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Models & Markets Update – May 2025

Register here for next month’s call: Thursday, June 19th, 2025.

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month, we spotlighted model backtesting updates, macroeconomic conditions, and market analytics that are shaping investment strategies across loans, securities, and private credit.

Click here to listen to the entire 23-minute recording, or continue reading for a summary.

Prepayment Model Performance and Enhancements

RiskSpan’s prepayment model continues to exhibit strong alignment with observed data across all coupon cohorts. During the call, we showcased updated backtesting results for 2022 FN/FH cohorts across multiple coupon bands (1.5s–3.5s, 4.0s–5.5s, and 6.5s), revealing that projected vs. actual CPRs remain closely correlated, even in volatile rate environments.

Additionally, RiskSpan has introduced a Non-QM-specific prepayment model to address behavioral differences in this segment. This is particularly timely, given elevated delinquency trends discussed later in the session.

Our recently enhanced Credit Model 7, leveraging a delinquency transition matrix, is expected to be released by the end of May and will provide a more granular view of credit migration patterns.

Spread at Origination: A Key Risk Signal

Spread at Origination (SatO), the difference between the borrower’s rate and the prevailing PMMS rate at application, is emerging as a critical predictor of refinance activity. Lower SatO values suppress prepayments even in pools with favorable coupons.

Using MBS loan-level data, we illustrated how SatO dynamics impact investor vs. owner-occupied loans, with notable geographic variation. States like CA, FL, and NY show materially different average rates for investor loans, independent of LLPA effects.

As a forward-looking initiative, we are developing a generalized spread model that isolates residual pricing differences not explained by known borrower or loan characteristics. This could further enhance predictive power by benchmarking loans against peer cohorts defined by origination date, FICO, occupancy, and geography.

Macroeconomic Outlook: Sticky Rates and Stable Housing

The economic backdrop remains mixed:

  • Mortgage rates hover around 6.95%, with no near-term relief in sight.
  • The Fed Funds Rate is projected to stay elevated, with the first potential cut not expected until September 2025. Even then, consensus suggests only a modest decline to 3.75–4.00% by year-end.
  • Home prices are largely stable, as reflected in the Case-Shiller Index. Year-over-year appreciation remains positive but muted.
  • Unemployment stands at 4.0%, and inflation is moderating but still above target.

This persistent high-rate environment will continue to dampen refinance activity and challenge affordability, reinforcing the importance of modeling spread-driven behavior accurately.

Non-QM Delinquencies Spike

The bad news: Delinquencies are surging within the Non-QM sector, particularly for 2022–2023 vintages:

  • DSCR/investor loans are showing delinquency rates an order of magnitude higher than conventional loans.
  • This reinforces the need for robust credit modeling, especially in the private credit space where standard agency risk buffers don’t apply.

The good news: RiskSpan’s new NonQM credit and prepay models are now live to support more accurate surveillance of these exposures.

Contact us to learn more or to request a free demo of our platform and models.

Using LLMs as judges for validating deal cash flow models: A new frontier in securitization modeling

As securitization models become increasingly complex and differentiated, validation becomes a critical challenge. We’ve experimented with an innovative approach that leverages large language models (LLMs) as impartial judges to validate models implemented across different platforms.

The Dual-Implementation Challenge

In cash flow modeling, we often maintain parallel implementations—typically in Python for flexibility and Excel for transparency. How do we ensure both versions produce consistent results?

Enter the “LLM as Judge” approach!

A Real-World Case Study: Residential Transition Loan Funding

Consider a portfolio of residential transition loans with a funding structure including:

  • 100 loans averaging $275,000 each
  • 12-month average terms at 8.75%
  • A 75% advance rate
  • 2% loss reserve build-up
  • Performance triggers based on delinquency rates

We implemented this structure in both Python and Excel, then submitted both models to an LLM for validation.

The LLM Validation Process

The LLM first analyzed the conceptual alignment between models, confirming both followed the same fundamental approach to cash flow projection, default assumptions, reserve mechanics, and triggers.

Next came a rigorous numerical comparison. The LLM detected a $100,000 investor distribution discrepancy in Month 2:

  • Python model: $1,790,702
  • Excel model: $1,690,702

Through logical analysis, the LLM determined this likely stemmed from differently evaluated trigger conditions. This kind of subtle implementation difference could easily go unnoticed in manual validation, potentially leading to significant valuation discrepancies over time.

