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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 private credit. 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 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

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Private Credit Investors

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Mortgage Prepayment and Credit Trends to Watch

Register here for our next monthly model update call: Thursday, April 17th at 1:00 ET.

Note: This post contains highlights from our March 2025 monthly modeling call. You can register here to watch a recording of the full 28-minute call.

Mortgage and credit markets remain dynamic in early 2025, with macroeconomic conditions driving both volatility and opportunity. In yesterday’s monthly model call, my team and I shared key insights into current market trends, model performance, and what to expect in the coming months.

Market Snapshot: A Mixed Bag

After trending downward in February, mortgage rates ticked up slightly in early March. Despite the fluctuation, expectations are for rates to remain relatively stable until at least summer 2025. Most mortgage-backed securities (MBS) are still deeply out of the money, making housing turnover—not rate refinancing—the dominant prepayment driver.

Macroeconomic signals remain mixed. While unemployment is still low and wage growth continues, inflation shows signs of persistence. The Fed is expected to hold the Fed Funds Rate steady through mid-year, with a potential first cut projected for June. Credit usage is creeping higher—especially in second liens and credit cards—hinting at growing consumer debt stress.

Model Performance and Updates

Prepayment Model

RiskSpan’s prepayment model continues to track closely with actuals across Fannie Mae, Freddie Mac, and Ginnie Mae collateral. The model shows:

  • Prepayments rising slightly, particularly among 2023 vintage loans in response to rate moves.
  • Delinquent loan behavior providing rich insights: For “out of the money” (OTM) collateral, delinquent loans are showing higher turnover speeds than performing ones, as borrowers try to avoid foreclosure.
  • Turnover sensitivity to borrower FICO scores is especially pronounced for delinquent loans—highlighting the need for granular credit analytics.

These behavioral insights are informing the next version of our prepayment model, which will incorporate GSE data research to enhance forecast accuracy.

Credit Model v7: A Leap Forward

RiskSpan’s new Credit Model v7—now available—is a significant upgrade, built on a delinquency transition matrix framework. This state-transition approach enables monthly projections of:

  • Conditional Default Rates (CDR)
  • Conditional Prepayment Rates (CPR)
  • Loss severity and liquidated balances
  • Scheduled and total principal & interest (P&I)

The model’s core components include:

  • A vector-based severity model
  • A robust liquidation timeline module
  • Loan-level outputs by delinquency state (including foreclosure and REO)

By modeling the lifecycle of loans and MSRs more explicitly, Credit Model v7 delivers deeper insight into portfolio credit performance, even in volatile markets.

Emerging Risks and Opportunities

Consumer credit balances—especially HELs and HELOCs—have grown significantly, fueled in part by debt consolidation. Credit card utilization has jumped from 22% in 2020 to nearly 30% as of late 2024, indicating growing financial strain.

Meanwhile, delinquencies in the Non-QM space (2022-2023 vintages) are rising—suggesting that investors need enhanced tools to monitor and manage these risks. RiskSpan’s tools, including the enhanced credit model and daily prepay monitoring, help investors keep pace with these shifting dynamics.

Looking Ahead

RiskSpan’s modeling team remains focused on:

  • Continuing to improve prepayment modeling with newly available GSE data
  • Rolling out and enhancing Credit Model v7 for broader use cases
  • Providing clients with forward-looking analytics to anticipate credit stress and capitalize on market dislocations

Be sure to register for next month’s model update call on Thursday, April 17th at 1:00 ET.

Want a deeper dive into the new Credit Model or Prepay insights? Contact me to schedule a session with our modeling experts.

Get a Demo or Free Trial of RiskSpan’s Platform

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

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February 2025 Model Update: Mortgage Prepayment and Credit Trends to Watch

Note: This post contains highlights from our February 2025 monthly modeling call. You can register here to watch a recording of the full call (approx. 25 mins).

As we move further into 2025, key trends are emerging in the mortgage and credit markets, shaping risk management strategies for lenders, investors, and policymakers alike. RiskSpan’s latest model update highlights critical developments in mortgage prepayments, credit performance, and consumer debt trends—offering valuable insights for investors, traders, and portfolio/risk managers in these spaces.

Prepayment speeds have continued to decline in Q1 2025, largely due to a lack of housing turnover and persistently high mortgage rates. While a drop in rates during Q3 2024 temporarily mitigated lock-in effects for borrowers with very low rates, MBS speeds remain low across most cohorts.

Key drivers of observed prepayment behavior include:

  • Mortgage rates are expected to stay high (~6.5%+) throughout 2025, keeping refinancing activity muted.
  • Turnover remains the primary driver of prepayments, with most MBS pools significantly out of the money.
  • RiskSpan’s Prepayment Model v3.7 effectively captures these dynamics, particularly the impact of deep out-of-the-money (OTM) speeds based on moneyness.

Growth in Non-QM and Second Lien Originations

The private credit market continues to expand, with increasing Non-QM and second lien originations. However, a concerning delinquency trend has emerged, with delinquencies among 2022-2023 Non-QM vintages now rising faster than among older vintages.

