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Non-QM Credit Stress by the Numbers: Investor and Full Doc Loan Performance Diverge

This is a follow-up to Bernadette Kogler’s short piece last month on stress in the Non-QM mortgage market. In this post, I use the CoreLogic Non-Agency loan data to split out the Non-QM population by loan type and look at the relative delinquency performance of mortgages backed by Investor properties vs. loans with full documentation vs. other Non-QM loan types (this last bucket comprises mainly Bank Statement loans).

As the following chart illustrates, the non-performing delinquency rate (60+ dpd loans as a percentage of the overall population) has risen from a post-COVID low of 1.01% to 3.59% as of March 2025. This increase has been driven by deterioration in the credit performance across all Non-QM loan types. Notably, the delinquency rate for Investor loans increased to 3.82% as of March, up more than three-fold from post-COVID lows of 1.1% in October 2022. While they remain the best performing loan type, even the Full Doc loans have seen a doubling of delinquency rate, to 1.11%.

The other driver of the sharp uptick in delinquency rates for the Aggregate Non-QM loan population is a shift in their mix away from the strongly performing Full Doc loans. As shown in the graph below, Full Doc loans as a percentage of the overall NQM mix have fallen from over 50% of NQM population as of the end of 2018 to only 22% in March. Meanwhile, Investor loans have increased from only 3% of the Non-QM population as of the end of 2018 to 10% just before COVID to 28% as of March.

Finally, we look at the gateway transition of mortgages to non-performing status: the current to 30 roll rates, or the percentage of current loans that roll to 30 days delinquent in any given month due to a missed payment. Not surprisingly, these trends are broadly in line with what we see for the overall delinquency rates: roll rates have increased significantly since their late 2022 lows.

But these roll rates give us a more real-time perspective on how different loan types are performing relative to each other than the delinquency rate levels, which represent the cumulative effect of historical performance. In the most recent remittance data, Investor-backed loans experienced a 1.42% C->30 roll rate, which was 2.5x the 0.58% roll rate experienced by Full Doc Non-QM loans. By contrast, that multiple was only 1.8x in October 2022 when NQM loans were experiencing their lowest post-COVID roll rate performance.

Given the deteriorating performance of Non-QM mortgages and backdrop of macroeconomic uncertainty, it is important for investors to monitor their portfolios that have Non-QM exposure. Our credit models at RiskSpan model these delinquency roll rates directly, and our modeling team calibrates our suite of models to capture both the overall trends and the differentiated performance across loan and product types. These models are just one component of our scaled analytics solutions to help our clients evaluate risk and make investment decisions.

Contact me to discuss.

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.

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.

AI-Powered Code Reviews

Our firm recently implemented a pilot that promises to dramatically accelerate our developer workflow by leveraging AI in code reviews. Feedback is now instant and actionable – and available in the very environments where our developers work.

The Problem: Time-consuming pull requests

A pull request is a developer’s proposal (after writing code to solve an issue/feature/bug) to merge changes in one branch of a code repository into (usually) the main branch. The resulting merged code is what gets promoted to production.

This is generally how junior developers submit code changes for review by more senior developers. These code reviews are critical but eat up a lot of senior developer time. Senior developers at RiskSpan face many of the same challenges as senior developers everywhere in that they juggle multiple priorities and struggle to find the time necessary to provide thorough, timely feedback on every pull request. This can lead to delays, inconsistent quality, and “technical debt” over time.

The Solution: An AI Merge Agent

At AWS re:Invent, we discovered Qodo Merge, an AI-powered tool that automates and enhances pull request reviews. Now live in our Bitbucket repositories, Qodo Merge:

  • Analyzes code changes and autogenerates pull request descriptions
  • Checks ticket compliance to ensure requirements are met
  • Flags security risks (e.g., command injection, cross-site scripting)
  • Suggests improvements in data processing, error handling, and logic
  • Provides real-time feedback, accelerating development cycles

Why this matters for our development lifecycle

This AI solution is revolutionizing and streamlining RiskSpan’s software development process by:

  • Standardizing our code review best practices
  • Reducing technical debt by enforcing quality baselines
  • Accelerating junior developers and making them more efficient by providing instant guidance
  • Freeing up senior engineers to focus their efforts on high-impact strategic work

What’s Next?