Beyond Discrepancy Detection

The true power of this approach extends beyond finding differences. The LLM also provided:

  1. Stress testing recommendations tailored to our specific product, including scenarios for rapid defaults, extension waves, and interest rate shocks
  2. Model risk management insights highlighting documentation needs and suggesting a formal reconciliation process
  3. Code quality assessment noting strengths and weaknesses in both implementations

Why This Matters

For securitization professionals, this approach offers several advantages:

  • Efficiency: Automation of tedious line-by-line comparisons
  • Comprehensiveness: Identification of conceptual differences, not just numerical ones
  • Regulatory compliance: Better documentation for model risk management requirements
  • Objectivity: Unbiased third-party perspective

Contact us to discuss.

Insurance Solutions

Insurance Solutions

Unlock the power of your portfolio with unified data, AI-driven analytics, and deep risk insights

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What's holding your portfolio back?

Managing diverse portfolios across asset types and jurisdictions is hard. Data is scattered. Surveillance is manual. Risk and investment decisions are disconnected.

RiskSpan changes that.

Our end-to-end platform gives insurers the tools to streamline operations, stay compliant, and optimize returns across public and private credit.

What we solve

Fragmented Data. Unified.

The Problem: Inconsistent, siloed data slows decisions and creates risk.
Our Fix: RiskSpan’s platform ingests, normalizes, and connects all your data—from loans and bonds to private credit and structured products.

  • Clean data. Cloud-native. Instantly available.

  • Snowflake and Databricks-ready.

Compliance Without the Headache

The Problem: Managing RBC, CECL, and global regulatory requirements is complex and time-consuming.
Our Fix: Multi-framework regulatory and capital modeling, audit-ready CECL tools, and scenario-based stress testing.

  • SOC 1 and SOC 2 certified

  • Full coverage of loans and securities

Surveillance that Saves Time

The Problem: Manual Excel processes delay insight and cost money.
Our Fix: Automated surveillance, real-time pricing, and stress testing—all in one platform.

  • Save 3 weeks/month and $1.5MM/year

  • Real-time dashboards and alerts

Smarter Scenarios Across All Assets

The Problem: No way to consistently assess risk across asset classes.
Our Fix: Scenario and risk analytics—tailored to your portfolio and powered by macro data from S&P and beyond.

  • VaR, tail risk, correlation, credit risk

  • Loan-level granularity. Climate and geopolitical risk ready.

One Connected Investment + Risk Workflow

The Problem: Investment and risk teams use different tools and speak different languages.
Our Fix: One platform for portfolio surveillance, trade support, ALM, and pricing.

  • Pre-trade credit memos through post-trade surveillance

  • API, dashboard, and reporting access for every team

“We went from spreadsheet chaos to a real-time view of our private deals. Closed more deals, with better risk controls.”
Head of Investment Risk
55B AUM Life Insurer

What's under the hood

  • AI-Powered Document Parsing & Deal Modeling

  • End-to-End CECL Processing

  • Regulatory Reporting Engine

  • Cross-Asset Stress Testing

  • SOC 2 Secure + Cloud Native

  • Easy API + Web UI Access

AI-Driven Data Extraction & Structuring

Turn Unstructured Deal Data into Actionable Intelligence

  • Automated document processing extracts key terms, conditions, and structural details from loan and deal documents.
  • AI-powered data validation minimizes human error and ensures accuracy in portfolio analytics.
  • Standardized data models integrate with Snowflake for seamless analysis.
  • Extracted deal structures, waterfalls, triggers, and covenants drive accurate cashflow modeling, portfolio surveillance, and reporting.

Advanced Cash Flow Modeling for Private ABF Portfolios

Scalable, Customizable AI-Powered Cash Flow Analytics

  • AI-generated open-source cash flow modeling provides a customizable starting point for deal structuring.
  • Custom security ID integration ensures seamless tracking in RiskSpan’s Edge Platform.
  • Scenario-based forecasting & pricing analytics deliver insights tailored to private credit portfolios.
  • Automated API access streamlines portfolio monitoring and cashflow analysis.

