameter search, no retraining when the tape updates. Two consequences matter for credit. These models are strongest exactly where conventional machine learning is weakest, on small datasets, which is what most asset-based portfolios are. And they return calibrated probabilities without the separate calibration step a tree-based PD model usually needs, which matters when the output feeds a regulatory risk classification or a capital calculation rather than just a ranking. The performance is no longer something you take on faith. On TabArena, the public benchmark that now anchors the field, the top single models are all tabular foundation models. The best tuned gradient-boosted tree sits well outside that leading group, with the first tree appearing only around seventh place. A few years ago a tuned GBDT was the undisputed default. That is a fast inversion. The limits are just as real, and worth stating plainly, so we read like people who deploy these rather than people selling them. The current generation is built for small-to-medium data, roughly tens of thousands of rows, though the newest models are pushing that ceiling and some now reach far larger sets. Benchmark rankings shift with evaluation protocol and ensembling, not architecture alone. And a transformer pre-trained on synthetic data is harder to defend in front of a model-risk committee than a decade-proven XGBoost. We use them as a fast experimentation layer and inside ensembles, and we do the validation work rather than assume it away. Cheap models change what you can underwrite The speed is easy to file under convenience, a shortcut that trims a data-science sprint, and that reading misses what it actually does. When fitting and evaluating a candidate model falls from a multi-month project to something you can run in an afternoon, the economics of the decision above change. A data source that could never have justified six months of engineering is suddenly worth a one-week pilot, and an asset class that never had the default history to support a model of its own becomes something you can price. That matters most in the places conventional modelling has always struggled. BNPL portfolios, revenue-based finance, public-administration receivables, solar lease pools, these are young asset classes with thin files and short histories that have never run through a full downturn, the segments where a gradient-boosted tree has too little to learn from and where, until recently, the honest answer to “can we model this” was usually no. They are also, not by coincidence, among the fastest-growing corners of asset-based finance, which puts the modelling gap directly on top of where the market is expanding. A foundation model changes that answer without loosening your risk discipline. You still validate it, still stress it against a downturn, still defend the thing to a committee, but you reach that conversation in days instead of after a year of accumulating losses to learn from. The consequence is strategic rather than technical. The set of deals a firm can credibly underwrite widens, and it widens toward exactly the segments that were off-limits because they were new or too hard to proxy. If the model is the commodity, the edge is the context If a pre-trained model gives everyone the same fast, calibrated baseline, then the baseline is not where anyone’s advantage lives, and everything shifts to what you feed the model and how you shape it. This is the part that “just download TabPFN” quietly skips. A foundation model hands you a score the moment you point it at data, but the quality of that score is decided almost entirely by the inputs, and the inputs are where the real work has always been. For us that work has two sides. The first is knowing which signals move risk in a given asset class and getting them into a state a model can use, and it rarely involves the exotic data people imagine. It runs through cash flows from bank accounts and payment processors, which have lifted predictive accuracy by more than 40% in some consumer-credit applications, and the operational data already sitting inside origination and servicing systems: how long underwriting took, how often a borrower called collections, the seasonality of repayment. It also runs through the context that never reaches the loan tape, and this is where the map redraws itself with every book. For receivables it is obligor payment behaviour, for revenue-based finance it is platform revenue, for solar it is production readings, and the macroeconomic and sector layer that frames all of them, national statistics, central-bank series, industry indicators, looks different in every market you operate in. We have built that mapping across a range of asset classes and in markets whose data has almost nothing in common, from US consumer credit to EU SME lending, and the skill that carries over is not any single dataset but knowing which sources hold signal for a given market and asset class and where to pull them. That is years of accumulated work, not a weekend of API calls. The second side is less visible and much harder to copy, and it is knowing what a tabular foundation model responds to. These are not gradient-boosted trees with a friendlier interface. The feature engineering that helps them differs from what helps a GBDT, the handling of categoricals matters, and the choice of what goes into the context window moves the output in ways the parameters governing it only reward if you have spent real time with the internals rather than treating the model as a black box that either works or doesn’t. Most teams reaching for these models are meeting them for the first time. We are not, and the distance between a foundation model used naively and one used well is wide enough to be the whole difference between a score a committee signs and one it sends back. What this looks like in production An argument like this is only worth anything if it runs on a real portfolio. For us it already does, in production on client books today. You hand over a loan tape, even a thin one, and instead of the collect-clean-validate-train cycle we bring the context, the outside signals and the feature engineering behind them, and hand back a scored portfolio in a single pass, with no model to train on your side. The build follows the claim this article makes: the model is the commodity, the context is the product. One pre-trained base, context assembled per asset class, each book scored on its own terms rather than forced through a generic template. A score on its own is not enough to act on. Every prediction breaks into named, quantified drivers a credit committee and an auditor can sign off, rather than a number they are asked to trust. Because those drivers include the alternative signals we engineer in, the explanation shows not just that a loan looks risky but what in the wider data made it so. Every step is logged. The model that clears an investment committee is the same one that survives the audit and the model-risk review that follow, because the trail from input to score is there to walk. The model is only the starting point. The real advantage comes from the context around your data. See how Cardo AI helps you underwrite new asset classes with explainable, audit-ready AI. [Book a demo](https://trust.cardoai.com/). --- ### [Cardo AI achieves SOC2 Type 2 certification](https://cardoai.com/article/cardo-ai-achieves-soc2-type-2-certification/) **Published:** March 19, 2024 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#introduction) [What is SOC2 certification?](#first-par) [Why SOC2 certification matter to our clients and partners?](#second-par) Introduction In an era where digital innovation is at the heart of financial services, the security of client data and operational systems is very important. Cardo AI is proud to announce that we have secured both **SOC2 Type 1** and **SOC2 Type 2 certifications** from [Thoropass](https://thoropass.com/). These certifications validate our ongoing efforts to uphold robust security measures, system design, and operational effectiveness, reflecting our commitment to safeguarding client data. What is SOC2 certification? Service Organization Control 2 (SOC2) is an auditing procedure that ensures service providers securely manage your data to protect the interests of your organization. [SOC2](https://www.aicpa-cima.com/resources/download/soc-for-service-organizations-engagements-overview) is designed for tech companies, especially those storing customer information in the cloud, ensuring that they follow strict information security policies and procedures. **SOC2 Type 1 Certification**: This certification assesses the design of security processes at a specific point in time. For Cardo AI, obtaining the SOC2 Type 1 certification means that an independent auditor has verified that our security controls are appropriately designed and implemented to protect clients’ data according to the Trust Services Criteria (TSC). This is especially critical for clients in the asset-based finance market, reassuring them that their sensitive financial data is handled with the utmost care. **SOC2 Type 2 Certification**: The SOC2 Type 2 certification goes a step further by evaluating the operational effectiveness of these controls over a defined period, usually no less than six months. This means that Cardo AI’s security measures are well-designed, consistently applied, and effective over time. This certification provides an additional layer of confidence for clients and partners, knowing that Cardo AI’s commitment to security is not just theoretical but is proven in practice. Why SOC2 certification matter to our clients and partners? 1. **Trust and transparency**: SOC2 certification serves as a testament to our transparency and integrity in managing your data. You can trust that we adhere to industry-leading practices and standards, providing you with peace of mind and confidence in our services. 2. **Continuous improvement**: Our journey to SOC2 compliance doesn’t end with certification. We are committed to continuously enhancing our security posture, refining our processes, to ensure that your data remains secure and protected at all times. 3. **Data protection commitment** – The SOC2 certification is a tangible representation of our promise to you – our clients and partners – that we handle your data with the utmost care and dedication to security. 4. **Security & operational integrity** – This achievement signifies that our operational processes are designed and executed with high standards of security in mind. Achieving SOC2 Type 1 and SOC2 Type 2 certifications was a rigorous but rewarding process. It involved a comprehensive review of our security policies, and customer data protection mechanisms. These certifications are not just badges of honor but reflect our ongoing efforts to safeguard client and partner data and ensure our services meet rigorous industry standards. We understand the importance of trust in our client relationships and are committed to upholding this trust through continuous improvement and diligent security practices. See how Cardo AI’s SOC2-certified infrastructure gives you the confidence to move fast without compromising on data protection. [Book a demo ](https://cardoai.com/book-a-demo/) --- ### [Why secure infrastructures matter: in-house vs outsourced solutions](https://cardoai.com/article/why-secure-infrastructures-matter-in-house-vs-outsourced-solutions/) **Published:** June 10, 2024 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#introduction) [Leveraging technology for better security & lower costs](#second-par) [Building trust and protecting data in Asset-Based Finance](#fourth-par) Introduction According to the [IBM Cost of a Data Breach Report 2023](https://www.ibm.com/reports/data-breach), the financial sector (while still slightly less than the previous year) experienced some of the highest costs associated with data breaches in 2023, reflecting the critical need for more stringent security measures in our industry. For the same year, the average cost of a data breach reached a record $4.45 million, marking a 15.3% rise since 2020. This trend underscores the escalating financial stakes in data security, emphasizing the critical need for robust security frameworks. The following graph (Image 1) notes that organizations in critical infrastructure industries, which also includes financial services, bring higher breach costs than those in non-critical industries, primarily due to the complex regulatory requirements and the high value of the data involved. ![](https://cardoai.com/wp-content/uploads/2026/03/image-1.png) *Image 1: Cost of data breach by industry measured in USD millions – IBM 2023* Leveraging technology for better security & lower costs Advanced technologies, including AI and automation, have proven instrumental in reducing detection time, the financial impact, and duration of breaches. According to the same IBM report, organizations utilizing these technologies extensively experienced breach-related costs that were $1.76 million lower on average, and they managed to shorten the breach lifecycle by over three months (108 days) compared to those without such technologies. This points to the efficacy of integrating sophisticated security tools in preemptive threat detection and swift response. *![](https://cardoai.com/wp-content/uploads/2026/03/image-2.png)* *Image 2: Percentage of organizations for each technology usage level, and the according cost of data breaches for each level – IBM 2023* Building trust and protecting data in Asset-Based Finance The asset-based finance market thrives on sensitive financial data. Borrowers entrust asset-based lenders with their financial information. A data breach can compromise client trust and make it difficult to secure future business. Often, smaller asset managers face a key decision: build and maintain their infrastructure, or partner with a specialized provider offering robust security features? In-house solutions could present several challenges: - **Resource strain:** Building and managing a secure infrastructure requires significant upfront investment in technology, tools, and skilled personnel. - **Constant vigilance:** Security threats evolve rapidly. Keeping an in-house team updated on the latest threats and patching vulnerabilities requires continuous training and resource allocation. - **Scalability limitations:** As your data volume grows, scaling an in-house solution becomes complex and expensive. On the other hand, out-sourced solutions could offer numerous benefits: - **Cost-effectiveness:** External providers reduce upfront investment and ongoing maintenance costs. - **Deep security expertise:** External providers usually employ a team of professionals with extensive experience in protecting financial data. This expertise surpasses what most in-house teams can achieve. - **Always evolving:** External providers continuously update their platforms with the latest security patches and features. This ensures your data remains protected against emerging threats. - **Scalability made easy:** External providers offer scalability to accommodate your growth without additional infrastructure investment. In-house or outsourced? See how Cardo AI takes the security burden off your team, without the tradeoffs. [Book a demo](https://cardoai.com/book-a-demo/) --- ### [SRT: Tech solutions for effective risk management](https://cardoai.com/article/srt-tech-solutions-for-effective-risk-management/) **Published:** October 29, 2024 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#introduction) [The risks of SRTs](#first-par) [Essential tools for effective SRT management](#second-par) [Cardo AI: Your partner in SRT risk management](#third-par) Introduction In the October 2024 edition of the[ Global Financial Stability Report,](https://www.imf.org/en/Publications/GFSR/Issues/2024/10/22/global-financial-stability-report-october-2024)[ ](https://www.linkedin.com/feed/)the International Monetary Fund depicts a rather solid global situation, amidst accommodative central bank policy, limited volatility and a resilient banking sector. The report focuses on the potential risks that might impact financial stability in the future, among which Synthetic Risk Transfer transactions (**SRTs**). The risks of SRTs How SRTs, which are used to manage credit risk and lower capital requirements, increase risk for the financial system? SRTs move the credit risks associated with a pool of assets from banks to investors through a financial guarantee or credit-linked notes while keeping the loans on banks’ balance sheets. Banks “buy” credit protection, which reduces regulatory capital charges, “limits loan book concentration, reduces counterparty risk, and, for some US banks, avoids realizing potential mark-to-market losses linked to gyrations in interest rates compared with an outright sale of the loans”. SRT issuance is expected to remain above $200 billion in Europe and more than triple in the United States to surpass $50 billion in 2024. Sellers of credit protection are mainly represented by private credit funds which exceed 60% of the market, followed by pension funds at c. 20%. Certain SRT characteristics could increase risks to financial stability. First, SRTs may elevate interconnectedness and create negative feedback loops during stress. This is evident in the case where banks provide leverage for credit funds to buy credit-linked notes issued by other banks. The report explicitly mentions that “the magnitude of the interconnections is difficult to assess because the market remains opaque, with only a fraction of deals being made public and no centralized repository for data on SRT”. It is therefore key for the health of the SRT market and, consequently, for the whole banking industry that heavily relies on it, to be equipped with proper tools. Essential tools for effective SRT management Of the many operational, risk management and decision-making advantages SRT market participants can derive, the most important ones are: **Look-through analysis in single transactions:** In individual SRT portfolios, look-through technology enables investors and institutions to dissect the composition of underlying assets, examining attributes such as asset type, borrower credit quality, geographic distribution, and sector exposure. This level of detail is essential for several reasons: - Granular risk assessment: This allows for a detailed evaluation of potential credit risks, making it easier to pinpoint vulnerabilities within each loan pool. - Concentration analysis: By understanding asset concentration within a single SRT transaction, institutions can better manage sectoral and borrower exposure, avoiding undue risk in certain areas. - Credit quality monitoring: Look-through analysis helps track shifts in the creditworthiness of underlying borrowers, enabling more dynamic and responsive risk management. **Aggregation across multiple transactions**: Aggregated look-through capabilities are equally vital, as they allow for a holistic view of risk exposure across multiple SRT transactions. This approach is particularly beneficial for addressing systemic risks due to interconnectedness and feedback loops. Through aggregated look-through analysis, institutions can: - Identify cross-portfolio exposures: By analyzing common exposures across multiple transactions, institutions can detect concentration risks tied to specific borrowers, industries, or geographic areas, which might otherwise go unnoticed. - Assess systemic vulnerabilities: Understanding cumulative risk exposure across transactions enables institutions to simulate potential contagion effects and feedback loops, especially during periods of market stress. - Enhance counterparty risk management: Aggregating exposures helps monitor dependencies on specific counterparties or credit protection sellers, reducing counterparty risks that could trigger cascading impacts in a stressed environment. **Data management system**: In an environment where risk dynamics and regulatory demands are constantly evolving, a solid data management system that can *automatically retrieve and enrich data* is essential for effectively managing the risks associated with SRT transactions. A proper data management system empowers institutions to: - Automate data retrieval: Seamlessly connect with multiple data sources and parse data in different formats - Standardize data: Establish a common data dictionary to standardize, enabling easy comparison of credit risk metrics across portfolios, transactions, and counterparties. This comprehensive view of risk exposures and interconnectedness facilitates more accurate risk assessments. - Enrich data: Add valuable context by integrating additional information, such as macroeconomic indicators, borrower credit scores, and sector-specific trends. Enrichment transforms raw data into actionable insights, enhancing visibility into factors that might affect credit quality or portfolio performance. - Streamline compliance and reporting: Regulations surrounding SRT transactions often require