Having completed our initial pilot testing, we are now rolling out Qodo Merge across RiskSpan’s various code repositories. Next up: training sessions and broader adoption across all of our modeling and engineering teams.

AI is transforming how we build, validate, and deploy code. Stay tuned for insights on how this initiative is improving our development speed and quality!

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!

Preparing For Impact: How Will Non-QM Prepay Speeds React to Lower Rates?

In a recent post, we addressed some of the less obvious ways in which a lower interest rate environment is likely to impact an agency universe with such a large volume of loans that are still out-of-the-money to refinance. In this post, we turn our attention to non-QM loans, whose unique characteristics mean they will likely feel the coming rate cuts differently.

Understanding the Distinctive Prepayment Dynamics of Non-QM Loans

Non-QM loans cater to borrowers who do not meet the stringent criteria of traditional agency loans, often due to factors like non-standard income documentation, credit issues, or investment property financing. Non-QM loans generally carry higher interest rates, and, unlike their agency counterparts, many have prepayment penalties designed to protect lenders from early payoff risk. Non-QM loans are also more likely than agency loans to involve investment properties – and thus, the underlying mortgages are not subject to the same “ability to repay” constraints that apply to agency/QM loans.

All these factors play a role in forecasting prepay speeds.

As rates decline, the incentive for some non-QM borrowers to refinance should increase, but several unique factors will shape the extent to which borrowers respond to this incentive:

  1. Prepayment Penalties: Many non-QM loans, especially those structured as Debt Service Coverage Ratio (DSCR) loans for investment properties, include prepayment penalties that can deter refinancing despite a favorable rate environment. These penalties vary widely, from a fixed percentage over a set period to declining penalties over time. The economic calculus for borrowers will hinge on whether the potential savings from refinancing outweigh these penalties
  2. Diverse Loan Structures: The non-QM market includes a variety of loan products, such as 40-year terms, hybrid ARMs and loans with interest-only periods, reminiscent of the pre-2008 lending landscape. This diversity means that not all non-QM loans will see the same incentive to refinance and the slope of the mortgage curve will matter. For example, loans with higher rates are likely to exhibit a stronger refinance response, particularly as the shape of the mortgage rate curve plays a significant role, with hybrid ARMs resetting off short-term rates and 30-year fixed-rate mortgages being influenced by movements in the 10-year Treasury yield
  3. Interest Rate Spread Compression: Historically, the spread between non-QM and agency mortgage rates has varied significantly, ranging from 100 to 300 basis points. A narrowing of this spread, driven by falling rates, could heighten the refinance incentive for non-QM borrowers, leading to faster prepayment speeds. However, the extent of this spread compression is uncertain and will depend on broader market dynamics. Souring economic conditions, for example, would likely contribute to a widening of spreads.

Key Factors Influencing Non-QM Prepayment Speeds

Loan Characteristics and Documentation Types

Non-QM loans can vary significantly by documentation type, such as full documentation, bank statements, or DSCR. Historically, as illustrated in the following chart, full documentation loans have shown faster prepayment speeds, because these borrowers are closer to qualifying for agency refinancing options as rates drop.

S-Curves by Doc Type (Full vs. Alt. vs. Bank Statement vs. DSCR)

Unlike agency mortgages, which include a substantial volume of loans originated at much lower rates, the non-QM market predominantly consists of loans originated in the past few years when rates were already elevated. As a result, a larger portion of non-QM loans is closer to being “in the money” for refinancing. This distinction suggests that the non-QM sector may see a more pronounced increase in prepayment activity compared to agency loans, where the lock-in effect remains stronger.