Private ABF Portfolio Risk & Surveillance

Comprehensive Risk Management & Real-Time Monitoring

  • Run daily pricing & risk analytics across public and private assets in a single framework.
  • Loan-level risk assessments enhance portfolio granularity and accuracy.
  • Automate covenant tracking & remittance report ingestion to monitor deal performance and triggers in real-time.
  • Custom stress testing & scenario analysis tailored to private credit portfolios.

Why Insurers Choose RiskSpan for Private ABF Analytics

  • Eliminate manual surveillance bottlenecks that delay critical performance insights.

  • Improve loan acquisition & investor reporting workflows with AI-powered automation.

  • Proven success supporting asset managers, insurance firms, and private credit funds.

  • Seamless integration with existing risk management and portfolio reporting frameworks.

Unlock the Power of AI for Private Credit Investing

📩 Contact us today to schedule a demo and streamline your private credit analytics.
🔗 Request a Demo

Product Summary

Introductory Presentation (coming soon)

Model Documentation (coming soon)

Built for Speed, Scale and Affordability

Cloud-Native for 15 Years

Get a Free Trial or Demo

Resources

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Private Credit Investors

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Private Credit: Asset-Backed Finance Analytics

Private Credit:
Asset-Backed Finance Analytics

AI-Powered Surveillance, Data Collection & Cashflow Modeling for Scalable Portfolio Management

Get a free trial or demo

THE PROBLEM: Private credit ABF portfolios are diverse and complex, encompassing various collateral types, structural features, and data formats. Traditional portfolio and risk management workflows remain fragmented and manual, creating inefficiencies that constrain growth.

RiskSpan's ABF Private Credit Solution

Introducing the only end-to-end solution for private credit deal modeling, portfolio surveillance, and risk management, enabling investors to optimize decision-making and scalability.

AI-Driven Data Extraction & Structuring

Turn Unstructured Deal Data into Actionable Intelligence

  • Automated document processing extracts key terms, conditions, and structural details from loan and deal documents.
  • AI-powered data validation minimizes human error and ensures accuracy in portfolio analytics.
  • Standardized data models integrate with Snowflake for seamless analysis.
  • Extracted deal structures, waterfalls, triggers, and covenants drive accurate cashflow modeling, portfolio surveillance, and reporting.

Advanced Cash Flow Modeling for Private Credit Portfolios

Scalable, Customizable AI-Powered Cash Flow Analytics

  • AI-generated open-source cash flow modeling provides a customizable starting point for deal structuring.
  • Custom security ID integration ensures seamless tracking in RiskSpan’s Edge Platform.
  • Scenario-based forecasting & pricing analytics deliver insights tailored to private credit portfolios.
  • Automated API access streamlines portfolio monitoring and cashflow analysis.

Private Credit Portfolio Risk & Surveillance

Comprehensive Risk Management & Real-Time Monitoring

  • Run daily pricing & risk analytics across public and private assets in a single framework.
  • Loan-level risk assessments enhance portfolio granularity and accuracy.
  • Automate covenant tracking & remittance report ingestion to monitor deal performance and triggers in real-time.
  • Custom stress testing & scenario analysis tailored to private credit portfolios.

Why Private Credit Investors Choose RiskSpan

  • Eliminate manual surveillance bottlenecks that delay critical performance insights.

  • Improve loan acquisition & investor reporting workflows with AI-powered automation.

  • Proven success supporting asset managers, insurance firms, and private credit funds.

  • Seamless integration with existing risk management and portfolio reporting frameworks.

Unlock the Power of AI for Private Credit Investing

📩 Contact us today to schedule a demo and streamline your private credit analytics.
🔗 Request a Demo

Product Summary

Introductory Presentation (coming soon)

Model Documentation (coming soon)

Built for Speed, Scale and Affordability

Cloud-Native for 15 Years

Get a Free Trial or Demo

Resources

view all

Private Credit Investors

reuse_tax_query=1 tag=”exclude” operator=”NOT IN”]

The Future of Private Credit: Growth Challenges, and How RiskSpan is Leading the Way

Private credit is having a moment, as they say, now approaching $7 trillion in global assets, and is poised to double in size over the next decade. As traditional banks tighten lending due to regulatory constraints, private credit is stepping in to provide flexible, high-yield investment opportunities for institutional investors. However, this expanding market brings challenges, including illiquidity, bespoke deal structures, and complex risk assessments.