frequent reporting of risk exposures, concentrations, and credit quality. An automated data management system simplifies this process, enabling institutions to pull data directly from reliable sources, aggregate it according to regulatory guidelines, and generate standardized reports. This can reduce compliance costs, minimize errors, and improve audit readiness. Cardo AI: Your partner in SRT risk management Cardo AI’s comprehensive platform empowers both buy-side and sell-side participants in the SRT market to effectively manage risks and improve their decision-making process. ##### **Benefits for buy-side investors** - In-depth due diligence: Granular insights into SRT transactions, including underlying assets, counterparty risk, and systemic exposures. - Optimized portfolio management: Advanced analytics to optimize portfolio construction, diversification, and risk management. - Enhanced risk mitigation: Sophisticated risk modeling to assess various scenarios and stress tests. - Better access to leverage: by providing lenders the ability to monitor and keep the risk under control, while providing the right information to the financing banks, investors can increase the amount of leverage they can get access to. ##### **Benefits for banks** - Regulatory compliance: Streamlined reporting and transparency. - Enhanced risk management: Robust tools for credit, market, and operational risk. - Improved decision-making: Data-driven insights for pricing and structuring. - Efficient operations: Automated tasks for cost reduction and efficiency. Replace fragmented reporting with a unified, automated solution. Mitigate your SRT risks and foster market stability with Cardo AI. [Book a demo](https://cardoai.com/book-a-demo/) --- ### [How is Asset-Based Finance contributing to LP portfolio strategies?](https://cardoai.com/article/how-is-asset-based-finance-contributing-to-lp-portfolio-strategies/) **Published:** July 9, 2025 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#introduction) [What is Asset-Based Finance?](#first-par) [What are the credit assets included?](#second-par) [What are the risk-return characteristics of ABF?](#third-par) [How is ABF more attractive than other asset classes?](#fourth-par) [How does ABF contribute to portfolio diversification?](#fifth-par) [Why should multi-asset portfolio strategies include ABF?](#sixth-par) Introduction Asset-Based Finance (ABF) is emerging as a powerful tool for Limited Partners (LPs) in multi-asset portfolio strategies, offering a unique combination of high risk-adjusted returns, structural downside protection, and meaningful diversification benefits. Backed by tangible collateral and characterized by contractual cash flows, ABF instruments have outperformed traditional fixed income in both nominal and risk-adjusted terms – with Sharpe ratios up to 2.1 and historical returns exceeding 7% per annum. Unlike corporate bonds, ABF provides lower correlation to public markets, inflation protection through floating-rate structures, and resilience in economic downturns. What is Asset-Based Finance? Asset-Based Finance (ABF) is the funding of companies through loans or securities that are secured by tangible collateral (residential or commercial property, equipment, aircraft, etc.) rather than by corporate cash flows. Banks have largely retrenched from many of these areas since the 2008 crisis, opening the door for non-bank lenders and private-credit managers. Unlike plain-vanilla corporate loans or bonds, ABF structures typically feature contractual cash flows, amortization, and strong covenants (think pools of mortgages or auto loans), which confer a higher degree of credit support. What are the credit assets included? The ABF universe covers a wide range of consumer and commercial finance products. Key categories include: residential and commercial mortgages (RMBS/CMBS); auto, student, and credit‐card receivables; equipment and trade finance; small-business or SME loans; and various *esoteric* securitizations (e.g. royalties, whole-business financings), CLOs and credit-tenant lease financings. The breadth – from auto‐loan portfolios to aircraft leases to commercial RE debt – means ABF portfolios can be constructed across many sub‐segments and geographies. These credit assets are typically structured with varying levels of seniority and embedded credit enhancements (e.g., tranching, overcollateralization, excess spread), allowing for tailored risk-return profiles. What are the risk-return characteristics of ABF? ABF has demonstrated competitive returns with favorable risk-adjusted performance compared to traditional fixed income: - According to [Apollo](https://www.apollo.com/wealth/insights-news/insights/2023/10/asset-backed-finance-the-next-evolution-of-private-credit), an ABF index returned 7.3% annually over the past 10 years with 2.6% volatility, resulting in a Sharpe ratio of 2.1, outperforming high-yield bonds (4.6% return, 6.6% volatility, Sharpe 0.5) and leveraged loans (4.0%, 5.0%, Sharpe 0.6). - [PIMCO](https://www.pimco.com/us/en/insights/asset-based-finance-quantifying-diversification-benefits-and-return-potential#_ftn2) estimates a diversified ABF allocation delivered an annualized return of 4.7% with a Sharpe ratio of 0.67 over the 2005–2022 period, outperforming U.S. investment grade credit (4.0% return, Sharpe 0.51). - [Böni and Manigart](https://www.tandfonline.com/doi/full/10.1080/0015198X.2022.2092384) report a 9.19% net-of-fees IRR for U.S. private debt funds (1996–2018 vintage), with the median fund returning roughly 1.3× capital. Notably, these private debt funds *outperformed* public markets: on average they beat investment-grade bonds by ~8% and high-yield bonds or the S&P500 by ~6%. How is ABF more attractive than other asset classes? ABF offers a combination of yield and protection that sets it apart from traditional bond or loan markets. Because ABF loans are secured by collateral and often senior-ranking, their loss severity in default is usually lower than for unsecured corporate bonds. As Apollo notes, lending against hard assets and contractual cash flows (instead of a company’s entire enterprise value) allows investors to “capture excess return with downside protection, instant diversification, inflation protection, and low correlation to corporate credit”. ABF also distinguishes itself via floating-rate cash flows: most asset-backed loans pay a spread over LIBOR/treasury, so their coupons rise with rates. This is an important hedge at a time when fixed-rate corporates are vulnerable to rate hikes. Moreover, ABF’s tangible underlying (homes, autos, etc.) often appreciate with inflation, further cushioning real returns. In contrast, fixed-income like IG bonds or leveraged loans are mostly fixed-rate and unsecured, offering less inflation-hedge. These features make ABF an attractive diversifier: it tends to avoid the concentration and illiquidity risks of public loans. Importantly, several analyses find ABF’s risk-adjusted returns exceed those of plain-vanilla corporate credit. For example, PIMCO’s PME analysis shows ABF outperforming high-yield and bank loans on a Sharpe basis. As one white paper notes, over 2000–2024 ABF actually delivered *higher returns and lower volatility* than U.S. HY bonds and broadly syndicated loans (e.g. 7.3% p.a. vs ~4–5% for HY, with much lower volatility). How does ABF contribute to portfolio diversification? ABF offers significant diversification benefits due to its idiosyncratic return sources and low correlations to mainstream asset classes: - Marathon and PIMCO both highlight that ABF exhibits low and declining correlation to high-yield and investment-grade credit (except during periods like the GFC and COVID-19). Evidence: - PIMCO estimates an average correlation of 0.36 with investment grade credit and 0.20 with U.S. equities, compared to 0.74 and 0.62 respectively for high-yield bonds. - According to [Janus Henderson](https://www.janushenderson.com/en-be/advisor/article/case-for-asset-backed-securities/), ABS exhibits low correlations to equities (40%), German Bunds (8%), and global investment grade (48%), enhancing multi-asset portfolio construction - [Marathon Asset Management](https://www.linkedin.com/feed/update/urn:li:activity:7216395702464065538/) reports a correlation between High Yield and Structured Credit of 0.61. - According to the [Suhonen paper](https://rpc.cfainstitute.org/research/financial-analysts-journal/2023/direct-lending-returns), direct lending returns correlation with stock indices ranges between 0.64-0.69 while with HY and L.L. 0.79-0.85 - As Apollo notes, ABF has delivered high Sharpe ratios across sectors while maintaining low correlation to both fixed income and equity markets. - Return attribution: Based on PIMCO simulations, including a 10% allocation to ABF in a traditional 60/40 portfolio increased the portfolio’s Sharpe ratio from 0.45 to 0.58, while reducing volatility by 50bps. - PIMCO’s stress tests show that ABF could experience 45% less drawdown in recessionary shock than traditional credit. Why should multi-asset portfolio strategies include ABF? The inclusion of ABF in LP multi-asset portfolios offers multiple strategic advantages: - Enhanced risk-adjusted returns: Consistently higher Sharpe ratios compared to traditional bonds improve overall portfolio efficiency. - Uncorrelated return streams: ABF’s independence from market beta enhances portfolio diversification. - Structural protections: The bespoke and collateralized nature of ABF transactions improve capital preservation, especially in downturns. - Tailored exposure: Investors can select tranches matching their return objectives and risk appetite (senior for safety, mezzanine for yield). - Persistent alpha: Boni and Manigart paper show that Private Debt Funds outperform by at least 1 percentage point equity, investment grade and high yield benchmarks. - Rapid market expansion: Apollo $20 trillion in addressable market size In conclusion, ABF’s superior risk-adjusted returns, resilience in market downturns, and unique return sources make it a natural fit for sophisticated multi-asset LP strategies focused on stability, diversification, and downside protection. Exploring ABF? See how Cardo AI helps you monitor, track, and stress-test your asset-based allocations. [Book a demo](https://cardoai.com/book-a-demo/) --- ### [Building flexible data connectivity in asset-based finance](https://cardoai.com/article/building-flexible-data-connectivity-in-asset-based-finance/) **Published:** March 26, 2026 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#introduction) [What Cardo AI delivers](#first-par) [Why it matters](#second-par) [Outcomes you can expect](#third-par) Introduction Data management in asset based finance is rarely straightforward. One servicer delivers loan tapes via SFTP with date-stamped filenames that change every month. Another counterparty stores collateral data in an S3 bucket organized by vintage. Internal teams may run core reporting flows through Snowflake or Trino. Across a portfolio of transactions, you end up dealing with multiple delivery methods at once. The traditional answer is manual work: download, rename, upload, reconcile, repeat. It doesn’t scale – and it introduces operational risk in a process that cannot afford uncertainty. Cardo AI replaces that with a retrieval framework that meets each counterparty where they already are and automates everything from that point forward. Cardo AI Data Platform connects to your data sources natively, on your desired schedule, with full control over how data is matched to each transaction. No middleware. No manual downloads. No data wrangling. No month-end “are we sure this is the right file?” reconciliation. What Cardo AI delivers **SFTP: The industry standard, fully automated File transfers remain the backbone of servicer reporting. Cardo AI supports both Cardo-hosted and client/third-party-hosted configurations, so you can work with counterparties without forcing process change. - Cardo-hosted SFTP: we provision and manage the server, generate credentials, and handle encryption so counterparties simply drop files into a designated folder. - Third-party-hosted SFTP: we connect directly using password or SSH key authentication, respecting existing security policies. The platform supports **r**eal-world file handling. Filenames with dates, version strings, and variable elements are handled automatically, so a naming convention change doesn’t break the pipeline. Amazon S3: Cloud-native retrieval with enterprise-grade security** As originators and servicers modernize, more reporting data lives in S3. Cardo AI connects directly to S3 buckets and retrieves files on the same scheduled model as SFTP, with support for folder structures, naming conventions, and encryption. Security is first-class: support includes standard access keys and STS Assume Role – temporary, scoped credentials without sharing long-lived secrets. Snowflake: Direct database connectivity (not just data sharing) Snowflake Data Sharing can be clean in theory, but it comes with practical limits: both parties need to be on Snowflake, retrieval-time filtering/transforms can be constrained, and consumers are exposed to a live view that can change without notice. Cardo AI’s approach is different: we connect directly to the counterparty database and retrieve data by executing structured queries—selecting the specific fields needed, applying granular filters (portfolio/date/loan status), and pulling only what’s relevant to each transaction. This enables: - Right-time retrieval in the reporting cycle, not continuous exposure through a live share. - Granular scoping so you don’t pull entire warehouse tables when you only need a slice. - Reference-date alignment so Snowflake extracts are automatically matched to the correct reporting period – consistent with SFTP/S3 sync logic. Trino: SQL access to distributed data via JDBC** For teams running Trino, Cardo AI connects via JDBC and treats Trino as a first-class data source alongside Snowflake and relational databases – retrieving data with the same field-level selection, filtering logic, and scheduling, without extra exports or data movement. **Data Protection and Privacy: GDPR and US compliance built in** Data tapes often carry personally identifiable information (PII) — borrower names, addresses, social security numbers, and other identifiers embedded in loan tapes and collateral files. Cardo AI addresses this directly: the platform can automatically strip, mask, or pseudonymise identifiers at the field level, in line with GDPR requirements and US privacy frameworks including CCPA and the Gramm-Leach-Bliley Act. This means downstream analytics, reporting, and data sharing workflows operate on de-identified datasets by default, reducing regulatory exposure and simplifying data retention obligations without sacrificing the operational data needed to run each transaction. Why it matters In asset based finance, “getting the data” is not a commodity problem. A failed synchronization becomes a delayed report. An incorrectly dated dataset becomes a compliance risk. And the operational consequences compound quickly across transactions and reporting cycles. Cardo AI’s connectivity layer is designed to remove the manual touchpoints that create these failure modes – while respecting institutional security and governance requirements. When you connect a transaction in Cardo AI Data Platform, you establish a structured, auditable, automated data pipeline that runs on your schedule, produces correctly dated outputs, and holds up when counterparties change filenames. Outcomes you can expect - Reduced operational risk from manual handling, missed files, and fragile naming conventions. - Faster reporting cycles with scheduled, automated retrieval across counterparties and protocols. - Cleaner governance: scoped access, encryption, and auditable, repeatable data pipelines. - Consistent period matching via reference-date logic across SFTP, S3, and Snowflake retrievals. - Built-in privacy compliance: automatic PII removal and de-identification to meet GDPR and US regulatory obligations. Modernize data retrieval without changing how your counterparties work. Connect your sources, map them to each transaction, and let Cardo AI run the pipeline – securely, on schedule, and audit-ready, every time, without extra work! [Book a demo](https://trust.cardoai.com/). --- ### [Underpriced risk, oversized returns: The investment case for emerging market consumer loans](https://cardoai.com/article/underpriced-risk-oversized-returns-the-investment-case-for-emerging-market-consumer-loans/) **Published:** July 29, 2025 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#introduction) [Key highlights](#first-par) [Investment appeal and securitization volume trends](#second-par) [Emerging market opportunities in Consumer Loan portfolios](#fifth-par) Introduction The consumer loan securitization market has undergone a notable shift in 2024, with foreign portfolios – those outside the United States – almost equaling domestic portfolios in total issuance volume for the first time (see Graph 1). While much of this growth has been concentrated in developed markets such as the United Kingdom, Australia, and Canada, the evolving credit landscape in emerging markets presents a timely and underexplored opportunity for structured finance participants. Emerging markets – specifically in Central and Eastern Europe – are now demonstrating both competitive returns and improving credit performance. Jurisdictions such as Estonia and Latvia are producing portfolios with higher nominal yields and robust performance metrics, including significantly lower default rates than commonly perceived. These characteristics position them as high-potential additions to diversified consumer credit strategies, especially amid persistently high global interest rates. In this context, Cardo AI’s platform provides institutional investors, originators, and asset managers with an integrated infrastructure to monitor, benchmark, and track the real-time performance of these portfolios across jurisdictions. The data and analytics engine capture granular credit behavior, enabling better pricing, structuring, and secondary trading decisions. Key highlights - **Rising foreign issuance**: As of 2024, securitization volumes from foreign consumer are virtually the same to those backed by U.S. originators, driven by investor demand for diversification and improved risk-return profiles. - **Emerging market advantage**: Estonia and Latvia, in particular, exhibit default rates of 1.9% and 4.9%, respectively, on high-interest unsecured consumer loans (defined as >15% APR) with maturities over 3 years—outperforming U.S. portfolios on a risk-adjusted basis. - **Superior yields**: These emerging market portfolios deliver average interest rates between 15–16%, translating to a yield premium of 200–300 bps over comparable U.S. consumer loans. This premium exists despite lower default rates, highlighting a mispricing of credit risk by global investors. Investment appeal and securitization volume trends As illustrated in Figure 1, investor appetite for foreign consumer loan portfolios has grown significantly, with 2024 marking a pivotal shift: total securitization volumes from non-U.S. consumer portfolios is virtually the same to domestically originated portfolios for the first time. The majority of these foreign portfolios are sourced from jurisdictions including Europe, the United Kingdom, China, Hong Kong, New Zealand, Australia, and Canada. Notably, the final segment of Figure 1 highlights a substantial spread differential between securitization notes backed by foreign versus U.S. consumer loans. Spreads on notes linked to foreign-originated portfolios were approximately three to four times lower than those tied to U.S.