S-Curve (line) vs UPB (bars) by Refi Incentive

Economic Sensitivity to Rate Moves

For many non-QM borrowers, the primary barrier to agency loan qualification—whether credit score, income documentation, or property type—remains static despite lower rates. Thus, while a rate cut could improve the appeal of refinancing into another non-QM product, it might not significantly shift these borrowers towards agency loans. However, as noted, those closer to the threshold of agency eligibility could still be enticed to refinance if the rate spread and penalty structures align favorably.

Conclusion

The coming interest rate cuts are poised to influence the non-QM market in unique ways, with prepayment speeds likely to increase as borrowers seek to capitalize on lower rates. However, the interplay of rate spreads, prepayment penalties, and diverse loan structures will create a complex landscape where not all non-QM loans will behave uniformly. For lenders and investors, understanding these nuances is crucial to accurately forecasting prepayment risk and managing portfolios in a changing rate environment.

As the market evolves, ongoing analysis and model updates will be essential to capturing the shifting dynamics within the non-QM space, ensuring that investors and traders are well-prepared for the impacts of the anticipated rate cuts. Contact us to learn how RiskSpan’s Edge Platform is helping a growing number of non-QM investors get loan-level insights like never before.

Is Your Prepay Analysis Ready for the Rate Cut?

The forthcoming Federal Reserve interest rate cuts loom large in minds of mortgage traders and originators. The only remaining question is by how much rates will be cut. As the economy cools and unemployment rises, recent remarks by the Fed Chair have made the expectation of rate cuts essentially universal, with the market quickly repricing to a 50bp ease in September. This anticipated move by the Fed is already influencing mortgage rates, which have already experienced a notable decline.

Understanding the Lock-in Effect

One of the key factors influencing prepayments in the current environment is the lock-in effect, where borrowers are deterred from selling their current home due to the large difference between their current mortgage rate and prevailing market rates (which they would incur when purchasing their next home). As rates decrease, the gap narrows, reducing the lock-in effect and freeing more borrowers to sell and move.

As Chart 1 illustrates, a significant share of borrowers continues to hold mortgages between 2 and 3 percent. These borrowers clearly still have no incentive to refinance. But historical data suggests that the sizeable lock-in effect, which is currently depressing turnover, diminishes as the magnitude of their out-of-the-moneyness comes down. In other words, even a 100-basis point reduction can significantly increase housing turnover, as borrowers who were previously 300 basis points out of the money move to 200 basis points, making selling their old home and buying a new one, despite the higher interest rate, more palatable.

CHART 1: Distribution of Note Rates for 30-Year Conventional Mortgages: July 2024

Current Market Dynamics

Recent data from Mortgage News Daily indicates that mortgage rates have dropped over the past four weeks from around 6.8% to nearly 6.4%. This decrease is expected to continue, potentially bringing rates below 6% by the end of the year. This will likely have a profound impact on mortgage prepayments, particularly in the Agency MBS market.

Most outstanding mortgages, particularly those in Fannie and Freddie securities, currently have low prepayment speeds, with many loans sitting at 2% to 3% coupons. While a drop in mortgage rates to 6% (or lower) will still leave most of these mortgages out of the money for traditional rate-and-term refinances, it may bring a growing number of them into play for cash-out refinances, given significant home price appreciation and equity buildup over last 4 years. It will also loosen the grip of the lock-in effect for a growing number of homeowners currently paying below-market interest rates.

Implications for Prepayment Speeds

Factoring in the potential increase in turnover and cash-out refis, the impact of rate cuts on prepayment speeds could be substantial. For instance, with a 100-bp drop in rates, loans that are deeply out of the money could see their prepayment speeds increase by 1 to 2 CPR based on the turnover effect alone. Loans that are just at the money or slightly out of the money will see a more pronounced effect, with prepayment speeds potentially doubling. Chart 2, below, illustrates both the huge volume of loans deep out of the money to refinance as well as the small (but significant) uptick in CPR that a 100-bp shift in interest rates can have on CPR even for loans as much as 300 bps out of the money.