Consumer Debt Pressures Mounting

Consumer debt continues to rise rapidly, raising concerns about long-term credit performance:

  • Credit card balances have increased significantly, with utilization climbing from 22% in 2020 to 30% by late 2024.
  • More consumers are turning to personal loans for debt consolidation, a sign of financial strain.
  • Second liens (HEL/HELOCs) are being used to pay off high-interest debt, fueled by strong home equity growth since 2020.

Model Enhancements

To address these evolving market conditions, RiskSpan has rolled out key enhancements to its mortgage and credit models:

  • Prepayment Model v3.7 – Captures deep out-of-the-money lock-in effects with improved accuracy across Fannie, Freddie, and Ginnie collateral.
  • Credit Model v7 – Introduces a Delinquency Transition Matrix, providing more granular forecasting for loans and MSR valuation.
  • Non-QM Prepayment Model – Developed using CoreLogic data, offering improved prepayment insights for Non-QM loans.

Looking Ahead

  • Rates are likely to remain high, with no reductions expected before summer.
  • Home equity growth remains strong, driving continued second lien origination.
  • Debt servicing costs are beginning to strain consumers, as high interest rates persist.
  • Delinquency rates show strong correlation to credit quality, signaling potential risks ahead.

The evolving mortgage and credit landscape underscores the importance of robust modeling and risk assessment. With prepayments slowing, debt burdens rising, and consumer credit trends shifting, lenders and investors must adapt their strategies accordingly.

Non-QM Delinquencies Are Rising—And Home Prices Aren’t Helping 📉

The non-QM mortgage market is showing clear signs of stress, and the latest delinquency data confirms it. RiskSpan analysis shows 60+ day delinquencies are rising, with 2022 and 2023 vintages deteriorating faster than prior years. Non-Qualified Mortgages (Non-QM) are loans that don’t meet traditional underwriting guidelines and often include self-employed borrowers, investors, and those with alternative income documentation.

What’s Driving the Spike?

Sustained higher mortgage rates have created pressure for some non-QM borrowers with fewer refinancing options. A more granular analysis shows loan attributes and risk layering driving high delinquencies, particularly those with cashout refi as the loan purpose. In a slowing home price appreciation (HPA) environment, borrowers who took out cash-out refis may be struggling with payment shock and limited home equity growth.

But the Real Problem? The Changing Housing Market.

Since the Covid crisis, many believed low housing inventory would keep prices elevated, but not anymore. The Wall Street Journal reported last week that housing inventory rose by 16% compared to the previous year. Further, the Federal Reserve Economic Data (FRED) shows 2024 HPA at just +3.5%, the slowest since 2020 with certain MSAs declining.  Florida remains its own unique case, while DC faces recession fears following recent Trump policy changes. 2025 looks even weaker – WS research projects HPA at just +2.5%, signaling even slower home price growth ahead.

The Risk: What Comes Next?

Slower home price growth means reduced equity cushions and borrowers with less ability to absorb financial shocks. This means refinancing and selling become less viable options leading to rising delinquencies & liquidity concerns. The markets could certainly stabilize or non-QM delinquencies could continue their upward climb.

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

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Loans & MSRs: Managing model assumptions and tuners the easy way

One of the things that makes modeling loan and MSR cash flows hard is appropriately applying assumptions to individual loans. Creating appropriate assumptions for each loan or MSR segment is crucial to estimating realistic performance scenarios, stress testing, hedging, and valuation. However, manually creating and maintaining such assumptions can be time-consuming, error-prone, and inconsistent across different segments and portfolios.

Fortunately, hidden among some of the Edge Platform’s better-known features is a powerful and flexible way of running loan-level analytics on a portfolio using the Platform’s segment builder and loan model assumptions features.

These sometimes-overlooked features allow users to create and apply granular and customized modeling assumptions to a particular loan portfolio, based on its various, unique loan characteristics. Assumptions can be saved and reused for future analysis on different loans tapes.  This feature allows clients to effectively build and manage a complex system of models adjustment and tuners for granular sub-segments.

Applying the segment builder and loan model assumptions features, loan investors can:

    • Decouple how they run and aggregate results from how they assign modeling assumptions, and seamlessly assign different assumptions to various segments of the portfolio, based on user-defined criteria and preferences. For example, investors can assign different prepayment, default, and severity assumptions to loans based on their state, LTV, UPB, occupancy, purpose, delinquency status, loan type, collateral features, or virtually any other loan characteristic.

 

    • Choose from a variety of models and inputs, including RiskSpan models and vector inputs for things like CPR and CDR. Investors can define their own vector inputs as an aging curves by loan age or based on the forecast month, and apply them to different segments of the portfolio. For example, they can define their own CDR and CPR curves for consumer or C&I loans, based on the age of the loans.

    • Set up and save modeling assumptions one time, and then reference them over and over again whenever new loan tapes are uploaded. This saves time and effort and ensures consistency and accuracy in the analysis.

This hidden feature enables investors to customize their analysis and projections for different asset classes and scenarios, and to leverage the Edge Platform’s embedded cash flow, prepayment and credit models without compromising the granularity and accuracy of the results. Users can create and save multiple sets of loan model assumptions that include either static inputs, aging curves, or RiskSpan models, and apply them to any loan tape they upload and run in the forecasting UI.