Chartis Research, in collaboration with RiskSpan, explores these evolving dynamics in a recent report, shedding light on the forces shaping private credit’s expansion and the critical role of technology in mitigating risk.

As private credit markets grow, effective risk management is crucial for investors seeking stable returns. Advanced technologies like AI and machine learning are revolutionizing private credit risk assessment, enhancing cash flow modeling, pricing accuracy, and portfolio diversification. RiskSpan leads the industry with innovative solutions, leveraging loan-level data and cloud-based platforms to provide real-time analytics. Whether you’re an asset manager, institutional investor, or lender, understanding the latest private credit trends is essential for success.

Read the full article to explore how private credit is transforming finance and why technology-driven risk management is the key to sustainable growth.

Contact us to learn more about how RiskSpan’s platform can support your private credit analytics.

Loans LP

Loan and Private Credit Investors

Resi | Non-QM | MSR | Consumer | Auto | Commercial

  • Quickly ingest pools, run predictive analytics, and optimize buy/sell strategies.

  • Integrate analytics across front and middle office workflows

  • Leverage historical performance data for better risk management and pricing

  • Connect directly with trading systems

  • Customize and seamlessly integrate into traders’ existing processes

Get a free trial or demo

Product Summary

Introductory Presentation (coming soon)

Model Documentation (coming soon)

Built for Speed, Scale and Affordability

Cloud-Native for 15 Years

Get a Free Trial or Demo

Resources

view all

Private Credit Investors

reuse_tax_query=1 tag=”exclude” operator=”NOT IN”]

Private Credit Primer Series: Insights for Investors

We are delighted to announce the release of RiskSpan’s series of Private Credit Primers aimed at providing investors with essential knowledge about the diverse and growing landscape of the loan types that private credit investors are buying. These primers offer at-a-glance insights into the mechanics, performance expectations, and unique features of various asset classes, enabling investors to make informed decisions in this dynamic market.

The first three primers in the series are available now: They focus on Residential Transition Loans (RTLs), Personal Loans, and HELOCs — loan types that are becoming increasingly popular in the private credit space.

Residential Transition Loans (RTLs) (full primer here)

RTLs are short-term loans designed to help borrowers bridge financial gaps during transitional periods in residential real estate. Often used for construction, bridge, and relocation purposes, RTLs typically have terms ranging from 6 to 36 months and feature higher interest rates than traditional long-term financing. These loans play a crucial role for both homeowners and real estate investors, especially in markets where property values fluctuate or where short-term liquidity is needed.

  • Important Features: RTLs often involve draw functionality, allowing borrowers to access funds incrementally as projects progress. Another key aspect is the use of the “As-Repaired” Value (ARV) to calculate Loan-to-Value (LTV) ratios, based on the projected value of the property after repairs.
  • Performance Considerations: While RTLs have performed well during periods of stable or rising home prices, the primer cautions that these loans are more vulnerable during economic downturns or periods of home price decline​.

Personal Loans (full primer here)

Personal loans can be secured or unsecured and are used for a variety of purposes, including debt consolidation, medical expenses, and large purchases. They are repaid in fixed monthly installments over a predetermined period.

  • Important Features: The primer highlights key modeling considerations for personal loans, such as static default/prepayment assumptions, which rely on historical data to predict future loan performance based on factors like loan age and borrower profiles.
  • Performance Expectations: As of 2024, the delinquency rate for personal loans stands at around 3.38%, with average interest rates hovering around 12.42%, though they can vary widely depending on market conditions and borrower credit quality​.

HELOCs (full primer here)

The complexity of modeling HELOCs stems from their sharing characteristics of both a mortgage and a credit card. Reliable assumptions about borrower behavior both during the draw period (when balances can move in either direction at any time) and during the post-draw, repayment-only period are crucial to forecasting correct cash flows.

  • Important Features: Understanding regional variations and borrower characteristics can provide deeper insights into HELOC performance, helping to refine risk models and lending strategies.
  • Performance Expectations: The delinquency rate for HELOCs can vary based on factors such as economic conditions, borrower credit quality, and market trends. However, historically, the delinquency rate for HELOCs tends to be lower than for unsecured loans or credit cards.