CHART 2: CPR by Refinance Incentive (dotted line reflects UPB of each bucket)

Historical data suggests that if mortgage rates move to 6.4%, the volume of loans moving into the money to refinance could increase up to eightfold — from $39 billion to $247 billion (see chart 3, below.) This surge in refinance activity will significantly influence prepays — impacting both turnover and refi volumes.

CHART 3: Volume and CPR by Coupon (dotted line reflects UPB of each bucket)

The Broader Housing Market

Beyond prepayments, the broader housing market may also feel the effects of rate cuts, but perhaps in a nuanced way. A reduction in rates generally improves affordability, potentially sustaining or even increasing home prices despite the increased supply from unlocked homes. However, this dynamic is complex. While lower rates make homes more affordable, the release of previously locked-in homes could counterintuitively depress home prices due to increased supply. With housing affordability at multi-decade lows, an uptick in housing supply could swamp any effect of somewhat lower rates.

While a modest rate cut may primarily boost turnover, a more significant cut could trigger a wave of refinancing. Additionally, cash-out refinances may become more attractive, offering a cheaper alternative to HELOCs and other more expensive options.

Conclusion

The forthcoming Fed interest rate cuts are poised to have a significant impact on mortgage prepayments. As rates decline, the lock-in effect will ease, encouraging more refinancing and increasing prepayment speeds. The broader housing market will also feel the effects, with potential implications for home prices and overall market dynamics. Monitoring these trends closely will be crucial for market participants, particularly those in the agency MBS market, as they navigate the changing landscape.

Contact us to staying informed and prepared and learn more about how RiskSpan can help you make strategic decisions that align with evolving market conditions.

Leveraging Pool-Specific Performance and Recapture Analysis: A Game Changer for MSR Investors

Successfully forecasting MSR cash flows demands a level of precision and granularity in data analysis that few other asset classes require. This is especially true for investors seeking to estimate how much prepayment runoff they can reasonably expect to recapture, which is key to the performance of the asset. And often investors need to measure that performance by the specific pools of MSRs they purchase — as each pool may have its own unique recapture arrangements.

RiskSpan’s Edge Platform has incorporated a robust framework for managing MSR investment performance by enabling investors to track pool-specific performance and recapture analyses, thus obtaining a more nuanced understanding of their portfolios. In this post, we delve into some of the specific challenges MSRs pose, the benefits of transaction-specific segmentation, and the unique capabilities of RiskSpan’s Edge Platform.

Understanding Pool-Specific Performance

Owning MSRs requires investors to track the performance of various loan pools over time. For example, an investor may purchase an MSR pool and rely on a sub-servicer to service the loans as well as make efforts to recapture borrowers that are looking to refinance. It is important for the investor to understand and track the returns on that pool which may be largely driven by recapture efficiency.  

While performance needs to be monitored on a pool-level, the modeling of the underlying loans is dependent on the distinct characteristics of the loans within a pool and will be more accurate if the models are run at the loan-level (or at granular rep lines determined by smart rep line logic).  The ability to capture and analyze these pool-specific cash flows based on granular loan-level modeling is crucial for several reasons:

  1. Valuation Accuracy: Each loan can be valued more accurately by considering its unique attributes, such as the original loan terms, interest rates, and borrower profiles (e.g., FICO, LTV); at the same time, pools can be valued based on pool-specific assumptions such as recapture rates and prepayment penalties.
  2. Risk Management: Understanding the performance of individual pools helps in identifying which pools are more prone to prepayments or defaults, enabling more focused efforts on recapture and other risk management activities.
  3. Performance Tracking: Investors can track historical returns, CPRs, CDRs, Recapture and other historical performance metrics for each pool, facilitating more informed decision-making.

Supporting this functionality is RiskSpan’s ability to share and integrate data on Snowflake’s data cloud. RiskSpan’s Snowflake integration enhances the data management and analytics capabilities available to clients. Investors can easily share transaction-specific data through Snowflake, which is then seamlessly integrated into the Edge platform. The platform can then handle the large datasets (tens of millions of loans in some instances), providing real-time analytics and insights.