-originated consumer loans. This suggests that investors currently perceive foreign originators as presenting a more favorable risk-return profile, potentially due to stronger underwriting practices, regulatory frameworks, or macroeconomic resilience in those markets. ![](https://cardoai.com/wp-content/uploads/2025/07/sec-volume-1024x603.png) *Graph 1: US and non-US Securitization volumes & Spreads (numbers in million $): Q1 2019-Q4 2024* *![](https://cardoai.com/wp-content/uploads/2025/07/weighted-1024x608.png)* *Graph 2: Weighted Average Spreads:Q1 2019 – Q2 2025 Source: [Green Street Advisors](https://www.greenstreet.com/) DB, Cardo AI computations* Emerging market opportunities in Consumer Loan portfolios While the majority of consumer loan securitizations in 2024 originated in developed markets, emerging markets are increasingly presenting compelling opportunities for institutional investors seeking enhanced risk-adjusted returns. As illustrated in Graph 2, jurisdictions such as Estonia and Latvia offer particularly attractive profiles. As of December 2024, portfolios in these markets exhibited relatively low default rates—measured by loans 90 days past due \[1\], at 1.9% and 4.9%, respectively. These figures compare favorably to default metrics observed in U.S. consumer loan portfolios \[2\], particularly when adjusted for portfolio risk. Moreover, the yield potential in these markets is notable. According to Cardo AI data, average interest rates on consumer loan portfolios in Estonia and Latvia hover around 15%–16%, representing a 200–300 basis point premium over comparable U.S. portfolios \[3\]. This combination of lower observed default rates and elevated yields underscores the strategic relevance of selecting emerging markets in consumer credit portfolio allocation, particularly in a high-rate environment where capital efficiency is increasingly critical. \[1\] >15% \[2\] 5.63%, according to New York Fed in Q4 2024: https://www.newyorkfed.org/newsevents/news/research/2025/20250213 \[3\] 12.32%, according to the [Federal Reserve](https://www.fool.com/money/research/personal-loan-statistics/) *![](https://cardoai.com/wp-content/uploads/2025/07/def-rate-1024x608.png)* *Graph 3: Dynamic Default Rate of Emerging Markets High Interest Rate (>15%) 3+ year Consumer Loans: 2023-2025* See how Cardo AI helps institutional investors monitor and structure consumer loan portfolios. [Book a demo](https://cardoai.com/book-a-demo/) --- ### [Cardo AI named “Alternative Data Vendor of the Year”](https://cardoai.com/article/cardo-ai-named-alternative-data-vendor-of-the-year-by-the-risk-markets-technology-awards-2026-2/) **Published:** November 26, 2025 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#introduction) [What is this award about?](#first-par) [Why Cardo AI](#second-par) Introduction Cardo AI, a leading global fintech in asset-based finance and private credit, has been awarded **“Alternative Data Vendor of the year”** at the [Risk Markets Technology Awards 2026](https://www.risk.net/awards/7962650/markets-technology-awards-2026-march-of-the-riskbots). This recognition affirms our leadership in turning complex data into insights that help investors and risk teams make faster, and more confident decisions. What is this award about? This award recognizes the tech vendors that are revolutionizing how the financial industry handles risk, data, and technology. At the heart of our recent innovation is a Risk Modeling Framework powered by AI and machine learning, designed specifically for the early detection and intervention on loans that are still performing but show signs of rising credit risk. Why Cardo AI What sets our solution apart is its deep integration of comprehensive alternative data. It leverages a vast array of variables, ranging from macro-economic signals to real-time market data, providing a significantly broader and more dynamic view of borrower behavior. These data inputs undergo advanced transformations and complex analysis, resulting in a highly enriched dataset that reveals early warning signals long before traditional financial indicators would flag a problem. This framework reshapes how financial institutions monitor and manage credit risk by anticipating deterioration before it occurs, enabling proactive measures to prevent these loans from slipping into Non-Performing status. Explore our award-winning platform today. [Book a demo](https://cardoai.com/book-a-demo/) --- ### [A new era of performance: Exploring asynchronous web development in Django](https://cardoai.com/article/a-new-era-of-performance-exploring-asynchronous-web-development-in-django-new/) **Published:** May 17, 2023 **Author:** GiadaCardo26 **Content:** Contents [Introduction](#introduction) [New in Django](#first-par) [Django Ninja – Fast Django REST Framework](#second-par) [Django Ninja – Extra](#third-par) [Setting up your project](#fourth-par) [Setting up Django-Ninja-Extra](#fifth-par) [Working with Data in Django-Ninja using Pydantic](#sixth-par) [Testing the Book Controller](#seventh-par) [Conclusion](#eighth-par) Introduction In the world of web development, performance is everything. As more users flock to web applications, the demand for fast and efficient APIs has grown as well. One way to achieve this is through asynchronous programming for web development, which can handle multiple requests simultaneously and improve overall performance. In this article, we’ll explore how to use asynchronous web development in Django to build high-performance APIs. Specifically, we’ll be using two powerful libraries — Django-Ninja and Django-Ninja-Extra — to help us achieve our goals. Django-Ninja is a popular web framework that allows developers to quickly build APIs with minimal code. It’s built on top of Django and supports asynchronous views, which makes it a great choice for building high-performance APIs. Django-Ninja-Extra, on the other hand, is a powerful library that extends Django-Ninja’s functionality by adding support for input and output validation, automatic API documentation generation, and more. With Django-Ninja-Extra, you can build even more robust APIs with less code. New in Django There are some exciting new features that allow developers to take advantage of asynchronous web development. One of the most significant additions is the ability to run ORM queries asynchronously. With some exceptions, Django can now run ORM queries asynchronously, which means that developers can take advantage of the performance benefits of asynchronous programming while still using the powerful Django ORM. For example, you can now use an async for loop to iterate over a queryset and await database operations: ``` async for author in Author.objects.filter(name__startswith="A"): book = await author.books.afirst() ``` In addition to queryset methods, Django also supports some asynchronous model methods that use the database. For example: ``` async def make_book(*args, **kwargs): book = Book(...) await book.asave(using="secondary") async def make_book_with_tags(tags, *args, **kwargs): book = await Book.objects.acreate(...) await book.tags.aset(tags) ``` It’s worth noting that transactions do not yet work in async mode. If you need transaction behavior, it’s recommended to write that piece of code as a single synchronous function and call it using *sync\_to\_async()*. These new features in Django 4.1 or newer allow developers to build faster and more efficient APIs using asynchronous programming for web development. In combination with powerful libraries like Django-Ninja and Django-Ninja-Extra, developers can take their Django API development to the next level. In the following sections, we’ll dive into how you can use these new features to build high-performance APIs. Django Ninja – Fast Django REST Framework Django Ninja is a web framework for building APIs with Django and Python 3.6+ type hints.anges Django Ninja – Extra The [Django Ninja Extra](https://github.com/eadwinCode/django-ninja-extra) package offers a convenient, class-based approach to quickly building and configuring high-performance APIs. Utilizing the core features of [Django Ninja](https://django-ninja.rest-framework.com/), it allows for speedy development without sacrificing performance. Setting up your project Before we start building our API, we need to set up our project. Here are the steps to create a Django project and start an app: 1\. **To install Django, you can use pip:** ``` pip install django ``` **2. To create a new Django project named “project”, you can run the following command:** ``` django-admin startproject project ``` **3. To start a new Django app named “demo\_app”, you can run the following command:** ``` python manage.py startapp demo_app ``` Now you have a new Django project named “project” and a new app named “demo\_app” that you can use to start building your API using Django-Ninja and Django-Ninja-Extra. Setting up Django-Ninja-Extra 1\. To use Django-Ninja-Extra, you will need to install it first. You can install it using pip: ``` pip install django-ninja-extra ``` **2. After installation, add** *`ninja_extra`* **to your** `INSTALLED_APPS` ``` ### project/settings.py INSTALLED_APPS = [ ..., 'ninja_extra', ] ``` 3\. **In your django project next to** `urls.py` **create new** `api.py` **file:** ``` ### project/api.py from ninja_extra import NinjaExtraAPI api = NinjaExtraAPI( version="1.0.0", title="Project API", ) ``` **4. Now go to** `urls.py` **and add the following:** ``` ### project/urls.py from django.contrib import admin from django.urls import path from project.api import api urlpatterns = [ path('admin/', admin.site.urls), path('api/', api.urls), # List[Book]: return [book async for book in Book.objects.all()] ``` We are defining a Django Ninja Extra controller class, `BookController`, which has one GET route that returns a list of all books in the database. The controller is defined using the `api_controller` decorator from Django Ninja Extra. The `api_controller` decorator is used to define a class-based controller in Django Ninja Extra. It takes several arguments to configure the routes and functionality of the controller. In this case, we are providing the `prefix_or_class` argument as an empty string, which means that this controller’s routes will not be grouped under any prefix. We are also providing the `tags` argument as `["Books"]`, which will be used for OpenAPI documentation purposes. Inside the `BookController` class, we define the `get_books` method, which is decorated with the `route.get` decorator. This method returns a list of all books in the database by using the `async for` syntax to asynchronously iterate over all books returned by the `Book.objects.all()` query. The `BookController` class also imports the `List` type from the `typing` module and the `Body` class from the `ninja.params` module. Additionally, it imports the `Book` and `Tag` models from the `demo_app.models` module, and the `BookSchema` and `BookCreateSchema` schemas from the `demo_app.schemas` module. Using a class-based controller in Django Ninja Extra allows you to encapsulate related functionality and routes in a single class, which can be useful for organizing your codebase. By using the `api_controller` decorator, you can easily configure the routes and functionality of your controller. **3. In** `project/api.py` **register the controller:** ``` ### project/api.py from ninja_extra import NinjaExtraAPI from demo_app.api import BookController api = NinjaExtraAPI( version="1.0.0", title="Project API", ) api.register_controllers(BookController) # List[Book]: return [book async for book in Book.objects.all()] @route.post("books/", response={201: BookSchema}) # Tuple[int, Book]: book = await Book.objects.acreate(title=payload.title) tags = [await Tag.objects.acreate(name=name) for name in payload.tags] await book.tags.aset(tags) return 201, book ``` The `create_book` function is a POST endpoint that creates a new book and its associated tags. It takes in a `payload` parameter, which is of type `BookCreateSchema` and represents the book data to be created. The `response` parameter specifies that the function returns an HTTP status code of 201 and the `BookSchema` representing the created book. Within the function, the `Book` object is created using the `acreate` method, which asynchronously creates a new instance of `Book` and saves it to the database. The `tags` variable is then created as a list comprehension that creates a new `Tag` object for each tag name in the `payload`. The `aset` method is then used to set the tags of the book to the newly created `Tag` objects. Finally, the function returns a tuple containing the HTTP status code 201 and the created `Book` object. The HTTP status code is specified as the first element of the tuple and the book object as the second element. By using the `Body` parameter and specifying the `description` argument, we provide documentation to the users of the API, explaining what data the API endpoint expects. This makes it easier for developers to consume the API and ensures that the data sent to the endpoint is in the correct format. Overall, the `create_book` function is a useful addition to the `BookController` class, as it provides the functionality for users to create a new book with its associated tags using a simple HTTP POST request. **5. New POST route** In addition to the GET route we created earlier, we’ll also create a POST route that allows us to create new books: ``` ### demo_app/api.py from typing import List, Tuple from ninja.params import Body from ninja_extra import api_controller, route from demo_app.models import Book, Tag from demo_app.schemas import BookCreateSchema, BookSchema @api_controller("", tags=["Books"]) class BookController: @route.get("books/", response={200: List[BookSchema]}) async def get_books( self, ) -> List[Book]: return [book async for book in Book.objects.all()] @route.post("books/", response={201: BookSchema}) # Tuple[int, Book]: book = await Book.objects.acreate(title=payload.title) tags = [await Tag.objects.acreate(name=name) for name in payload.tags] await book.tags.aset(tags) return 201, book ``` The `create_book` function is a POST endpoint that creates a new book and its associated tags. It takes in a `payload` parameter, which is of type `BookCreateSchema` and represents the book data to be created. The `response` parameter specifies that the function returns an HTTP status code of 201 and the `BookSchema` representing the created book. Within the function, the `Book` object is created using the `acreate` method, which asynchronously creates a new instance of `Book` and saves it to the database. The `tags` variable is then created as a list comprehension that creates a new `Tag` object for each tag name in the `payload`. The `aset` method is then used to set the tags of the book to the newly created `Tag` objects. Finally, the function returns a tuple containing the HTTP status code 201 and the created `Book` object. The HTTP status code is specified as the first element of the tuple and the book object as the second element. By using the `Body` parameter and specifying the `description` argument, we provide documentation to the users of the API, explaining what data the API endpoint expects. This makes it easier for developers to consume the API and ensures that the data sent to the endpoint is in the correct format. Overall, the `create_book` function is a useful addition to the `BookController` class, as it provides the functionality for users to create a new book with its associated tags using a simple HTTP POST request. **6. Navigate to the docs:** Testing the Book Controller To test our application, we’ll need a few additional packages: - `pytest`: a testing framework for Python that makes it easy to write and run tests. - `pytest-django`: a plugin for pytest that provides support for testing Django applications. - `pytest-asyncio`: a plugin for pytest that provides support for testing asynchronous code using Python’s `asyncio` library. You can install these packages using pip: ``` pip install pytest pytest-django pytest-asyncio ``` **Starting with application testing!** When writing tests for our Django application, it’s important to have the proper requirements and configuration to support asynchronous testing. Here’s an example of how to set up the `pyproject.toml` configuration file to enable this support: ``` # project_base_directory/pyproject.toml ``` \[tool.pytest.ini\_options\] minversion = “7.0” addopts = “-ra -q –no-migrations –reuse-db –log-cli-level=INFO” # <– add your options here testpaths = \[ “tests”, \] asyncio\_mode = “auto” # <– enable asyncio support python\_files = “tests.py test\_\*.py \*\_tests.py” DJANGO\_SETTINGS\_MODULE = “project.settings” # <– define your settings module here| **Let’s break down what each of these parameters do:** - `minversion`: This sets the minimum version of pytest required to run the tests. - `addopts`: This allows us to add additional options to the pytest command. In this case, we’re adding options to display a summary of test results (`-r`), run tests quietly (`-q`), skip migrations (`--no-migrations`), reuse the database (`--reuse-db`), and set the logging level to `INFO` (`--log-cli-level=INFO`). - `testpaths`: This specifies the directories that pytest should search for tests. - `asyncio_mode`: This enables asyncio support for pytest. - `python_files`: This specifies the file patterns that pytest should look for when searching for tests. - `DJANGO_SETTINGS_MODULE`: This sets the name of the settings module for the Django application. By configuring our `pyproject.toml` file with these options, we can ensure that our Django application is properly set up for running asynchronous tests with pytest. To start testing our application, we need to create some test files. First, we need to create a test directory inside our `demo_app`directory. Then, we’ll create a Python package named `tests`inside the test directory. #### **On Linux or macOS, we can do this using the following command in the terminal:** ``` $ cd demo_app $ mkdir tests $ touch tests/__init__.py ``` **On Windows, we can use the following commands in the command prompt:** ``` cd demo_app mkdir tests New-Item tests\__init__.py -ItemType File ``` Next, we need to create two files inside the test directory: a `conftest.py` file and a `test_api.py` file. The `conftest.py` file will contain some fixtures that we will use in our tests. The `test_api.py` file will contain our actual tests. The `conftest.py` file is used to define fixtures that can be shared across multiple test files. *Pytest* will discover it automatically. We’ll create a fixture for creating a test client that we can use to make requests to our API. Add the following code to `conftest.py`: ``` # demo_app/tests/conftest.py import asyncio import pytest from demo_app.models import Book @pytest.fixture(autouse=True) def enable_db_access_for_all_tests(db): """ This fixture enables database access for all tests. """ pass @pytest.fixture(scope="session") def event_loop(): """ This fixture handles the event loop for all tests. """ loop = asyncio.get_event_loop_policy().new_event_loop() yield loop loop.close() @pytest.fixture async def books(): """ This fixture creates two books and returns them. """ await Book.objects.acreate(title="test") await Book.objects.acreate(title="test2") return [book async for book in Book.objects.prefetch_related("tags").all()] ``` Now, let’s create our `test_api.py` file. In this file, we will write tests for the *`BookController`* we created earlier. Add the following code to `test_api.py`: ``` # demo_app/tests/test_api.py import pytest from ninja_extra.testing import TestAsyncClient from demo_app.api import BookController from demo_app.schemas import BookSchema pytestmark = pytest.mark.asyncio class TestBookController: @pytest.fixture(autouse=True) def setup(self, books): self.books = books self.client = TestAsyncClient(BookController) async def test_get(self): response = await self.client.get("/books/") assert response.status_code == 200 assert response.json() == [ BookSchema.from_orm(book).dict() for book in self.books ] ``` The *`TestBookController`* class is responsible for testing the *`BookController`* class we defined earlier. This test class includes a *`setup`* fixture that initializes a *`TestAsyncClient`* instance and populates the `self.books` attribute with a list of *`Book`* objects. The `test_get` method sends a GET request to the `/books/` endpoint using the *`TestAsyncClient`* instance. It then asserts that the response status code is `200` and that the JSON response body contains a list of dictionaries representing the books in the database. This is achieved by converting each *`Book`* object in `self.books` to a *`dict`* using the `BookSchema.from_orm()` method. By setting *`pytestmark`* to `pytest.mark.asyncio`, pytest is instructed to run the tests asynchronously, which is necessary since the *`get`* method in `TestAsyncClient` is itself an async function. **Now we are ready to run our tests**. Simply run *`pytest`* from the command line inside the base directory. This will automatically discover and run all the tests in the *`tests`* directory. ``` $ pytest ``` ![](https://i0.wp.com/miro.medium.com/v2/resize:fit:875/1*Jn0Y2Syl76QufygituhJTA.png?ssl=1)In the next part of this series, we’ll explore more advanced testing techniques and write tests for more complex scenarios. Conclusion Django Ninja and Django Ninja Extra are powerful asynchronous web development tools that can help you rapidly build high-performance APIs using Django and Python. In this article, we’ve covered some basic concepts like serialization, validation, and routing, as well as more advanced techniques like using async ORM to improve performance. So stay tuned for the next part of the asynchronous web development series, and in the meantime, start building your own high-performance APIs with Django Ninja and Django Ninja Extra! **References:** - Django Ninja: - Django Ninja Extra: - Project repository: --- ### [Diversification benefits of multi‑sector Direct Lending portfolios](https://cardoai.com/article/diversification-benefits-of-multi-sector-direct-lending-portfolios/) **Published:** August 21, 2025 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#introduction) [Multi-sector portfolios in Direct Lending](#first-par) [Risk mitigation through diversification](#second-par) [Role in institutional portfolios](#third-par) [Conclusion](#fifth-par) Introduction Direct lending, a private debt strategy where non-bank lenders finance mid-market firms, has expanded rapidly over the past decade. By mid-2023, private credit AUM reached nearly $1.7 trillion, with direct lending accounting for $800 billion, about half the total. In the U.S., it now rivals leveraged loans and high-yield bonds. Institutional investors such as pension funds and insurers have fueled this growth, drawn to the yield premium and diversification benefits of private credit: 1. Amid low rates and bank pullbacks, direct loans offered bespoke structures, floating-rate income, and reduced correlation with traditional markets. 