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.

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.