Recapture Analysis: Enhancing Portfolio Performance

Recapture analysis is a critical component for MSR portfolio risk management. When borrowers refinance or otherwise pay off their loans, the servicer’s cash flows usually vanish entirely. However, if, in the case of refinance, the investor retains the rights to service the loan replacing the refinanced loan, then the new loan can be considered as a recapture. RiskSpan’s Edge platform excels in tracking these recaptures, offering several advantages:

  1. Detailed Tracking: The platform allows for the separation and detailed tracking of original loans and their recaptures, maintaining the distinction between the two. Recaptures should have better performance (i.e., lower CPRs) than original loans.
  2. Performance Comparison: By comparing the performance of original loans and recaptures, investors can gauge the effectiveness of their recapture strategies.
  3. Granular Assumptions: Edge supports highly granular recapture rate assumptions used for projecting cash flows, which can be tailored to specific pools or deals, enhancing the precision of valuation.

A Case Study: Supporting a Large Mortage REIT’s MSR Portfolio Management Regime

A practical example of these capabilities involves a mortgage REIT, which relies on RiskSpan’s platform to manage a large MSR portfolio. Specifically, the Edge platform has enabled the REIT investor to accomplish the following:

  • Capture Transaction-Specific Data: the investor can track and analyze data at the transaction level, maintaining detailed records of each pool’s performance and its recaptures. This allows, for example, investors to review performance with sub-servicers and evaluate whether certain changes can be made to enhance performance either on the existing pool or on future pools.
  • Custom Assumption Setting: The platform allows for custom segmentation and assumption setting for valuation purposes, such as different recapture rates based on prepayment projections or loan age. This provides an ability to more accurately measure future projected cash flows and factor that into valuation of owned MSRs as well as potential purchases.

RiskSpan’s Edge platform offers MSR investors a robust toolset for managing their portfolios with precision not available anywhere else. By enabling pool-specific performance and detailed recapture analysis, Edge helps investors optimize their strategies and enhance portfolio performance. The ability to capture and analyze nuanced data points sets RiskSpan apart, making it a valuable ally in the complex landscape of MSR investments.

MSR investors, contact us to discover how tailored analytics and granular data management can transform your investment strategy and give you a competitive edge.

AI Prompt Structuring — Does it Even Matter?

At the mesh point of human ingenuity and artificial intelligence, the importance of appropriately structured prompts is frequently underestimated. Within this dynamic (and, at times, delicate) ecosystem, the meticulous craftmanship of prompts serves as the linchpin, orchestrating a seamless collaboration between human cognition and machine learning algorithms. Not unlike to a conductor directing an ensemble, judicious prompt structuring lays the foundation for AI systems to synchronize with human intent, thereby facilitating the realization of innovative endeavors. Given the large number of interactions with Large Language Models (LLMs) based on 1:1 digital chats, it is important to carefully prompt gen AI models to generate accurate and tailored outputs.

Gartner predicts that more than 80% of enterprises will have used generative artificial Intelligence (gen AI) or deployed gen AI-enabled applications in production environments by 2026, up from less than 5% in 2023.[1] As gen AI adoption continues to accelerate, understanding proper prompt engineering structures and techniques is becoming more and more important.

With this in mind, we are going to discuss the criticality of the structure of AI prompting to the accuracy of AI outputs. Specifically, we discuss how defining objectives, assigning roles, providing context, specifying the output format, and reviews each play a role in crafting effective prompts.  

@Indian_Bronson. “salmon swimming in a river.” 15 Mar. 2023. X(Twitter), https://twitter.com/Indian_Bronson/status/1636213844140851203/photo/2. Accessed 3 Apr. 2024

Interacting with LLMs through a chat bot function may result in frustrations as users are faced with outputs that are not on par with their expectations. However, the more detail and clarity given to the model, the more resources it will have to understand and execute the task properly. In this context, “detail and clarity” means:

    1. Defining the objective

    1. Assigning Roles and Providing context

    1. Specifying the output format

    1. Reviewing & Refining

1. Define the Objective
Some good questions to ask oneself before providing a prompt to the gen AI include: What needs to be done? What tone does it have to be in? What format do we need? A 2023 Standford University study found that models are better at using relevant information that occurs at the very beginning or the end of the request.[2] Therefore, it is important to generate prompts that are context rich, and concise. 