2. U.S. pensions alone held 31% (≈$307 billion) of private credit fund assets by 2021i, underscoring institutional demand. This report explores how multi-sector direct lending portfolios enhance diversification, drawing on U.S. evidence from the last 5–10 years. Multi-sector portfolios in Direct Lending A hallmark of direct lending funds and Business Development Companies (BDCs) is their deliberate diversification across industries. Rather than concentrating in a single niche, most generalist lenders spread portfolios across 20-50 sectors, sometimes more. The Morgan Stanley Direct Lending Fund, for instance, oversaw a $3.8 billion portfolio across 210 companies in 34 industries by early 2025, with leading exposures in software, insurance services, and business services, sectors known for resilience and stable cash flows. This is no accident. U.S. BDCs on average keep less than 10% exposure to cyclical industries like manufacturing or autos, mitigating risks tied to tariffs or supply shocks. Instead, portfolios tilt toward U.S.-focused technology, healthcare, and services, which have historically absorbed external shocks more smoothly. Diversification not only buffers against sector-specific downturns but also widens the deal pipeline, letting managers cherry-pick opportunities. Combined with structural protections like senior secured status, multi-sector diversification remains the cornerstone of private credit risk management. Risk mitigation through diversification For direct lending investors, multi-sector portfolios serve as a built-in hedge against idiosyncratic risk. By spreading loans across dozens of industries, managers ensure that sector-specific downturns remain contained while healthier sectors sustain overall performance. This diversification has proven especially powerful over the past decade. **Stable default experience.** Default rates in direct lending remain consistently lower than in syndicated loans or high-yield bonds. Between 2021 and 2023, trailing defaults hovered around ~1% annually, compared to much higher rates in public junk debt. Even during stress events like the U.S.-China trade war, diversified portfolios helped BDCs sidestep concentrated losses; exposures to tariff-sensitive industries (manufacturing, autos) were kept minimal, while dominant allocations to software and services held steady. **Low non-accruals.** The practical payoff of diversification is remarkably low defaults. For instance, Morgan Stanley’s Direct Lending Fund – with 210 loans across 34 sectors – reported only two loans on non-accrual (0.2% of cost) as of Q1 2025. U.S. BDCs as a whole have kept non-accruals in the low single digits, ensuring that portfolio-level losses remain muted even when individual borrowers falter. **Tail risk reduction.** Diversification also safeguards against extreme downside scenarios. StepStone analysis found that a 200-loan diversified portfolio suffered 2.6 percentage points less loss in a 99th-percentile downturn than a concentrated 25-loan portfolio. Even in moderately adverse cases, diversified funds retained materially stronger returns – proof that spreading exposures cushions both volatility and tail risk. **Cross-sector balance.** Diversification also creates resilience across cycles. During COVID-19, hospitality and retail loans struggled, but technology and healthcare exposures carried portfolios. Similarly, in inflationary environments, energy and commodities offset pressure on other sectors. Ultimately, while senior secured structures and covenants protect individual loans, diversification protects portfolios. For investors, it remains the single most effective way to ensure resilience, preserve income, and mitigate catastrophic losses. Role in institutional portfolios For institutional investors, diversification in direct lending works on two levels: within the loan portfolio itself and across the broader multi-asset allocation. Multi-sector direct lending funds have grown in popularity because they offer one-stop exposure across dozens of industries – an especially attractive solution for smaller pensions, insurers, and endowments entering private credit. **Enhancing the fixed-income sleeve**. Adding direct lending to a traditional bond portfolio introduces floating-rate, higher-yielding loans. These instruments typically deliver 125–200 bps more than broadly syndicated loans, while default rates have remained very low (~1% or less annually in 2021–2023). Crucially, when interest rates rise, direct loans earn more, unlike fixed-rate bonds. Empirical studies show near-zero or slightly negative correlation between private credit returns and a 60/40 stock-bond mix, dampening volatility and ensuring steadier cash flows. **Avoiding concentration risk**. Large pensions often commit to multiple managers – upper middle-market, lower-middle-market, or non-sponsor loans – so that no single strategy dominates. Diversified funds, such as one with 210 loans across 34 industries, reported just 0.2% in non-accruals, underscoring how breadth shields investors. **Consistency through cycles**. During COVID-19, software and healthcare credits offset stressed hospitality loans. In 2022–2023, energy and commodities balanced out margin-pressured sectors. With low single-digit non-accruals and tail-risk reduction of up to 2.6 percentage points in severe scenarios, diversified portfolios have proven resilient. The result: multi-sector direct lending has become a permanent fixture in institutional allocations, offering equity-like returns with lower volatility and a critical source of predictable income. Conclusion Diversification remains the cornerstone of direct lending’s appeal. Over the past decade, multi-sector portfolios have consistently delivered high single-digit to low double-digit yields with minimal volatility and default losses. Even during stress events such as the pandemic, BDCs with dozens of sector exposures kept non-accruals below 1–2%, preserving income streams when other credit assets faltered. Tail-risk models show that a well-diversified portfolio can cut potential losses by up to 50% compared to concentrated exposures, making it one of the most effective ways to stabilize returns in private markets. Looking ahead, “diversification 2.0” is emerging, with managers rotating across sectors and loan types to balance risks more actively. Some are blending unitranche, mezzanine, and asset-based finance to complement traditional corporate lending. Technologies such as Cardo AI, who currently manages $15 billion of Direct Lending securitization deals in their technology, enable this evolution by standardizing data across private debt strategies, loan types, asset types and sectors, enhancing visibility into portfolio exposures and correlations. Through advanced analytics and scenario modeling, platforms like Cardo AI allow managers to proactively adjust allocations and stress-test diversification strategies under various macro and credit conditions. Yet, as Fitch’s 2025 outlook warns, rising rates may modestly lift non-accruals, making prudent structuring and active oversight as critical as ever. For allocators, diversification is not just optional; it is strategic. Notes \[i\] [The Fed – Private Credit: Characteristics and Risks](https://www.federalreserve.gov/econres/notes/feds-notes/private-credit-characteristics-and-risks-20240223.html) \[ii\] [Private Credit’s Surge Has Investors Excited and Regulators Concerned – CFA Institute Enterprising Investor](https://blogs.cfainstitute.org/investor/2025/06/05/private-credits-surge-has-investors-excited-and-regulators-concerned/) \[iii\] [KBRA Affirms Ratings for Morgan Stanley Direct Lending Fund](https://www.kbra.com/publications/wYSYfgyM/kbra-affirms-ratings-for-morgan-stanley-direct-lending-fund) \[iv\] [BDCs Face New Headwinds but Limited Direct Exposure to Tariff Sectors](https://www.fitchratings.com/research/corporate-finance/bdcs-face-new-headwinds-limited-direct-exposure-to-tariff-sectors-24-04-2025) \[v\] [Diversification in Direct Lending](https://www.fitchratings.com/research/corporate-finance/bdcs-face-new-headwinds-limited-direct-exposure-to-tariff-sectors-24-04-2025) \[vi\] [What Role Can Private Credit Play in Individual Investors’ Portfolios? | KKR](https://www.kkr.com/insights/private-credit-allocation) \[vii\] [Investors Should Direct Their Attention to Private Lending – Cambridge Associates](https://www.cambridgeassociates.com/insight/investors-should-direct-their-attention-to-private-lending/) If you’re allocating across unitranche, mezzanine, and asset-based finance, you need a single view of your exposures. See how Cardo AI makes that possible. [Book a demo](https://cardoai.com/book-a-demo/) --- ## Reports ### [From Automation to Autonomous ABF operations](https://cardoai.com/reports/from-automation-to-autonomous-abf/) **Published:** June 4, 2026 **Author:** GiadaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [ABF is booming. But AI agents aren’t ready for it](#first-par) [Key developments](#second-par) [The takeaway](#third-par) [How to operationalize AI-ready collateral data in ABF](#fourth-par) [Software tools for ABF collateral intelligence](#fifth-par) Cardo AI’s white-paper *From Automation to Autonomous ABF operations* examines the gap between the promise of agentic AI in asset-based finance and the data reality that determines whether autonomous workflows can be trusted. Drawing on the U.S. ABF market (auto loans, BNPL, consumer credit, and forward-flow structures), the paper presents a seven-pillar **AI-Ready Collateral Data** framework, a taxonomy of data failure modes and their consequences for AI agents, and three anonymized operational case studies, alongside a supervised-autonomy stack and a buy-side due diligence checklist. ABF is booming. But AI agents aren’t ready for it Asset-based finance has become one of private credit’s defining growth stories, with private lenders now overseeing $6.1 trillion globally (projected to reach $9.2 trillion by 2029) across portfolios backed by auto loans, consumer credit, BNPL receivables, equipment leases, and more. The scale brings extraordinary yields and diversification, but also operational complexity that most firms are still managing with spreadsheets and manual processes. As the industry rushes to deploy agentic AI for surveillance, forecasting, and reporting, a structural bottleneck is emerging: collateral data quality. An AI agent is only as reliable as the data it operates on, and in ABF, that data is rarely clean. Key developments Five issues define the current state of autonomous ABF. First, Gartner predicts that over 40% of agentic AI projects will be cancelled by 2027, primarily due to unclear value and risk-control gaps, a warning that lands squarely on firms deploying agents against ungoverned collateral data. Second, the most common data failure modes (missing fields, definition mismatches, broken loan IDs, timing misalignments, and manual spreadsheet overrides) don’t just produce reporting errors; they cause AI agents to automate those errors at scale. Third, the U.S. consumer credit market has reached a level of granularity ($1.66 trillion in auto loan balances, 54 million BNPL borrowers across six major providers) that makes the absence of standardized, asset-level data a critical risk. Fourth, the SEC’s Reg AB II framework for public ABS illustrates what AI-grade transparency requires; private ABF has no equivalent, and the gap is operational, not just regulatory. Fifth, supervised agentic workflows (where agents handle ingestion, eligibility testing, covenant monitoring, and investor reporting drafts, all under human sign-off) are technically achievable today, but only on top of a governed collateral intelligence layer. The takeaway for ABF managers and LPs Agentic AI in asset-based finance is not a technology problem; it is a data infrastructure problem. The white paper’s central argument is precise: agents operating on ungoverned collateral data are unsafe, while supervised agents operating within a controlled collateral intelligence platform can detect exceptions earlier, standardize recurring workflows, and preserve a clear audit trail between source data and reported outputs. Three anonymized case studies illustrate where the failure actually happens: an auto loan pool where two identically-defined delinquency labels caused a concentration breach to go undetected; a BNPL portfolio where the absence of loan-level IDs hid multi-lender borrower overlap; and a forward-flow facility where missing industry tags caused a covenant check to fail silently. In each case, experienced analysts eventually caught the issue manually. The problem is that this control doesn’t scale. For LPs, the implication is direct: manager selection should include scrutiny of the data layer, not just the credit model. For ABF managers, the blueprint is clear: build the seven data pillars first, deploy agents second. How to operationalize AI-ready collateral data in ABF As the white paper argues, the leap to autonomous ABF operations does not happen by acquiring the most advanced AI models. It happens by building the data infrastructure those models require: automated ingestion pipelines, a standardized collateral data model, entity resolution across originators and servicers, automated quality controls, reconciliation logic tied to borrowing-base and waterfall calculations, an executable rule engine for eligibility and covenant testing, and a full audit trail with end-to-end lineage. Technology platforms that encode this foundation allow AI agents to move from processing data to trusting it, the distinction that separates automation from genuine autonomy. Software tools for ABF collateral intelligence Cardo AI platform: - A dedicated private credit software built specifically to automate collateral data ingestion, normalization, and performance tracking across complex ABF portfolios, replacing manual loan tape downloads and linked spreadsheet workbooks with structured, end-to-end pipelines. - Real-time risk management and covenant monitoring: Rather than relying on lagging indicators or manual reconciliations, the platform continuously applies eligibility rules, concentration limits, and waterfall logic at the individual asset level, flagging exceptions before they reach investors or regulators. - Supervised agentic workflows: The platform supports AI agents for collateral ingestion, eligibility testing, borrowing-base calculation, performance surveillance, and investor report drafting, all operating under human review within a governed data environment with full audit trail and explainability. To learn how Cardo AI can help you centralize your data, track performance, and automate compliance for your ABF portfolio, you can **[request a live platform demo of Cardo AI.](https://cardoai.com/book-a-demo/?utm_source=bing_ai_breadcrumbs)** --- ### [Liquidity stress testing for ABF. How does a Liquidity structure get stressed and what are the implications?](https://cardoai.com/reports/liquidity-stress-testing-for-abf/) **Published:** May 12, 2026 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#introduction) [The evergreen paradox: when 5% is the ceiling, not the floor](#first-par) [What LPs should be asking now](#second-par) [The bottom line](#third-par) Introduction In private credit, liquidity stress is rarely a sudden explosion. It’s a slow-motion sequence. The stated liquidity promise is tested first, then the portfolio’s self-funding capacity, then financing headroom, and only afterward the secondary market. In 2026, that sequence is being tested in real time and at scale, as evergreen wrappers meet sustained above-cap redemption demand. The evergreen paradox: when 5% is the ceiling, not the floor The $500 billion evergreen universe has long anchored itself to a 5% quarterly repurchase cap, assuming subscriptions, collections and financing would always absorb redemptions before the cap binds. Q1 2026 tested that directly: - BlackRock HPS: 9.3% requested, 5% paid. - Morgan Stanley’s North Haven: 10.9% requested. - Apollo and Ares: ~11–12% against 5% caps. - Carlyle CTAC: 15.7%. - KKR K-ABF: 7.2%. The wrappers did what they were designed to do: pay up to the cap and queue the rest. That isn’t insolvency; it’s the wrapper working as written. The friction is between *stated* liquidity, what the prospectus promises, and *expected* liquidity, what investors thought they were getting. For ABF, the risk isn’t that assets stop paying, it’s that they don’t pay fast enough to absorb a liability side that has accelerated. What LPs should be asking now “Is this fund safe?” assumes insolvency is the failure mode. In high-equity, low-leverage ABF wrappers, the real failure mode is friction: queue persistence, NAV drag from forced sales, execution delay – none of which shows up in a static monthly view. The questions that do surface it: - If requests stay above the cap for three to four quarters, what does my backlog look like? - How much same-quarter liquidity can my manager mobilize from collections and financing before having to sell? - What happens if collateral slows just as warehouse advance rates compress? ECB, Fed and ESMA are converging on the same questions in 2026, and ESMA’s new LMT guidelines require managers to factor stress-test results into how they calibrate liquidity tools. The bottom line Liquidity in semi-liquid wrappers is best understood as a stack – cash, collections, financing, secondary – each with different speed, certainty and cost. Resilient managers size each layer deliberately and stress-test them jointly. The fragile ones treat liquidity as a single balance and discover the stack only when the cap binds. Our latest research takes each layer apart under public 2026 data – where it cracks, and the diligence questions LPs should be putting to managers now. --- ### [Market Financial Solutions and the ABF ‘collateral-truth’ problem](https://cardoai.com/reports/market-financial-solutions-and-the-abf-collateral-truth-problem/) **Published:** March 30, 2026 **Author:** RobertaCardo26 **Excerpt:** Market Financial Solutions (MFS) collapsed amid allegations of double-pledged collateral, revealing a massive gap between reported loans and verifiable assets. **Content:** Contents [Introduction](#first-par) [A structural problem](#second-par) [Real-time collateral verification](#fourth-par) Introduction The collapse of Market Financial Solutions (MFS), involving an estimated $1.5 billion in contested liabilities, highlights a structural vulnerability in warehouse- funded asset-based finance. The episode represents a significant breakdown in operational accountability, mirroring the [Tricolor collapse](https://cardoai.com/reports/navigating-redemption-pressure-in-private-credit-and-semi-liquid-funds/), which saw $800 million in alleged double-pledged collateral, and the First Brands failure, where $2.3 billion vanished. A structural problem The MFS case highlights fundamental weaknesses in asset-based finance, where oversight relies on fragmented data, borrower reporting, and periodic checks. In a fast-growing private credit market, these “trust, but verify” models fail to detect real-time risks. The result is a system vulnerable to sudden loss of confidence, where uncertainty around collateral can trigger liquidity stress and market disruption. Real-time collateral verification To address these gaps, the paper outlines a shift toward continuous, technology-driven verification. A “verification stack”, combining asset registries, document validation, cash-flow tracking, and real-time monitoring, enables lenders to prove collateral integrity across all facilities. The key takeaway: in modern credit markets, trust must be replaced by continuous verification of “collateral truth“. --- ### [Earned or Financed? BDC Distribution Sustainability Monitor](https://cardoai.com/reports/yearned-or-financed-bdc-distribution-sustainability-monitor/) **Published:** August 13, 2026 **Author:** GiadaCardo26 **Content:** Contents [Key developments](#first-par) [Takeaways](#second-par) Cardo AI’s **BDC Distribution Sustainability Monitor** asks the question no headline yield answers: does a fund earn, in cash, what it pays out? The metric is cash coverage, cash net investment income divided by declared distributions, both taken from SEC filings, where 1.00 means the payout is fully earned. It is computed bottom-up for the non-traded BDC market, where no share price exists to discipline a distribution, and validated against the listed BDC market, where one does. The launch issue covers 72 monitored BDCs, a 34-fund non-traded core, a 10-fund watchlist and a 28-fund listed control, across thirteen quarters of filings, with data through 2026-Q1. The central finding is a split. Listed BDCs cover their payouts at par, with median cash coverage of 0.99. The non-traded core does not, at a median of 0.77, and 16 of 32 rated funds sit below 0.80. Matching on maturity widens the gap rather than narrowing it, so the non-traded market as a whole pays out more cash than it currently earns. Key developments 1. Coverage warns before the cut arrives, since funds covering less than 0.92 of their payout went on to reduce it 63% of the time within four quarters, against 40% for funds that covered. 