What to Expect from the Series

Each primer in the series will not only break down the mechanics of the loan type but also provide performance insights and modeling considerations. With the ongoing volatility in the financial markets, these primers will explore how various asset classes perform under different economic conditions, such as rising interest rates, declining home prices, or increasing unemployment.

By offering practical, data-driven insights, the Private Credit Primer series will serve as an invaluable resource for private credit investors who are looking to deepen their understanding of these asset classes and navigate potential risks effectively.

Stay tuned for more primers in this series, as we continues to expand RiskSpan’s library of resources for private credit investors!

Enhancing a HELOC Lender’s Operations with RiskSpan’s Data as a Service (DaaS)

A leading fintech company specializing in home equity lines of credit (HELOCs), was seeking to optimize the management of its data operations. To accomplish this, the company turned to RiskSpan, a leader in data analytics and financial technology solutions. Through a tailored Data as a Service (DaaS) offering, RiskSpan helped the company improve its HELOC business operations by providing advanced data management and modeling capabilities.

Challenges

The company sought to enhance its HELOC operations in two critical areas:

  1. Data Management and Integration: The company was dealing with complex data sets from multiple sources, including credit bureaus, property data, and customer behavior insights. Integrating and managing this data effectively was crucial for making informed lending decisions.
  2. Risk Assessment and Modeling: Accurate and reliable risk assessment models were necessary for evaluating customer behavior and predicting loan performance. The company required a solution that could model draw behavior and other variables specific to HELOCs.

RiskSpan’s DaaS Solution

RiskSpan’s DaaS offering provided the company with a comprehensive solution tailored to address these challenges. The key components of the solution included:

  1. Advanced Data Integration: RiskSpan’s DaaS platform seamlessly integrated the company’s various data sources, enabling a more streamlined and efficient data management process. This integration allowed the company to better understand their borrowers and make more informed lending decisions.
  2. Enhanced Loan-Level HELOC Pricing and Projections: The client successfully loaded its historical loan performance data onto RiskSpan’s DaaS platform and established a monthly process within the platform’s flexible data warehouse. Using the embedded historical performance tool, the client analyzed loan-level behavior across its portfolio. This enabled the client to generate detailed collateral performance reports for investors and rating agencies, as well as leverage these insights to enhance future projections and loan-level pricing for new loans.
  3. Cost-Effective Data Services: RiskSpan also identified an opportunity to replace the client’s existing data services provider at a significantly reduced cost. By offering a more competitive pricing structure while maintaining high-quality data services, RiskSpan positioned the client to achieve substantial cost savings, making them more competitive in the HELOC market.

Outcomes and Benefits

Implementing RiskSpan’s DaaS solution brought several key benefits:

  • Improved Decision-Making: With better-integrated data and more accurate modeling of HELOC draw behavior, the client could make more informed lending decisions, ultimately reducing risk and enhancing profitability.
  • Operational Efficiency: The streamlined data management process allowed the client to operate more efficiently, freeing up resources to focus on core business activities.
  • Cost Savings: RiskSpan’s competitive pricing enabled the client to cut costs significantly, improving their bottom line and allowing them to reinvest in other areas of the business.

RiskSpan’s Data as a Service solution provided the clients with the tools it needed to optimize its HELOC business. By addressing its data integration challenges, improving risk assessment through advanced modeling, and offering a cost-effective alternative to existing data services, RiskSpan helped the client strengthen its market position and enhance overall business performance.

What is the Draw of Whole Loan Investing?

Mortgage whole loans are having something of a moment as an asset class, particularly among insurance companies and other nonbank institutional investors. With insurance companies increasing their holdings of whole loans by 35 percent annually over the past three years, many people are curious what it is about these assets that makes them so appealing in the current environment.

We sat down with Peter Simon, founder and CEO of Dominium Advisors, a tech-enabled asset manager specializing in the acquisition and management of residential mortgage loans for insurance companies and other institutional investors. As an asset manager, Dominium focuses on performing the “heavy lifting” related to loan investing for clients. 

Click for Free Access to Whole Loan Trading Market Color Dashboards
on RiskSpan’s Edge Platform

How has the whole loan asset class evolved since the 2008 crisis? How have the risks changed?