2. Assign Roles and Provide Context
Arguably the most important part of prompting, providing context is critical because gen AI machines cannot infer meanings beyond the given prompts. Machines also lack the years of experience necessary to grasp the sense of what is needed and what is not without some explicit direction. The following principles are important to bear in mind:

Precision and Personalization: Providing detailed context and a clear role enables the AI system to deliver responses that are both accurate and tailored to individual user needs, preferences, and the specificity of the situation.

Delimiters like XML tags: & angle brackets: <> are a great way to separate instructions, data, and examples from one another. Think of XML tags as hash tagging on social media.

For example:

 

I want to learn about Mortgage Finance and its history

What are some key institutions in the industry?

 

Efficiency and Clarity in Communication: By understanding its expected role, whether as a consultant, educator, or support assistant, an AI application can adjust its communication style, level of detail, and prioritization accordingly. This alignment not only streamlines interactions but also ensures that the dialogue is efficiently directed towards achieving the user’s goals, minimizing misunderstandings and maximizing productivity.

Appropriateness and Ethical Engagement: Knowledge of the context in which it operates, and the nuance of its role allows an AI to navigate sensitive situations with caution, ensuring that responses are both appropriate and considerate. Moreover, this awareness aids in upholding ethical standards in an AI’s responses — crucial for maintaining user trust and ensuring a responsible use of technology.

3. Specify the output format
In crafting a prompt for AI text generation, specifying the output format is crucial to ensuring that the generated output is not only relevant, but also suitable for the intended purpose and audience or stakeholders. To this end:

  • Provide clear instructions that include details of the text’s purpose, the audience it’s intended for, and any specific points or information that should be included. Clear instructions help prevent ambiguity and ensure that the AI produces relevant and coherent output.
  • Set the desired tone, language, and topics so that the output is properly tailored to a business need or setting, whether it is an informative email or a summary of a technical report. Outlining specific topics in combination with language and tone setting aids in generating output that resonates with the stakeholders at the appropriate level of formality and delegates the correct purpose of such output to the end user.
  • Define constraints (length, count, tools, terminology) to help guide the AI’s text generation process within predetermined boundaries. These constraints ensure that the generated output meets the task’s requirements and is consistent with existing systems or workflows. It also minimizes review time and reduces the possibility of submitting additional prompts.

    • Supply output examples. This is a great way to encompass all the above tricks for specifying the output format. Examples serve as reference points for style, structure, and content, helping the AI understand the desired outcome more effectively. By providing a tangible example to the gen AI, a user increases the likelihood of achieving a satisfactory result that aligns with expectations.

4. Review & Refine
Last, but nevertheless important, is to review the prompt before submitting it to the gen AI. Check for consistency of terminology and technical terms usage throughout the prompt and formatting, such as tags and bullet points, to avoid confusion in the responses. Make sure the prompt follows logical flow, avoids repetition and unnecessary information to maintain the desired level of specificity and to avoid skewing the response onto the undesired path.

As users navigate the complexities of AI integration, remembering these prompting structures ensures maximization of AI’s potential while mitigating risks associated with misinformation.

Contact us to learn more about how we are helping our clients harness AI’s capabilities, informed by a strategic and mindful approach.

[1] “Gartner Says More than 80% of Enterprises Will Have Used Generative AI Apis or Deployed Generative AI-Enabled Applications by 2026.” Gartner, 11 Oct. 2023, www.gartner.com/en/newsroom/press-releases/2023-10-11-gartner-says-more-than-80-percent-of-enterprises-will-have-used-generative-ai-apis-or-deployed-generative-ai-enabled-applications-by-2026.

[2] Liu, Nelson F., et al. Lost in the Middle: How Language Models Use Long …, July 2023, cs.stanford.edu/~nfliu/papers/lost-in-the-middle.arxiv2023.pdf.

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