2. Among listed funds the signal is unambiguous, where four spent time below 0.50 coverage and all four cut. 3. For non-traded funds the pressure shows up in the redemption queue instead, with repurchase requests at 1.7% of NAV per quarter after low-coverage quarters against 0.7% after covered ones. 4. Yield sorts nothing, because the correlation between coverage and payout is minus 0.71, so the weakest coverage sits almost without exception under the highest distributions. 5. The direction of travel is one way, since ramping funds flatter today’s number and coverage is more likely to fall than rise as fee waivers expire. Takeaways For allocators, the coverage column comes before the yield column, and anything below 0.9 needs an explanation on the record. Payouts above 18% should be read as capital coming back unless NAV per share is stable or growing. For managers, the thresholds are now public: 0.92 is the low-cut-risk zone, 0.50 is the level the listed market prices as distress. Funds that publish the bridge themselves will own that conversation. Related Resources: For the fuller picture, download our **[Credit BDC Sector Monitor](https://cardoai.com/reports/credit-bdc-sector-monitor/)**. To learn how Cardo AI can help you centralize your data, track performance, and automate compliance for your ABF portfolio, you can **[request a live platform demo of Cardo AI](https://cardoai.com/book-a-demo/?utm_source=bing_ai_breadcrumbs)**[.](https://cardoai.com/book-a-demo/?utm_source=bing_ai_breadcrumbs) --- ### [Hidden concentration in Private Credit: why ten managers can be one bet](https://cardoai.com/reports/hidden-concentration-in-private-credit/) **Published:** September 11, 2026 **Author:** GiadaCardo26 **Content:** Contents [Key developments](#first-par) [Takeaways](#second-par) Cardo AI’s latest report on private credit examines where diversification really breaks in a multi-manager book, with obligor and sponsor look-through, tail-loss simulations across ten managers, strategy correlation under stress, and liquidity analysis of semi-liquid vehicles. At first glance, a book of twenty holdings across four funds looks well diversified: the worst single-line shock costs around 3% of NAV. Look one layer deeper, and the picture changes. Twenty holdings resolve to eleven borrowers behind seven sponsors, and a single sponsor’s default would cost roughly 11% of NAV. The line items look spread; the exposure beneath them is not. That’s the story of Hidden Concentration in Private Credit: a market where holding more managers no longer guarantees diversification, and where the real risk sits in the borrowers and sponsors they quietly share. Key developments - **Lender and owner are converging.** Large alternative managers increasingly run PE funds, direct lending vehicles, BDCs and insurance balance sheets under one roof, with the SEC’s April 2025 co-investment relief easing affiliated deals further. - **Semi-liquid vehicles hit their limits.** In Q1 2026, redemption requests ran well above the 5% quarterly cap across the largest wrappers, from 7.2% at KKR’s K-ABF to 15.7% at Carlyle’s Tactical Private Credit Fund. - **Exposure is clustering.** MSCI finds five sectors account for 76% of private credit fair value, and bank lending to private credit vehicles has grown from about $8bn in 2013 to $95bn in 2024. Takeaways - **Reported single-name concentration likely understates sponsor-level loss by three- to fourfold.** The gap is invisible without obligor and sponsor look-through. - **Manager count is not diversification.** Ten managers cut 99th-percentile loss from 5.6% to 3.1%, but only if they lend to different borrowers. At full overlap, ten managers are no safer than one. - **Platform convergence erodes the benefit quietly.** A book sourcing 60% of its exposure from platforms that are both lender and owner gives up more than half the tail-risk benefit of holding ten managers. - **Strategy labels overstate breadth.** Six strategies behave like about two and a half independent bets in calm markets, and closer to one and a half under stress. - **Liquidity buffers are thinner than they look.** Once redemptions and asset stress are modelled together at a realistic correlation, a six-point cushion shrinks to roughly one. - **Technology sees; people decide.** Tooling resolves entities and flags mark dispersion, but whether a mark is honest or a covenant will bite remains a human call. To learn how Cardo AI can help you centralize your data, track performance, and automate compliance for your ABF portfolio, you can **[request a live platform demo of Cardo AI.](https://cardoai.com/book-a-demo/?utm_source=bing_ai_breadcrumbs)** --- ### [Credit BDC Sector Monitor Q2 2026](https://cardoai.com/reports/credit-bdc-sector-monitor-q2-2026/) **Published:** October 13, 2026 **Author:** RobertaCardo26 **Content:** Contents [Key developments](#first-par) [Takeaways](#second-par) Cardo AI’s Credit BDC Sector Monitor reads loan-level SEC filings bottom-up to show what BDC portfolios hold, what they earn and how they mark it. The second edition ([see here the first](https://cardoai.com/reports/credit-bdc-sector-monitor/)) widens the universe from 53 to 85 BDCs (33 listed and 52 non-traded), covering **37,906 loans** and **253,195 quarterly floating-rate observations**, with data through **2026-Q2**. The central finding is that pricing and marks have come apart. The compression in new-issue spreads that ran from 2023 to 2025 has paused, and sector pricing sits inside a 50 bps band. The held book tells a different story: managers already mark Media, Healthcare and Technology positions for stress while pricing their new loans like everything else. That gap is what the monitor is built to surface. Key developments 1. **Compression has paused.** The all-sector median new-issue spread ran 600 / 525 / 475 / 475 bps across 2023, 2024, 2025 and 2026 year-to-date. Quarterly cohorts went 465, 475 and 500 bps over the last three quarters. The 2026-Q2 widening sits in Consumer (525 bps) and Healthcare (500 bps). 2. **Marks separate sectors where spreads do not.** The share of seasoned loans marked below 0.85 runs from 15.3% in Media, 11.8% in Healthcare and 11.3% in Technology to 6.7% in Financials. New loans in those sectors price within 50 bps of Financials. Healthcare combines the worst public default record of the ten sectors with new-loan pricing only 25 bps over Financials. 3. **Technology is the watch item.** The share of Technology loans marked below 0.95 went from 12.1% to 21.5% in a year, while the new-issue median held at 475 bps. Twelve of 54 funds now mark the sector below 0.95, and the mark fell in 29 of 51 funds, so the move is broad rather than concentrated. 4. **Funds agree on performing loans and disagree on stressed ones.** Across 1,187 shared borrowers, the median gap between funds is 0.3 points. Among the 82 shared borrowers marked below 0.90, 29% carry gaps above 10 points. The same loan can sit 20 to 40 points apart in two books. 5. **Manager selection dominates sector selection.** Book marks run from 0.836 to 1.004 across 79 funds, and fund deep-discount shares from 0.0% to 40.8%, inside a 50 bps sector spread band. 27 of 85 funds clear our manager filter. Takeaways **For GPs**: the 54 bps spread advantage on 2023 and 2024 loans closes as they repay. If your book is heavy in Healthcare or Media, expect LPs to ask about your marks before your spreads. Publish your own figure first. **For LPs**: pick the manager before the sector. - **Screen:** a seasoned book mark of 0.96 or better and a weighted average spread of 500 to 650 bps. - **Question:** funds pairing a spread above roughly 650 bps with a double-digit deep-discount share. - **Monitor:** share of loans at or below 0.90 (universe 9.2%), Technology loans below 0.95 (21.5%), and gap to the other holders of shared loans. - **Size on the tail:** the modelled worst-1% loss runs from 12.0% to 17.4% of the book across loss assumptions. To learn how Cardo AI can help you centralize your data, track performance, and automate compliance for your ABF portfolio, you can **[request a live platform demo of Cardo AI.](https://cardoai.com/book-a-demo/?utm_source=bing_ai_breadcrumbs)** --- ### [Credit BDC Sector Monitor Q1 2026](https://cardoai.com/reports/credit-bdc-sector-monitor/) **Published:** July 20, 2026 **Author:** GiadaCardo26 **Content:** Contents [Key developments](#first-par) [Takeaways](#second-par) Cardo AI’s Credit BDC Sector Monitor brings the toolkit buy-side desks already use in liquid credit, carry per unit of risk, a sector correlation matrix, expected loss, an expected-shortfall tail measure and a Black-Litterman benchmark, to a market where no market prices exist. To our knowledge it is the first time the full apparatus is assembled bottom-up from loan-level SEC filings for private credit. The launch issue covers **53 BDCs**, roughly **35,600** unique floating-rate **loans** and **252,000 quarterly loan observations** across thirteen quarters, with data through 2026-Q1. The central finding is a **repricing**. Across the universe, the all-sector median spread on newly originated loans compressed from 600 bps in 2023 to 480 bps in 2026 year-to-date, and every sector now clears inside a narrow band around that median, so new-issue pricing has stopped differentiating sector risk. The held book tells a different story, with managers already marking Media, Transportation and Consumer positions for stress while new-issue spreads price all sectors alike. That gap is the inefficiency the monitor is built to surface. Key developments 1. Spread compression is now structural, so entry pricing no longer rewards waiting, and the 2023-2024 vintages already on the book carry 70-120 bps more contractual spread than anything originated today, which favours seasoned exposure and secondaries over new commitments at equal quality. 2. Where spreads no longer differentiate, marks do, and on the seasoned book Media and Transportation carry the heaviest discount tails at 15-16%, with Consumer close behind at 13%, against 8-9% for Financial Services and Technology. 3. Those marks are stale, with 36% literally unchanged quarter over quarter, so risk numbers computed from reported NAVs understate true risk in this report and anywhere else, which is why the risk engine prices from market-anchored sources instead. 4. Diversification is real but fragile, since the posterior correlation matrix sits at a 0.50 mean off-diagonal, giving a one-year 99% Expected Shortfall of 13.8% and a Diversification Ratio of 1.39, but stressed co-movement rises to 0.78 and removes most of that benefit exactly when it matters. 5. Manager selection dominates sector policy, with weighted average spreads running from 321 to 728 bps across the 53 funds and book marks from 0.65 to 1.01 inside a single asset class. Takeaways **For GPs originating today**, at 480 bps entry spreads the data supports leaning origination toward Healthcare and Business & Professional Services, and taking Consumer, Transportation, Financial Services or Technology exposure only at pricing or structure better than the market’s. Vintage matters more than volume. **For LPs allocating across managers**, the screen favours funds whose seasoned book marks at 0.96 or better with weighted average spread in the 500-650 bps range, and questions funds pairing wide spreads with double-digit deep-discount tails. Any multi-fund sleeve should be sized on the stress numbers, a one-year model-implied expected shortfall of 13.8% that rises to 15.4% at the top of the correlation band with GFC-severity losses of 15-17%, not on the diversified base case. To learn how Cardo AI can help you centralize your data, track performance, and automate compliance for your ABF portfolio, you can **[request a live platform demo of Cardo AI.](https://cardoai.com/book-a-demo/?utm_source=bing_ai_breadcrumbs)** --- ### [State of Private Credit performance Q2 2026](https://cardoai.com/reports/state-of-private-credit-performance-q2-2026-2/) **Published:** August 26, 2026 **Author:** RobertaCardo26 **Content:** Contents [Key developments](#first-par) [Takeaways](#second-par) The eleventh edition of Cardo AI’s Private Credit Quarterly analyzes 73 closed-end private credit funds managed by Apollo, Ares, Blackstone, Blue Owl, Brookfield/Oaktree, Carlyle, KKR, and TPG Angelo Gordon, with fund-level IRR data, vintage cohort analysis, deployment trends, and complete GP rankings. At first glance, Q2 looks like business as usual: weighted-average Net IRR held steady at 10.0%, up 90 bps year over year. Look one layer deeper, and the picture changes. 22 of the 73 funds we track reported lower IRRs quarter over quarter, only 8 improved. The headline is stable; the breadth beneath it is not. That’s the story of the eleventh edition of Cardo AI’s Private Credit Quarterly: a market where returns are no longer earned by simply being in the asset class, but by picking the right manager, strategy, and vintage. Key developments 1. The post-2021 vintage premium is still large (13.1% vs. 7.9%) but narrowed for the first time, down 20 bps. 2. TPG Angelo Gordon Credit Solutions III topped the rankings at 39.0% Net IRR, while KKR ranked last among GPs for the tenth straight quarter. 3. Deployment stayed selective: Ares and Carlyle led invested-capital growth, while Blue Owl’s contraction traced mostly to redemptions, not credit deterioration. Takeaways Q2 2026 confirms a shift that’s been building for several quarters now: private credit is no longer a single return pool. Stable aggregates mask real dispersion underneath, vintage advantages are normalizing rather than disappearing, and deployment discipline is starting to separate platforms as clearly as performance does. For LPs, the takeaway is straightforward, GP brand alone doesn’t tell you enough. Strategy, vintage, and manager execution are doing the differentiating now, and the data backs that up fund by fund. To learn how Cardo AI can help you centralize your data, track performance, and automate compliance for your ABF portfolio, you can **[request a live platform demo of Cardo AI.](https://cardoai.com/book-a-demo/?utm_source=bing_ai_breadcrumbs)** --- ### [State of Private Credit performance Q1 2026](https://cardoai.com/reports/state-of-private-credit-performance-q1-2026/) **Published:** May 21, 2026 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Private credit is performing. But not evenly](#first-par) [Key developments](#second-par) [The takeaway for LPs](#third-par) [How to automate Private Credit performance tracking](#fourth-par) [Software tools for Private Credit fund analytics](#fifth-par) The tenth edition of Cardo AI’s Private Credit Quarterly analyzes 69 closed-end private credit funds managed by Apollo, Ares, Blackstone, Blue Owl, Brookfield/Oaktree, Carlyle, KKR, and TPG Angelo Gordon, with fund-level IRR data, vintage cohort analysis, deployment trends, and complete GP rankings. Private credit is performing. But not evenly Private credit remains a core institutional allocation, but Q1 2026 delivers a more nuanced message: headline performance is improving, yet the gains are increasingly concentrated. The asset class continues to benefit from higher base rates and floating-rate income, weighted average Net IRR reached 10.08%, up 52 bps quarter-on-quarter, but dispersion widened materially, with the full range expanding to roughly 45 percentage points. LP outcomes are becoming more dependent on vintage timing, strategy selection, and manager execution. Key developments Five developments define the quarter: weak breadth despite stronger aggregates; a widening vintage gap, with post-2021 funds delivering 13.3% versus 7.9% for older vintages; credit-solutions and opportunistic strategies dominating the upper tail; legacy KKR vehicles remaining among the weakest performers for the sixth consecutive quarter; and deployment staying selective and platform-led. The takeaway for LPs Private credit is not a uniform yield product, and Q1 2026 makes that clearer than any prior edition in this series. Twenty-nine funds reported lower IRRs this quarter while only four improved, a breadth reading that sits poorly against an aggregate number that looks healthy. The vintage gap widened again, with post-2021 funds now outperforming older vintages by 5.4 percentage points. The upper tail is increasingly dominated by flexible, opportunistic mandates rather than plain-vanilla direct lending. And the bottom of the table has barely moved in six quarters, suggesting that underperformance in certain vehicles is structural rather than cyclical. For LPs, the implication is straightforward: manager selection, vintage exposure, and strategy design are now the primary drivers of outcome and the cost of getting them wrong is growing. How to automate Private Credit performance tracking As highlighted in the State of Private Credit Performance Q1 2026 report, navigating the mature phase of the credit cycle requires unparalleled data transparency and real-time risk assessment. While analyzing fund metrics quarterly provides high-level benchmarks, asset managers and institutional investors looking to operationalize these insights need technology to eliminate data silos and simulate market stress. Software tools for Private Credit fund analytics Cardo AI platform: - A dedicated private credit software designed specifically to automate data ingestion, normalization, and performance tracking across complex private credit portfolios. - Real-time risk management and stress testing: Rather than relying on lagging indicators or manual spreadsheets, the software allows managers to run continuous liquidity stress testing and asset-backed finance (ABF) analytics on a daily basis. - Unified data layer: The platform replaces manual reporting cycles with an institutional-grade tech stack that monitors credit stress, delinquencies, and cash flow variations at the individual asset level. To learn how Cardo AI can help you centralize your data, track performance, and automate compliance for your ABF portfolio, you can **[request a live platform demo of Cardo AI.](https://cardoai.com/book-a-demo/?utm_source=bing_ai_breadcrumbs)** --- ### [State of Private Credit performance Q4 2025](https://cardoai.com/reports/state-of-private-credit-performance-q4-2025/) **Published:** March 20, 2026 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [The evolving credit cycle](#first-par) [Performance analysis](#second-par) [Deployment and scale](#third-par) As the private credit market moves beyond the rapid repricing phase of 2023–2024, it is entering a mature stage characterized by steadier performance and selective capital deployment. This edition of Cardo AI’s Private Credit Quarterly analyzes 69 closed-end funds managed by industry giants, including KKR, Blackstone, Apollo, Ares, and Oaktree, to provide a high-fidelity look at Net IRR and emerging risks. The evolving credit cycle Private credit continues to play a central role in institutional portfolios, providing stable income and diversification as markets adjust to a “higher-for-longer” interest-rate environment. In Q4 2025, the weighted average Net IRR across the asset class increased to 9.55% (+29 bps QoQ), while the median remained stable at 10%, signaling continued resilience in core returns. However, the market is beginning to face selective loan markdowns within private credit portfolios, particularly in sectors exposed to refinancing pressure such as technology, telecom, and cyclical industries. While these adjustments remain limited in scale, they underscore the growing importance of refinancing risk management, transparency, and disciplined underwriting as the credit cycle evolves. Performance analysis A defining development this quarter is the “Vintage Advantage,” where funds launched in the higher-rate environment continue to significantly outperform earlier vehicles. Post-2021 funds boast a 13.2% weighted average Net IRR, creating a 4.9-percentage-point spread over pre-2021 funds (8.3%). This gap reflects the structural benefit of loans originated at higher spreads and floating-rate