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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 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.

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.

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.

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.

Ready to gain deeper insights into your distressed investments? Request a demo here to see the tool in action.