Peter Simon: Since 2008, laws and regulations like the Dodd-Frank act and the formation of the Consumer Financial Protection Bureau have created important risk guardrails related to the origination of mortgage products. Many loan and mortgage product attributes, such as underwriting without proper documentation of income or assets or loan structures with negative amortization, which contributed to high levels of mortgage defaults in 2008 are no longer permissible. In fact, more than half of the types of mortgages that were originated pre-crisis are no longer permitted under the current “qualified mortgage” regulations.  In addition, there have been substantial changes to underwriting, appraisal and servicing practices which have reduced fraud and conflicts of interest throughout the mortgage lifecycle.

How does whole loan investing fit into the overall macro environment?

Peter Simon: Currently, the macro environment is favorable for whole loan investing. There is a substantial supply-demand imbalance – meaning there are more buyers looking for places to live then there are homes for them to live in. At the current rates of new home construction, mobility trends, and household formation, it is expected that this imbalance will persist for the next several years.  Demographic trends are also widening the current supply demand imbalance as more millennial buyers are entering their early 30s – the first time-homebuyer sweet spot.  And work from home trends created by the pandemic are creating a desire for additional living space.

Who is investing in whole loans currently?

Peter Simon: Banks have traditionally been the largest whole loan investors due to their historical familiarity with the asset class, their affiliated mortgage origination channels, their funding advantage and favorable capital rules for holding mortgages on balance sheet.  Lately, however, banks have pulled back from investing in loans due to concerns about the stickiness of deposits, which have been used traditionally to fund a portion of mortgage purchases, and proposed bank capital regulations that would make it more costly for banks to hold whole loans.  Stepping in to fill this void are other institutional investors — insurance companies, for example — which have seen their holdings of whole loans increase by 35% annually over the past 3 years. Credit and hedge funds and pension funds are also taking larger positions in the asset class. 

What is the specific appeal of whole loans to insurance companies and these other firms that invest in them?

Peter Simon: Spreads and yields on whole loans produce favorable relative value (risk versus yield) when compared to other fixed income asset classes like corporate bonds.  Losses since the Financial Crisis have been exceptionally low due to the product, process and regulatory improvements enacted after the Financial Crisis.  Whole loans also produce risks in a portfolio that tend to increase overall portfolio diversification.  Borrower prepayment risk, for example, is a risk that whole loan investors receive a spread premium for but is uncorrelated with many other fixed income risks.  And for investors looking for real estate exposure, residential mortgage risk has a much different profile than commercial mortgage risk.

Why don’t they just invest in non-Agency securities?

Peter Simon: Many insurance companies do in fact buy RMBS securities backed by non-QM loans.  In fact, most insurance companies who have residential exposure will have it via securities.  The thesis around investing in loans is that the yields are significantly higher (200 to 300 bps) than securities because loans are less liquid, are not evaluated by the rating agencies and expose the insurer to first loss on a defaulted loan.  So for insurance investors who believe the extra yield more than compensates them for these extra risks (which historically over the last 15 years it has), they will likely be interested in investing in loans.

What specific risk metrics do you evaluate when considering/optimizing a whole loan portfolio – which metrics have the highest diagnostic value?

Peter Simon: Institutional whole loan investors are primarily focused on three risks: credit risk, prepayment risk and liquidity risk. Credit risk, or the risk that an investor will incur a loss if the borrower defaults on the mortgage is typically evaluated using many different scenarios of home price appreciation and unemployment to evaluate both expected losses and “tail event” losses.  This risk is typically expressed as projected lifetime credit losses.  Prepayment risk is commonly evaluated using loan cash flow computed measures like option adjusted duration and convexity under various scenarios related to the potential direction of future interest rates (interest rate shocks).

How would you characterize the importance of market color and how it figures into the overall assessment/optimization process?

Peter Simon: Newly originated whole loans like any other “new issue” fixed income product are traded in the market every day.  Whole loans are generally priced at the loan level based on their specific borrower, loan and property attributes.  Collecting and tabulating loan level prices every day is the most effective way to construct an investment strategy that optimizes the relative differences between loans with different yield characteristics and minimizes credit and prepayment risks in many various economic and market scenarios.

Click for Free Access to Whole Loan Trading Market Color Dashboards
on RiskSpan’s Edge Platform

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