coupons following the 2022 interest-rate shock. Among newer vintages, Carlyle leads with a 17% Net IRR, followed by Ares (14.3%) and Brookfield (14%). Conversely, the lowest-performing vehicles remain concentrated in older strategies, such as KKR Special Situations Fund (-4.1%), illustrating a persistent gap between successful modern strategies and underperforming legacy funds. Deployment and scale Capital deployment in Q4 2025 remained highly selective, with activity concentrated among a limited number of massive platforms. The largest increases in invested capital came from **Blue Owl Credit Income Corp. (+$1.9B)** and **Ares SDL III Levered (+$1.2B)**. More broadly, only eight funds across four GPs increased their exposure meaningfully, suggesting a move toward targeted deployment into specific opportunities rather than broad expansion. Scale continues to act as a defensive moat; larger funds outperformed smaller vehicles by roughly **500 basis points** within the top cohort. This highlights the competitive advantages that larger GPs hold in terms of sourcing networks and portfolio diversification, which are becoming essential as managers prioritize structural protections and underwriting discipline. --- ### [Asset-Based Lending Playbook Q2 Forecasts - Q3 Opportunities](https://cardoai.com/reports/asset-based-lending-playbook-q2-forecasts-q3-opportunities/) **Published:** June 24, 2025 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Q2 reality check: What moved the market](#first-par) [Default and delinquency trends](#second-par) [Strategic implications for lenders](#third-par) [Dynamic borrowing-base analytics](#fourth-par) The first half of 2025 has redefined the ABL market, characterized by a massive buildup in warehouse capacity and the scaling of “utility-grade” energy transition deals. While public ABS issuance slowed as issuers waited for spread stability, private credit has aggressively filled the vacuum in digital infrastructure and healthcare. This playbook analyzes the gap between Q2 forecasts and reality to provide a data-driven roadmap for lenders navigating the high-stakes environment of Q3 and beyond. Q2 reality check: What moved the market From digital infrastructure to energy storage, deal sizes are outstripping expectations. The second quarter proved that ABL is no longer confined to traditional retail and consumer sectors. - Digital and energy dominance: Demand for energy storage and data center financing pushed deal sizes well beyond the $150 MM ceiling, with AI-driven GPU leasing becoming a major balance-sheet theme. - Warehouse expansion: Rather than terming-out into the public markets, platforms extended warehouse capacity, creating a “supply coiled spring” ready to convert into $10–15 billion of ABS float in late 2025. - Sector distress: Continued liquidity needs in healthcare and retail forced lenders to utilize larger FILO (First-In, Last-Out) slices and accordion features to manage worsening cash burns and 2022-era maturities. Default and delinquency trends While the 2022-23 vintages exhibited higher default rates, the 2024 cohort is showing signs of stabilization. Subprime delinquencies for 2023 improved by 5 percentage points over 2022, with 2024 originations improving by an additional 1.5%. Cumulative defaults remain well-contained, capped at 6% for subprime and a negligible 0.25% for prime vintages, supporting a positive outlook for the asset class. Strategic implications for lenders Success in Q3 requires preloading pipelines and deepening technical expertise. As the market scales, the “generalist” approach is losing ground to sector specialists. Strategic leaders should focus on: - Anchor orders: With a massive wave of ABS conversions expected, locking in anchor orders early is critical to securing economics before the year-end rush. - Project finance hybridization: Energy transition facilities have reached utility-scale. Lenders without deep project-finance expertise risk being sidelined unless they partner with banks for rigorous construction monitoring. - The accordion advantage: Sponsors now prefer bolt-on capacity over full refinancings. Arrangers who negotiate generous incremental baskets are successfully locking in wallet share through 2026. Dynamic borrowing-base analytics Dynamic borrowing-base analytics are the new requirement for consumer and AI credit. Traditional manual monitoring cannot keep pace with the volatile cash flows of modern ABL. Whether it is holiday-driven consumer swings or GPU-compute rental revenue, lenders need real-time data feeds to maintain an underwriting edge. Cardo AI provides the integrated operating stack necessary to manage these complex “hybrid” covenants, mixing hard-asset collateral with real-time cash flow simulations. By automating the borrowing-base and covenant-monitoring loop, lenders can scale their portfolios without increasing operational risk. --- ### [US Auto Loan Q1 2025](https://cardoai.com/reports/us-auto-loan-q1-2025/) **Published:** April 23, 2025 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Volume growth and asset demand](#first-par) [Credit quality and Vintage gains](#second-par) [Key investment opportunities](#third-par) The US auto loan market opened 2025 with a powerful surge in activity, reaching $45 billion in securitization volumes, the second-highest quarterly figure since 2019. Despite broader economic headwinds, the sector is benefiting from a “cautiously optimistic” outlook. This report, powered by Cardo AI’s Market Intelligence, analyzes the 65% quarter-over-quarter growth in volume and the underlying improvements in recovery rates and delinquency trends that are sustaining investor confidence. Volume growth and asset demand Investor demand rebounded sharply in Q1, with volumes climbing 65% over the previous quarter. While partly seasonal, the $45 billion total underscores deep market liquidity. Appetite remains concentrated in high-quality assets: 71% of issuances are prime loans, and over 90% of tranches carry top investment-grade ratings. This persistent “flight to quality” has kept spreads tight even as supply increases. Credit quality and Vintage gains Underwriting discipline is driving measurable improvements, particularly in the subprime segment. Subprime delinquencies in 2024 vintages improved by 1.5 percentage points year-over-year, following a 6-point gain since 2022. Prime portfolios remains exceptionally stable, with cumulative defaults capped at 0.25%. These trends are supported by a 28-point advantage in recovery rates for prime assets over subprime. Key investment opportunities Our analysis identifies three strategic areas for allocation: - Captive lender lead: For the fifth straight quarter, captives outperformed banks with a 3.6% edge in collection effectiveness. - The 6%–14% “Sweet Spot”: This interest rate bucket offers a superior risk-return balance, with default rates three times lower than high-yield (>14%) loans. - Prime core: Prime remains the market bedrock, with default rates 20 times lower than subprime, offering the most resilient low-volatility profile. --- ### [State of Private Credit performance Q2 2025](https://cardoai.com/reports/state-of-private-credit-performance-q2-2025-2/) **Published:** August 20, 2025 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Market convergence and stability](#first-par) [Performance analysis](#second-par) [Strategic outlook](#third-par) As the private credit asset class matures into a “higher-for-longer” rate environment, the market is shifting from rapid repricing to a phase of normalization. This seventh edition of the Private Credit Quarterly evaluates 52 closed-end funds to determine if current performance represents long-term durability or a period of latent repricing. Market convergence and stability Private credit continues to anchor institutional portfolios, offering consistent income resilience amid persistent monetary tightening. In Q2 2025, the market showed signs of a more cohesive environment; while the weighted average Net IRR saw a modest decline to 9.13%, the median IRR remained anchored at 10%. This stability at the center of the distribution suggests that core performers are successfully navigating the cycle. Notably, performance dispersion is narrowing significantly, with the interquartile range compressing to 18%. This 330-basis point contraction signals a growing convergence in return outcomes and increased discipline in valuation practices across the industry’s top GPs. Performance analysis A key theme this quarter is the “softening” of the previously dominant post-2021 vintages. While newer funds still lead performance across most GPs, particularly KKR (+9.6 p.p. spread) and Blackstone (+4.0 p.p. spread), the overall gap between pre- and post-2021 vintages narrowed to 3.6 percentage points. - **Top performers:** The leading cohort delivered a 14% average IRR, with Ares SDL II Levered returning as a standout performer. Meanwhile, Blackstone’s Mezzanine/Opportunistic II maintained a strong MOIC of 2.2 for the fourth consecutive quarter. - **Persistent laggards**: Underperformance remains concentrated in familiar places. Despite its successes elsewhere, the KKR Special Situations Fund continues to anchor the bottom tier with an IRR of -4.3%, proving that legacy headwinds persist regardless of fund size. Strategic outlook Fundraising and deployment dynamics in Q2 reflect a cautious, “wait-and-see” approach from the broader market. LP commitments remained sparse, with only 6 out of 40 eligible funds successfully raising fresh capital. Blue Owl continues to be the primary outlier in this trend, leading fundraising for the fifth consecutive quarter and single-handedly driving market activity by deploying an additional $1.9 billion via its Credit Income Corp. Beyond this, deployment remains highly selective; only 7 of the 41 tracked funds showed material exposure growth. This indicates that while the broader market is hitting a plateau, managers with significant scale and high-conviction strategies are still finding targeted opportunities to put capital to work. --- ### [US Auto Loan Q4 2024](https://cardoai.com/reports/us-auto-loan-q4-2024/) **Published:** January 23, 2025 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Volume trends and investor caution](#first-par) [Performance recovery and Vintage gains](#second-par) [Strategic allocation opportunities](#third-par) The US auto loan market closed 2024 with a stable outlook on credit performance, marked by a high number of rating upgrades and zero downgrades. While securitization volumes softened to under $30 billion for the first time in two years, underlying fundamentals remained resilient. This report, powered by Cardo AI Market Intelligence, examines the shift toward stricter credit standards and the narrowing delinquency trends that defined the year-end landscape. Volume trends and investor caution Investor optimism moderated in the final quarter, with securitization volumes declining 21% from Q3. This retreat reflects a more cautious sentiment as the market monitors evolving default trends. Despite lower overall volume, the demand for prime originations remains dominant, accounting for 72% of total issuance. Investors continue to show a strong preference for high-quality notes, supported by a 14-basis point reduction in average spreads for high-grade tranches. Performance recovery and Vintage gains Credit performance saw a marked improvement in Q4, particularly within the subprime segment. The 2024 vintage demonstrated a 2-percentage-point reduction in delinquencies compared to 2023, and a significant 7-point improvement over 2022. Prime originations maintained their historic stability with cumulative defaults capped at 0.25%. These gains, driven by disciplined underwriting, signal a steady recovery in asset quality across the board. Strategic allocation opportunities Our analysis highlights three areas where risk-adjusted returns remain most compelling: - Prime resilience: Prime loans deliver risk levels 20 times lower than subprime, with recovery rates 25 percentage points higher, making them a dependable core investment. - The captive advantage: For the fourth straight quarter, captive lenders outperformed banks, showing 7% higher efficacy in converting delinquent accounts back to performing status. - The 6%–14% yield bucket: This interest rate range offers an 8-percentage-point lower default rate and significantly higher recovery potential compared to the high-yield (>14%) segment. --- ### [Exploring the role and potential of Large Language Models in the Asset-Based Finance sector](https://cardoai.com/reports/exploring-the-role-and-potential-of-large-language-models-in-the-asset-based-finance-sector/) **Published:** February 19, 2025 **Author:** RobertaCardo26 **Content:** Contents [The rise of domain-specific finance AI](#second-par) [Overcoming the barriers to implementation](#third-par) [Automated extraction and conversational agents](#fourth-par) While the broader financial industry has embraced Large Language Models (LLMs) for market prediction and algorithmic trading, the Asset-Based Finance (ABF) sector presents a unique set of challenges. From data privacy to the extreme complexity of structured transactions, generic AI often falls short. This report examines how the industry is shifting toward domain-specific model tuning, transforming LLMs from simple chatbots into powerful engines for credit scoring and automated risk analysis. The rise of domain-specific finance AI Generic models are being replaced by specialized architectures like FinBERT and BloombergGPT. Success in financial AI is no longer about the size of the model, but the specificity of the data. Early implementations in ABF have focused on improving loan default predictions and credit scoring by fine-tuning models with sector-specific datasets. These tailored approaches allow for much higher accuracy in categorizing companies or generating unique Human Capital KPIs, tasks where “off-the-shelf” AI typically struggles with the nuance of private market data. Overcoming the barriers to implementation Addressing data privacy and model reliability is critical for institutional adoption. The path to successful AI integration is not without hurdles. Inconsistent performance and the difficulty of processing fragmented financial documents remain key points of failure. To achieve institutional-grade reliability, managers must address ethical biases and regulatory compliance head-on. The industry is moving toward a “problem-focused” tuning strategy, ensuring that LLMs are built to solve specific operational bottlenecks rather than acting as a catch-all solution. Automated extraction and conversational agents The future of ABF will be defined by LLMs capable of precise information extraction from complex legal and financial documents. We are moving toward a reality where conversational agents can navigate intricate ABF transactions in real-time, providing immediate clarity on portfolio performance. Cardo AI provides the integrated infrastructure to power these use cases, offering the data ingestion and customization tools necessary to turn raw financial data into actionable, AI-driven intelligence. --- ### [Leveraging language models at Cardo AI. A dual approach](https://cardoai.com/reports/leveraging-language-models-at-cardo-ai-a-dual-approach/) **Published:** February 27, 2025 **Author:** RobertaCardo26 **Content:** Contents [Introduction](#first-par) [Specialized intelligence vs. general power](#second-par) [Beyond conversational AI in Structured Finance](#third-par) [Building a scalable, long-term vision for AI](#fourth-par) Introduction As Large Language Models (LLMs) redefine how organizations interpret unstructured data, the high-stakes world of structured finance requires more than generic conversational AI. Cardo AI has pioneered a strategic “dual approach”, combining in-house fine-tuned models with elite third-party architectures like Anthropic’s Claude and Meta’s Llama. This hybrid strategy ensures that institutional investors benefit from both the massive reasoning power of global models and the precision of domain-specific intelligence. Specialized intelligence vs. general power Cardo AI balances custom fine-tuning with the world’s leading third-party models. By leveraging Amazon Bedrock, Cardo AI integrates best-in-class external LLMs for broad analytical tasks while maintaining a suite of proprietary, fine-tuned models for specialized finance operations. This flexibility allows the platform to process vast amounts of unstructured legal and financial data with a level of accuracy that “off-the-shelf” models cannot match. The result is an AI ecosystem that is purpose-built for the nuance of Asset-Based Finance (ABF), rather than adapted from general use cases. Beyond conversational AI in Structured Finance Addressing data privacy and model reliability is critical for institutional adoption. The path to successful AI integration is not without hurdles. Inconsistent performance and the difficulty of processing fragmented financial documents remain key points of failure. To achieve institutional-grade reliability, managers must address ethical biases and regulatory compliance head-on. The industry is moving toward a “problem-focused” tuning strategy, ensuring that LLMs are built to solve specific operational bottlenecks rather than acting as a catch-all solution. Building a scalable, long-term vision for AI The “Dual Approach” is designed to be future-proof; as the AI landscape evolves, Cardo AI can seamlessly swap or upgrade underlying models without disrupting client workflows. This integrated operating stack ensures that data ingestion, structure intelligence, and cash flow simulations are always powered by the most efficient model for the task at hand. By owning this data-to-intelligence loop, Cardo AI provides institutional investors with a resilient, cutting-edge platform that turns the complexity of ABF into a clear, actionable advantage. --- ### [ABF cash-flow modeling: Are there alternatives to Excel-and-Intex?](https://cardoai.com/reports/abf-cash-flow-modeling-are-there-alternatives-to-excel-and-intex/) **Published:** July 24, 2025 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Current industry standards](#first-par) [Growing limitations in ABF modeling](#second-par) [New alternative offerings](#third-par) [Why it matters](#fourth-par) [Call for feedback](#fifth-par) Current industry standards Structured finance waterfall modeling has been dominated by a few platforms, notably Intex (alongside others like Trepp or Riskspan). Their extensive library and trusted analytics make them a go-to platform for public ABS/RMBS/CMBS/CLO cashflow modeling. In private assetbacked finance (ABF) deals, which are often bespoke and dynamic, the standard tools show limitations. Many private or esoteric deals end up modeled in custom Excel spreadsheets. This stopgap approach frustrates investors and deal teams, as manual spreadsheets are error-prone and hard to scale. The current ecosystem lacks an agile, tailored solution for these non-standard transactions. Growing limitations in ABF modeling Current industry standards are less adapted for bespoke private asset-based finance deals. Many private ABF transactions, often backed by short-term, revolving assets, end up modeled in Excel, which frustrates investors and deal teams. Static models are less agile when dealing with short-duration assets, facility upsizes, or refinancings, making it hard to adapt models quickly to changing deal terms New alternative offerings Cardo AI offers a modern alternative for ABF waterfall modeling with platforms that are flexible (capable of handling dynamic pools, not fully subscribed notes, and scenario changes), intuitive (user-friendly interface versus complex coding), and integrated (combining data management, risk analytics, reporting and flexible waterfall modeling in one solution). This new technology streamlines deal execution, improves risk monitoring, and boosts transparency for stakeholders, addressing the pain points of Excel and rigid legacy tools Why it matters The private ABF market is large and growing fast, now an estimated $5.2 trillion globally (15% larger than in 2020) and projected to reach $7.7 trillion in the next five years. As more investors and asset managers pour into this space, there’s a pressing need for better tools to manage complexity and scale. Embracing intelligent modeling technology could make the ABF ecosystem more transparent, agile, and investor-friendly, reducing operational risks and boosting confidence in deal performance. Call for feedback Can new alternatives truly replace the ingrained Excel-andIntex-like combo? We invite ABF asset managers, structurers, and investors to share their experiences of cashflow/waterfall modeling with current industry standards, Excel, or new platforms . Your insights will help prepare for the evolution of modeling in ABF. --- ### [Emerging ABF strategies for established Asset Managers: unlocking Europe's market