Contact us and request a free demo or trial to learn more about how to use these and other exciting hidden (and non-hidden) features and how they can enhance your loan analytics.

CONTACT US

Enriching Pre-Issue Intex CDI Files with [Actual, Good] Loan-Level Data

The way RMBS dealers communicate loan-level details to prospective investors today leaves a lot to be desired.

Any investor who has ever had to work with pre-issue Intex CDI files can attest to the problematic nature of the loan data they contain. Some are better than others, but virtually all of them lack information about any number of important loan features.

Investors can typically glean enough basic information about balances and average note rates from preliminary CDI files to run simple, static CPR/CDR scenarios. But information needed to run complex models — FICO scores, property characteristics and geography, and LTV ratios to name a few — is typically lacking. MBS investors who want to run to run more sophisticated prepayment and credit models – models that rely on more comprehensive loan-level datasets to run deeper analytics and scenarios – can be left holding the bag when these details are missing from the CDI file.

The loan-level detail exists – it’s just not in the CDI file. Loan-level detail often accompanies the CDI file in a separate spreadsheet (still quaintly referred to in the 21st Century as a “loan tape”). Having this data separate from the CDI file requires investors to run the loan tape through their various credit and prepayment models and then manually feed those results back into the Intex CDI file to fully visualize the deal structure and expected cash flows.

This convoluted, multi-step workaround adds both time and the potential for error to the pre-trade analytics process.

A Better Way

Investors using RiskSpan’s Edge Platform can streamline the process of evaluating a deal’s structure alongside the expected performance of its underlying mortgage loans into a single step.

EDGEPLATFORM

Here is how it works.

As illustrated above, when investors set up their analytical runs on Edge, RiskSpan’s proprietary credit and prepayment models automatically extract all the required loan-level data from the tape and then connect the modeling results to the appropriate corresponding deal tranche in the CDI file. This seamlessness reduces all the elements of the pre-trade analytics process down to a matter of just a few clicks.

Making all this possible is the Edge Platform’s Smart Mapper ETL solution, which allows it to read and process loan tapes in virtually any format. Using AI, the Platform recognizes every data element it needs to run the underlying analytics regardless of the order in which the data elements are arranged and irrespective of how (or even whether) column headers are used.

Contact us to learn more about how RMBS investors are reaping the benefits of consolidating all of their data analytics on a single cloud-native platform.

LEARN MORE

RiskSpan to Launch Usage-based Pricing for its Edge Platform at SFVegas 2024 

New innovative pricing model offers lower costs, transparency, and flexibility for analytics users 

RiskSpan, a top provider of cloud-based analytics solutions for loans, MSRs, structured products and private credit, announced today the launch of a usage-based pricing model for its Edge Platform. The new pricing model enables clients flexibility to pay only for the compute they use. It also gives clients access to the full platform, including data, models, and analytics, without having to license individual product modules. 

Usage-based pricing is a trend that reflects the evolving nature of analytics and the increasing demand for more flexible, transparent, and value-driven pricing models. It is especially suited for the dynamic and diverse needs of analytics users, whose data volumes, usage patterns, and analytical complexity requirements often fluctuate with the markets.

RiskSpan was an early adopter of the Amazon Web Services (AWS) cloud in 2010. Its new usage-based pricing, powered by the AWS cloud, enables RiskSpan to invoice its clients based on user-configured workloads, which can scale up or down as needed. 

“Usage-based pricing is a game-changer for our clients and the industry,” said Bernadette Kogler, CEO of RiskSpan. “It aligns our pricing with the value we deliver and the outcomes we enable for our clients. It also eliminates the waste and inefficiency of paying for unused, fixed-fee compute capacity, year after year in long-term, set price contracts. Now our clients can optimize their spending while experimenting with all the features our platform has to offer.”

“We are excited RiskSpan chose AWS to launch its new pricing model. Our values are aligned in earning trust through transparent variable pricing that allows our customers to innovate and remain agile.” said Ben Schreiner, Head of Business Innovation, at Amazon Web Services. “By leveraging the latest in AWS technology, including our generative AI services, RiskSpan is accelerating the value they deliver to their customers, and ultimately, the entire financial services industry.”

Usage-based pricing offers several benefits for RiskSpan clients, including: 

  • Lower Costs: Clients pay only for what they need, rather than being locked into an expensive contract that may not suit their current or future situation. 
  • Cost Sharing: Clients can share costs across the enterprise and better manage expense based on usage by individual functions and business units. 
  • Transparency: Clients can monitor their usage and directly link their analytics configuration and usage to their results and goals. They can also better control their spending by tracking their usage and seeing how it affects their bill. 
  • Flexibility: Clients can experiment with different features and options of RiskSpan’s Edge Platform, as they are not restricted by a predefined package or plan. 

For a free demo, visit https://riskspan.com/ubp/.

### 

About RiskSpan, Inc. 

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With an unparalleled team of data science experts and technologists, RiskSpan is the leader in data as a service and end-to-end solutions for loan-level data management and analytics.

Its mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments. Learn more at www.riskspan.com.

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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