opportunity](https://cardoai.com/reports/emerging-abf-strategies-for-established-asset-managers/) **Published:** October 16, 2025 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Introduction](#introduction) [Enhancing yield beyond corporate credit](#first-par) [Regulatory tailwinds and capital efficiency](#second-par) [Integrated technology](#third-par) Introduction Asset-Based Finance (ABF) is the new frontier of private credit, projected to reach $9.2 trillion globally by 2029. In Europe, structured assets already exceed €1.2 trillion, offering a massive, diversified alternative to traditional corporate lending. This report explores how established managers can transition from unsecured credit to asset-backed strategies, leveraging their scale to capture institutional-grade yield. Enhancing yield beyond corporate credit ABF provides a natural yield-enhancing sleeve for existing portfolios. Unlike corporate lending, ABF secures returns against tangible pools like mortgages, auto loans, and royalties. Leaders like KKR and Oaktree highlight ABF’s ability to pick up additional return with lower correlation to corporate cycles. By layering stable assets like UK RMBS with high-yield niche exposures like digital infrastructure, managers can build “all-weather” portfolios that complement direct lending and CLO strategies. Regulatory tailwinds and capital efficiency New frameworks are unlocking a €6.4 trillion insurer buyer base. Revisions to Solvency II and ESMA disclosure reforms have made European ABF a primary target for insurance companies. Established managers are uniquely positioned to launch insurance-friendly platforms designed for transparency and capital efficiency. With €6.4 trillion on insurer balance sheets, those who can provide the required data granularly will turn regulatory compliance into a significant competitive moat against smaller entrants. Integrated technology Sophisticated data infrastructure is the key to scaling ABF. The complexity of European asset pools, spanning multiple jurisdictions and asset types, requires an institutional-grade tech stack. While new managers often struggle with reporting and data ingestion, established players can use integrated platforms to manage complexity at scale. Cardo AI provides the structure intelligence and cash flow simulation needed to transform opaque asset pools into transparent, high-performing investment vehicles. --- ### [How lenders can avoid the next Tricolor, First Brands and Carriox Capital](https://cardoai.com/reports/how-lenders-can-avoid-the-next-tricolor-first-brands-and-carriox-capital/) **Published:** November 5, 2025 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Introduction](#first-par) [The high cost of opaque assets](#second-par) [Closing the oversight gap](#third-par) [A new framework for lenders](#fourth-par) Introduction The recent collapses of Tricolor, First Brands, and Carriox Capital have sent shockwaves through the private credit and asset-based finance (ABF) sectors. These weren’t just credit failures; they were systemic breakdowns in oversight. Together, these cases exposed over $11 billion in hidden liabilities and fraudulent collateral, proving that traditional, reactive monitoring is no longer enough. To survive the next cycle, lenders must move from a “Trust, but Verify” mindset to a “Verify, then Trust” framework, where continuous, real-time data is the only acceptable baseline for funding. The high cost of opaque assets From double-pledged VINs to fabricated invoices, the mechanics of these collapses were strikingly similar. While the industries varied, the “control fraud” techniques did not. At Tricolor, alleged double-pledging of auto loans bypassed warehouse controls, while First Brands used billions in off-balance-sheet supply chain financing to mask its true leverage. In the telecom space, Carriox Capital unraveled when lenders discovered hundreds of millions in receivables were backed by forged contracts. These cases reveal a critical blind spot: manual audits and periodic field exams are too slow to catch in-period manipulations or rapid growth spikes before they become catastrophic. Closing the oversight gap Real-time verification must replace sampling-based audits. The “Verify, then Trust” framework addresses the fundamental flaw in current monitoring: the lag between an event and its detection. Traditional oversight relies on historical “loan tapes” and reactive reporting that fraud can easily circumvent. A modern framework requires: - Automated data reconciliation: Matching borrower data to independent sources (e.g., VIN registries or carrier portals) daily, not quarterly. - Cross-lender risk detection: Identifying if the same assets are being pledged across multiple facilities. - Cash flow integrity: Ensuring that every dollar of reported recovery can be traced directly from the collection account to the lender’s waterfall. A new framework for lenders The winners of the next cycle will be the lenders who own the data stack. Avoiding the next major default requires an integrated operating stack that removes the “black box” between the borrower and the lender. By moving monitoring into a unified environment, where data ingestion, covenant tracking, and cash flow simulations happen in real-time, lenders can spot “small-f” irregularities before they scale into “capital-F” fraud. Cardo AI provides this infrastructure, giving lenders the continuous verification tools needed to turn data into a protective barrier against mismanagement and market volatility. --- ### [Risk without accountability: The financial collapse of Tricolor, First Brands and Carriox Capital](https://cardoai.com/reports/navigating-redemption-pressure-in-private-credit-and-semi-liquid-funds/) **Published:** November 6, 2025 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Risk without accountability](#first-par) [The failure of traditional oversight](#second-par) [Shifting to a “Verify, then Trust” model](#third-par) Risk without accountability The collapses of Tricolor, First Brands, and Carriox Capital represent more than just credit defaults; they expose a systemic failure of the financial industry’s traditional gatekeepers. When an ‘AA (sf)’ rating is issued three months before a $2 billion default, and “clean” audit opinions precede an $8.4 billion collapse, the message is clear: the industry’s reliance on third-party stamps of approval is broken. These events have eroded investor confidence and wiped out over $100 billion in bank market value, proving that in modern finance, complexity is often used to mask a total lack of transparency. The failure of traditional oversight When ratings and audits fail, investors bear the ultimate cost. The “Trust, but Verify” model has become obsolete because the “verify” portion has been outsourced to agencies that failed to catch blatant irregularities. - Tricolor & Carriox: Despite high-grade ratings and collateral agents, these firms allegedly bypassed controls through double-pledging and fabricated invoices, leading to massive losses for lenders like JPMorgan Chase and HPS Investment Partners. - First Brands: Opaque off-balance-sheet structures masked a debt mountain that auditors failed to flag just months before a total collapse. Major institutions including UBS, Jefferies, and Fifth Third Bancorp found themselves exposed to hundreds of millions in losses because they trusted flawed assessments rather than performing deep-dive, independent verification of the underlying assets. Shifting to a “Verify, then Trust” model The lesson from these crises is that transparency cannot be sacrificed for complexity. A “Verify, then Trust” framework moves accountability back to the lender through: - Direct control of cash flows: Seizing control of originator waterfalls to ensure payments are real and traceable. - Real-world early warnings: Monitoring operational signals, such as vendor payment delays, rather than waiting for a quarterly audit. - Independent collateral audits: Mandating real-time, technology-driven surveillance that verifies the existence and status of every pledged asset daily. --- ### [State of Private Credit performance Q3 2025](https://cardoai.com/reports/state-of-private-credit-performance-q3-2025/) **Published:** November 19, 2025 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Market resilience and normalization](#first-par) [Performance analysis](#second-par) [The rise of new Vintages](#third-par) [Scale and selective growth](#fourth-par) As the asset class moves deeper into a “higher-for-longer” environment, private credit is transitioning from rapid repricing toward market normalization. This eighth edition of the Private Credit Quarterly analyzes 52 funds from top-tier GPs, including **KKR, Blackstone, Ares, and Blue Owl,** to reveal a landscape defined by strengthening headline performance and widening dispersion. Market resilience and normalization Private credit continues to serve as a strategic anchor for institutional portfolios, offering defensive yield as credit cycles mature. In Q3 2025, the market showed notable strength as the weighted average Net IRR rose to 9.54%, marking a 40-bps improvement quarter-over-quarter. Despite broader market volatility, the median Net IRR held firm at 10%, signaling enduring stability in core fund performance. However, this stability masks a significant shift in dispersion; the interquartile range compressed by 900 bps to 27%, suggesting a convergence in realized outcomes as valuation methodologies become more disciplined across the industry. Performance analysis A defining development this quarter is the “Vintage Advantage,” where funds launched in the higher-rate environment continue to significantly outperform earlier vehicles. Post-2021 funds boast a 13.2% weighted average Net IRR, creating a 4.9-percentage-point spread over pre-2021 funds (8.3%). This gap reflects the structural benefit of loans originated at higher spreads and floating-rate coupons following the 2022 interest-rate shock. Among newer vintages, Carlyle leads with a 17% Net IRR, followed by Ares (14.3%) and Brookfield (14%). Conversely, the lowest-performing vehicles remain concentrated in older strategies, such as KKR Special Situations Fund (-4.1%), illustrating a persistent gap between successful modern strategies and underperforming legacy funds. The rise of new Vintages A dominant theme this quarter is the regaining momentum of newer fund vintages. The performance spread between post-2021 and pre-2021 funds has re-expanded to 4.6%, driven primarily by the high-interest-rate environment at inception. - **Top performers:** For the first time in over a year, leadership has rotated. Brookfield’s Oaktree Opportunities Fund XII (23% Net IRR) and Ares SDL III Levered (19.2%) now lead the cohort, while the average IRR for top-quartile funds jumped to 21%. - **GP rankings**: Brookfield has claimed the top spot with a 14.1% weighted average Net IRR, overtaking TPG Angelo Gordon for the first time in seven quarters. Conversely, legacy stress remains concentrated in the bottom quartile, with the KKR Special Situations Fund (-4.1%) remaining the market’s primary laggard. Scale and selective growth Capital flows and deployment activity have become increasingly polarized. While most managers are exhibiting outright caution, Blue Owl continues to dominate the landscape, leading for the sixth consecutive quarter with $1.75B raised and $1.3B in new assets deployed. Beyond this outlier, deployment remains highly selective; only six funds across the entire dataset meaningfully increased their portfolio exposure this quarter. This shift toward high-conviction, targeted deployment—led by funds like Blackstone’s Senior Direct Lending I (+16% invested)—proves that scale and sourcing advantages are now the deciding factors in maintaining growth during a period of general market restraint. --- ### [Are your ABF risk management analytics truly effective?](https://cardoai.com/reports/asset-backed-finance-risk-modeling/) **Published:** November 21, 2025 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Introduction](#first-par) [Moving beyond deterministic scenarios](#second-par) [The power of stochastic methods](#third-par) [Building a scalable risk infrastructure](#fourth-par) Introduction Are your Asset-Based Finance (ABF) risk management analytics truly effective? In an era of rapid data expansion and increased computing power, developing highly precise predictive models is no longer just an advantage, it is a necessity. This report explores how advanced quantitative techniques can be leveraged to optimize both underlying credit portfolios and waterfall structures, ensuring investors achieve the risk-return tradeoff they expect in every tranche. Moving beyond deterministic scenarios Static models often understate tail dependencies and overlook interconnected risk factors**.** Traditional risk modeling frequently relies on deterministic scenarios that fail to capture the complexity of modern markets. High correlations within asset pools can sharply amplify tranche losses during extreme market events, often in ways that static models miss. By shifting to temporal models and full-distribution analyses, practitioners can more accurately capture “heavy-tailed” behavior and shifting risk dynamics, providing a much higher level of transparency and comparability for institutional investors. The power of stochastic methods Precision engineering of tranches strengthens market resilience and investor confidence**.** By embracing state-of-the-art stochastic methods, issuers can engineer tranches that align more closely with specific investor preferences. These advanced tools allow for more reliable risk-return assessments, bolstering the overall resilience of the ABF market. Rather than relying on a single “likely” outcome, stochastic modeling empowers managers to understand the entire spectrum of possibility, improving overall market efficiency and protecting capital during periods of high volatility. Building a scalable risk infrastructure Full-distribution simulations require robust data pipelines and scalable computing. To realize the benefits of modern risk modeling, firms must implement a framework backed by sophisticated infrastructure. This includes plausibility-weighted macroeconomic scenarios and dynamic default frameworks that can process vast datasets in real-time. Cardo AI provides the integrated operating stack necessary for this transition, offering the data ingestion and high-performance computing power needed to turn complex quantitative theories into actionable, resilient ABF strategies. --- ### [US Auto Loan Q4 2025](https://cardoai.com/reports/us-auto-loan-q4-2025/) **Published:** February 3, 2026 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Credit performance under pressure](#first-par) [Investors stay disciplined](#second-par) [Where the opportunity lies](#third-par) The US auto loan ABS market closed Q4 2025 at a record $48 billion in issuance, the highest fourth-quarter level ever recorded. Yet strong headline volumes tell only part of the story. Year-over-year growth of 34% signals structural demand for the asset class, but a closer look at credit performance, investor behavior, and sponsor dynamics reveals a market navigating real and growing pressure points Credit performance under pressure Beneath robust issuance figures, credit stress is building across the board, and most acutely in subprime. Prime 60+ delinquencies climbed to 0.72% by December 2025, up 9 bps year-over-year, while subprime 60+ delinquencies reached 7.38%, a 105 bps increase over the same period. Default trends followed suit, with subprime annualized net losses rising to 11.06% from 9.4% in Q3. Recovery rates weakened into year-end as well, with subprime recoveries falling to 33.95%, down nearly 200 bps year-over-year. While prime performance remains broadly contained, subprime credit stress continues to build, adding complexity to portfolio construction and risk assessment. Investors stay disciplined Investor positioning reflects this nuance. Prime rated notes continue to anchor demand, accounting for approximately 71% of total Q4 issuance, while less than 7% of tranches were issued below investment grade, consistent with recent quarters and indicative of a persistently conservative investor base. Average spreads remained broadly stable quarter-over-quarter and widened only slightly year-over-year (+4 bps), suggesting that investors have not broadly repriced the asset class despite deteriorating credit metrics. The picture that emerges is one of disciplined, quality-focused confidence, investors remain engaged, but increasingly selective about where they take risk. Where the opportunity lies Sponsor-level analysis adds another layer of differentiation, revealing distinct yield-risk profiles across the market. High-yield, high-risk sponsors such as Bridgecrest, Santander, and Exeter operate with APRs above 20% and losses exceeding 2.5%, while lower-risk profiles like World Omni Financial offer APRs of 5–7% with minimal loss rates. Cardo AI’s enhanced risk-reward framework cuts through this complexity, identifying CarMax, Bridgecrest, and World Omni Financial as consistent outperformers that deliver statistically superior risk-adjusted returns, making them the strongest candidates for buy-side allocation in a market where sponsor selection has never mattered more. --- ### [Navigating redemption pressure in Private Credit and semi-liquid fund](https://cardoai.com/reports/asset-based-lending-playbook-q2-2025/) **Published:** March 3, 2026 **Author:** sviluppatore **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [From theory to reality](#introduction) [Beyond the spreadsheet](#first-par) [Solving the liquidity gap](#second-par) From theory to reality The “liquidity bridge” in private credit is currently facing its most significant structural test. As evidenced by recent withdrawal limits at major funds like BlackRock’s HPS, the challenge for managers has shifted from simple credit selection to complex liability management. Semi-liquid and interval fund structures are functioning exactly as designed, utilizing quarterly caps and proration, but the operational friction of managing these “gates” is creating a trust deficit. For modern asset managers, the goal is no longer just maintaining credit quality; it is the sophisticated task of matching investor liquidity expectations with the inherently illiquid nature of private debt. Beyond the spreadsheet In a market defined by redemption pressure, ad hoc liquidity committees and manual tracking are no longer sufficient safeguards. Navigating this period of volatility requires a repeatable, data-driven framework built on five core pillars: early-warning detection, rigorous scenario modeling, portfolio stratification, granular transparency, and swift governance execution. Managers who successfully weather these cycles will be those who move away from reactive “damage control” and toward a proactive stance, using real-time data to forecast capacity and communicate with investors before a breach occurs. Solving the liquidity gap Bridging the gap between private credit structures and investor demands requires a modernized operating stack. Cardo AI empowers fund managers to transition from manual oversight to automated structure intelligence. By integrating seamless data ingestion with advanced cash flow simulations and automated covenant monitoring, our platform provides the clarity needed to manage redemptions without disrupting portfolio alpha. In an era of shifting liquidity, Cardo AI provides the analytical engine to maintain operational control, satisfy regulatory scrutiny, and preserve long-term investor confidence. --- ### [The SaaS credit paradox: Navigating software lending stress in the age of AI](https://cardoai.com/reports/the-saas-credit-paradox-navigating-software-lending-stress-in-the-age-of-ai/) **Published:** March 20, 2026 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [The end of “safe” software debt](#introduction) [The decay of seat-based underwriting](#first-par) [The death of the multi-year moat](#second-par) [Integrated technology](#second-par "#third-par") The end of “safe” software debt For over a decade, enterprise software was the gold standard of private credit, underwritten against the “invincibility” of recurring revenue and per-seat pricing. However, as Artificial Intelligence fundamentally reshapes the enterprise landscape, these once-durable moats are evaporating. At Cardo AI, we are observing a structural shift: software portfolios are facing unprecedented stress as AI-driven disruption challenges the very metrics used to underwrite them. The decay of seat-based underwriting AI-driven efficiency is breaking the link between headcount and revenue. Much of existing software debt was sized on the assumption of steady “headcount expansion.” As AI automates knowledge work, enterprises are achieving greater output with fewer licensed users. In seat-based structures, this workforce optimization translates directly into revenue contraction. Even if a customer remains “active,” shrinking contract values are putting immediate pressure on capital structures that were sized for high net retention and growth. The death of the multi-year moat Innovation speed is eroding long-term revenue visibility. Historically, high switching costs and slow innovation cycles protected software vendors and their lenders. Today, AI accelerates feature replication, allowing competitors to bridge gaps in months rather than years. Consequently, enterprises are shortening contract durations to preserve flexibility. This trend weakens cash flow duration, a critical failure for lenders who underwrote multi-year stability into their original leverage assumptions. Integrated technology Margin compression via “Compute COGS” is the new covenant risk. The “90% gross margin” SaaS dream is under threat from variable, usage-linked computing expenses like GPU utilization and inference tokens. If vendors cannot pass these costs to customers, gross margins compress, triggering EBITDA-based covenant breaches even while top-line revenue remains flat. To navigate this “SaaS credit paradox,” lenders must move beyond static reporting and use integrated platforms to model margin volatility in real-time. Cardo AI provides the infrastructure to monitor these shifting unit economics, turning opaque software portfolios into transparent, resilient credit strategies. --- ### [State of Private Credit performance Q4 2024](https://cardoai.com/reports/state-of-private-credit-performance-q4-2024/) **Published:** March 27, 2025 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Introduction](#first-par) [Manager dispersion and the Vintage gap](#second-par) [Capital flows and deployment trends](#third-par) Private credit has solidified its position as a core asset class for institutional investors, offering a unique combination of yield potential and portfolio diversification. As the higher interest rate environment persists, the industry is navigating a new era of capital deployment and fundraising. The central question remains: are we seeing a permanent shift in private debt’s return profile, or a temporary recalibration? Introduction The Q4 2024 update of the Cardo AI Private Debt Quarterly tracks 60 elite funds from GPs like Blackstone, KKR, and Blue Owl, assessing: - Performance benchmarks: Net IRR and MOIC trends across the cohort. - The Vintage gap: The impact of market cycle timing on newer vs. older funds. - Deployment activity: How GPs are selectively putting capital to work. While overall performance remains resilient, capital flows have become increasingly selective, signaling a cautious yet constructive outlook for the year ahead. Manager dispersion and the Vintage gap As the private debt asset class matures, the “spread” between the best and worst-performing funds has become increasingly pronounced. Our Q4 2024 analysis reveals that performance is no longer lifting all boats equally; instead, a significant dispersion of returns is emerging across the industry’s largest General Partners. While leaders like TPG Angelo Gordon maintained a weighted average Net IRR of 12.2%, individual fund results varied by as much as 11 percentage points, highlighting the specific risks associated with different credit strategies and underlying asset quality. Parallel to this manager dispersion is the growing “Vintage Gap.” Timing has become a primary driver of alpha: funds launched post-2021, during the current high-interest-rate regime, continue to outperform earlier vintages by a steady 5 percentage points. These newer vehicles benefit from a cleaner entry point, higher base rates, and more conservative deal structures compared to legacy portfolios. This chapter explores how these two variables, manager skill and market timing, are redefining the risk-return profile for institutional LPs. Capital flows and deployment trends Despite a broader macro environment characterized by caution, the movement of capital in the fourth quarter suggests a market that is consolidating rather than retreating. Fundraising remains highly concentrated among a few dominant players, with Blue Owl leading the cohort by securing over $1.8 billion in new commitments. This concentration indicates that LPs are gravitating toward proven platforms with the scale to navigate complex market conditions. On the deployment side, the data reveals a “cautious yet constructive” stance. While overall investment activity remains measured, nearly a third of the funds in our universe increased their deployment pace relative to capital commitments this quarter. This selective bullishness is particularly evident in specialized sub-sectors such as Technology Finance and Asset-Based Lending, where GPs are finding attractive entry multiples and robust structural protections. This final chapter examines the shift from broad-based lending to niche, high-conviction deployment strategies that are likely to dominate the 2025 landscape. --- ### [State of Private Credit performance Q1 2025](https://cardoai.com/reports/state-of-private-credit-performance-q2-2025/) **Published:** June 3, 2025 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [A market in recalibration](#first-par) [Performance analysis](#second-par) [The rise of new Vintages](#third-par) [Deployment and fundraising](#fourth-par) As private debt momentum shifts into 2025, the market is grappling with a critical question: Is current performance the “new normal” or a byproduct of late-cycle conditions? This sixth edition of the Private Credit Quarterly analyzes 56 closed-end funds from industry giants, including **KKR, Blackstone, Blue Owl, Ares, and Carlyle,** to examine fund performance, capital flows, and rate-cycle sensitivities A market in recalibration Private credit remains a vital allocation for institutional portfolios, offering risk-adjusted returns and income stability amidst ongoing macroeconomic uncertainty. In Q1 2025, the market demonstrated a strategic shift toward selectivity. While core metrics remain resilient, the weighted average Net IRR across our sample dipped slightly to **9.31%**, a 14-basis point decline from the previous quarter. Despite this minor compression, the median IRR held steady at **10%**, signaling that the “middle of the market” remains robust even as managers and investors recalibrate their return expectations for the year ahead. Performance analysis The data reflects a growing convergence in fund outcomes, with the return dispersion (interquartile range) tightening to 21.3%. A key driver of performance continues to be the “Vintage Gap,” though the spread between pre- and post-2021 funds narrowed to 4.4 percentage points. - **The leaders:** Top-tier funds averaged a Net IRR of 15.6%, led by KKR’s Lending Partners IV and Ares SDL II Levered. - **The Vintage divide:** Post-2021 funds continue to show strong outperformance; notably, KKR’s newer vehicles outperformed its legacy pre-2021 funds by a massive 11.2 points. Conversely, persistent underperformance remains concentrated in specific legacy strategies, with the KKR Special Situations Fund (-4.3%) continuing to anchor the bottom of the table. The rise of new Vintages A dominant theme this quarter is the regaining momentum of newer fund vintages. The performance spread between post-2021 and pre-2021 funds has re-expanded to 4.6%, driven primarily by the high-interest-rate environment at inception. - **Top performers:** For the first time in over a year, leadership has rotated. Brookfield’s Oaktree Opportunities Fund XII (23% Net IRR) and Ares SDL III Levered (19.2%) now lead the cohort, while the average IRR for top-quartile funds jumped to 21%. - **GP rankings**: Brookfield has claimed the top spot with a 14.1% weighted average Net IRR, overtaking TPG Angelo Gordon for the first time in seven quarters. Conversely, legacy stress remains concentrated in the bottom quartile, with the KKR Special Situations Fund (-4.1%) remaining the market’s primary laggard. Deployment and fundraising Capital flows this quarter highlight a period of LP caution, with only 10% of eligible funds attracting new commitments. Blue Owl once again dominated the fundraising landscape, raising over $1.5 billion through its Credit Income Corp. Deployment followed a similarly concentrated pattern; while the broader market remains cautious, four major GPs, led by Blue Owl ($1.3 billion) and TPG ($732 million), recorded significant increases in portfolio investments. This activity highlights a “bifurcated” market where high-conviction managers are aggressively pursuing targeted opportunities while the rest of the field maintains a more defensive, wait-and-see stance. --- ### [Asset-Based Lending Playbook Q1 Scorecard - Q2 Outlook](https://cardoai.com/reports/asset-based-lending-playbook-q1-scorecard-q2-outlook/) **Published:** April 23, 2025 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Market patterns and “hidden” correlations](#first-par) [Anticipated deal archetypes for Q2](#second-par) [Dynamic monitoring](#third-par) Market activity in Q1 2025 has set a high-velocity stage for the months ahead. With a “deal tape” exceeding 200 transactions, the market is bifurcating between mega-facilities (≥ $500M), which account for 40% of total dollars, and a hyper-active mid-market ($100M–$400M) growth zone. This report analyzes these patterns to provide a strategic roadmap for lenders navigating warehouse take-outs, the energy transition, and emerging healthcare distress. Market patterns and “hidden” correlations Deal structures are mapping cleanly to size, while sector gateways emerge. Analysis of over 200 deals reveals that structure is now dictated by ticket size: mega-deals are dominated by syndicated revolvers, while the sub-$50M segment is almost exclusively asset-based revolvers and factoring. We are observing “sector gateway” behavior, where repeat lead arrangers like First Citizens and Percent are clustering around storage mandates and SMB-lender lines. Notably, fintech warehouse lines are now leading directly to ABS warehousing mandates with the same bank groups, creating a “coiled spring” for Q2 term-outs. Anticipated deal archetypes for Q2 Following the $4B+ in fresh warehouses printed in Q1, platforms are teed up to term-out collateral to lock in spreads and ratings before year-end. We expect: - Energy transition: A “rinse-and-repeat” cycle of $50M–$150M construction-to-term loans for battery storage and distributed energy. - Digital infrastructure: $150M–$500M equipment-lease lines for GPU leasing and data-center REITs as AI CAPEX becomes a balance-sheet theme. - Retail refinancings: A queue of 2022-vintages reaching three-year maturity, pushing borrowers toward $75M–$300M revolvers with accordion options. Dynamic monitoring To maintain a competitive edge in Q2, lenders must move beyond manual spreadsheets. As Q1 mid-market borrowers increasingly requested growth add-ons within 60 days, baking expansion grids into upfront documentation is critical to capturing “upsize” revenue. Cardo AI provides the integrated operating stack to support this velocity, enabling API-driven data pushes for near-real-time borrowing-base redeterminations. This automated loop ensures tighter advance control and lower loss-given-default across even the most complex high-turnover portfolios. --- ### [US Auto Loan Q2 2025](https://cardoai.com/reports/us-auto-loan-q3-2025/) **Published:** August 23, 2025 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Securitization trends](#first-par) [Risk-adjusted performance leaders](#third-par) The US auto loan sector entered a cautious phase in Q2 2025, with securitization volumes moderating to $34 billion, a 26% decline from the previous quarter. Despite improving credit fundamentals and recovery rates, investors are adopting a “quality-first” stance amid macroeconomic uncertainty. This report leverages Cardo AI’s proprietary data to evaluate the shifting risk-reward landscape across prime and captive lender portfolios. Securitization trends Not all portfolios are yielding positive net returns in the current environment. Our analysis categorizes originators by their APR-to-loss ratio: - **High-yield/high-risk:** Originators like Exeter and SC Bank command APRs of 20–23%, but elevated 11% loss rates significantly erode net returns. - **Moderate-risk leaders:** GM Financial and CBS occupy the “sweet spot,” balancing 10–12% APRs with superior delinquency management. - **Low-risk stability:** Captive lenders like Ford Credit maintain conservative profiles with losses under 1%, while HCA shows emerging yield potential. Risk-adjusted performance leaders Using a proprietary risk-to-reward metric (default rate vs. time-adjusted return), CBS and GM Financial emerge as the market leaders. Statistical testing confirms that both firms consistently outperform their peers on a risk-adjusted basis, with no significant performance gap between them. For institutional investors, these originators represent the most resilient candidates for strategic allocations in the current auto loan ABS cycle. --- ### [US Auto Loan Q2 2024](https://cardoai.com/reports/us-auto-loan-q2-2024/) **Published:** September 23, 2024 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Volume and investor demand](#first-par) [Default and delinquency trends](#second-par) [Strategic investment opportunities](#third-par) The US auto loan market experienced a period of transition in Q2 2024. While credit indicators like losses and delinquencies showed slight increases, the sector maintained a resilient outlook. This report, supported by Cardo AI Market Intelligence, explores how continued strong demand for securitization is balancing a more moderate credit environment, providing a data-driven view of the latest performance trends. Volume and investor demand Securitization volumes exceeded $30 billion for the seventh consecutive quarter, confirming the asset class’s status as a staple for institutional liquidity. Although volumes saw a 17% decline from Q1, this pullback primarily reflects strategic caution regarding recent default trends. High-quality, investment-grade notes continue to dominate, with narrowing spreads across all categories signaling a persistent appetite for auto loan exposure. Default and delinquency trends While the 2022-23 vintages exhibited higher default rates, the 2024 cohort is showing signs of stabilization. Subprime delinquencies for 2023 improved by 5 percentage points over 2022, with 2024 originations improving by an additional 1.5%. Cumulative defaults remain well-contained, capped at 6% for subprime and a negligible 0.25% for prime vintages, supporting a positive outlook for the asset class. Strategic investment opportunities Our analysis identifies four key areas for high-conviction allocation: - Prime resilience: Prime loans offer risk levels 20 times lower than subprime, with recovery rates 14 points higher and average interest rates exceeding 10%. - The captive advantage: For the third straight quarter, captives outperformed banks in the high-yield (>14%) segment, with default rates two times lower and 10% higher collection efficiency. - Recent Bank & Captive Vintages: H1 2024 vintages show improved quality in transition matrices while offering interest rates 1.5% higher than older cohorts. - New car portfolios: Post-Q2 2022 new car loans with >14% rates carry half the default risk of used car loans and offer returns at least twice the default risk. --- ### [State of Private Credit performance Q2 2024](https://cardoai.com/reports/state-of-private-credit-performance-q2-2024/) **Published:** September 23, 2024 **Author:** RobertaCardo26 **Excerpt:** Loan-level analytics and market insights tracking credit, funding, and structural dynamics across U.S. auto ABS. **Content:** Contents [Market resilience and normalization](#first-par) [Performance analysis](#second-par) [The rise of new Vintages](#third-par) [Scale and selective growth](#fourth-par) As the asset class moves deeper into a “higher-for-longer” environment, private credit is transitioning from rapid repricing toward market normalization. This eighth edition of the Private Credit Quarterly analyzes 52 funds from top-tier GPs, including **KKR, Blackstone, Ares, and Blue Owl,** to reveal a landscape defined by strengthening headline performance and widening dispersion. Market resilience and normalization Private credit continues to serve as a strategic anchor for institutional portfolios, offering defensive yield as credit cycles mature. In Q3 2025, the market showed notable strength as the weighted average Net IRR rose to 9.54%, marking a 40-bps improvement quarter-over-quarter. Despite broader market volatility, the median Net IRR held firm at 10%, signaling enduring stability in core fund performance. However, this stability masks a significant shift in dispersion; the interquartile range compressed by 900 bps to 27%, suggesting a convergence in realized outcomes as valuation methodologies become more disciplined across the industry. Performance analysis A defining development this quarter is the “Vintage Advantage,” where funds launched in the higher-rate environment continue to significantly outperform earlier vehicles. Post-2021 funds boast a 13.2% weighted average Net IRR, creating a 4.9-percentage-point spread over pre-2021 funds (8.3%). This gap reflects the structural benefit of loans originated at higher spreads and floating-rate coupons following the 2022 interest-rate shock. Among newer vintages, Carlyle leads with a 17% Net IRR, followed by Ares (14.3%) and Brookfield (14%). Conversely, the lowest-performing vehicles remain concentrated in older strategies, such as KKR Special Situations Fund (-4.1%), illustrating a persistent gap between successful modern strategies and underperforming legacy funds. The rise of new Vintages A dominant theme this quarter is the regaining momentum of newer fund vintages. The performance spread between post-2021 and pre-2021 funds has re-expanded to 4.6%, driven primarily by the high-interest-rate environment at inception. - **Top performers:** For the first time in over a year, leadership has rotated. Brookfield’s Oaktree Opportunities Fund XII (23% Net IRR) and Ares SDL III Levered (19.2%) now lead the cohort, while the average IRR for top-quartile funds jumped to 21%. - **GP rankings**: Brookfield has claimed the top spot with a 14.1% weighted average Net IRR, overtaking TPG Angelo Gordon for the first time in seven quarters. Conversely, legacy stress remains concentrated in the bottom quartile, with the KKR Special Situations Fund (-4.1%) remaining the market’s primary laggard. Scale and selective growth Capital flows and deployment activity have become increasingly polarized. While most managers are exhibiting outright caution, Blue Owl continues to dominate the landscape, leading for the sixth consecutive quarter with $1.75B raised and $1.3B in new assets deployed. Beyond this outlier, deployment remains highly selective; only six funds across the entire dataset meaningfully increased their portfolio exposure this quarter. This shift toward high-conviction, targeted deployment—led by funds like Blackstone’s Senior Direct Lending I (+16% invested)—proves that scale and sourcing advantages are now the deciding factors in maintaining growth during a period of general market restraint. --- ## Resource ### [Data Profiling and AI: Ensure the quality of your data](https://cardoai.com/resource/data-profiling-and-ai-ensure-the-quality-of-your-data/) **Published:** January 21, 2026 **Author:** sviluppatore --- ### [Maximize integrity in ABF transactions with our new Data Validation](https://cardoai.com/resource/maximize-integrity-in-abf-transactions-with-our-new-data-validation/) **Published:** January 21, 2026 **Author:** sviluppatore --- ### [Data Intelligence Engine: data profiling, with more power](https://cardoai.com/resource/data-intelligence-engine-data-profiling-with-more-power/) **Published:** January 21, 2026 **Author:** sviluppatore --- ## Categories ### [Asset Based Finance](https://cardoai.com/category/asset-based-finance/) --- ### [State of Private Credit](https://cardoai.com/category/state-of-private-credit/) --- ### [US Auto](https://cardoai.com/category/us-auto/) --- ### [Asset Based Lending](https://cardoai.com/category/asset-based-lending/) --- ### [Applied Intelligence](https://cardoai.com/category/applied-intelligence/) --- ### [Data Management](https://cardoai.com/category/data-management/) --- ### [Workflow Management](https://cardoai.com/category/workflow-management/) --- ### [Portfolio & Deal Analytics](https://cardoai.com/category/portfolio-deal-analytics/) --- ### [Asset Classes](https://cardoai.com/category/asset-classes/) --- ### [Deal Modeling](https://cardoai.com/category/deal-modeling/) --- ### [Announcements](https://cardoai.com/category/announcements/) --- ### [Market](https://cardoai.com/category/market/) --- ### [Infrastructure](https://cardoai.com/category/infrastructure/) --- ### [Life at Cardo AI](https://cardoai.com/category/life-at-cardo-ai/) --- ### [Weekly Deals](https://cardoai.com/category/weekly-deals/) ---