Gen AI Archives

Use Case: RiskSpan’s Agentic AI for MBS Data Tool

Breaking Down VA vs FHA Prepayment Speeds

This use case demonstrates how agentic interaction with the MBS Data Tool helps investors move from a general observation (“VA speeds are fast”) to a data-backed, multidimensional explanation tied directly to credit, LTV, and program structure.

Business Question

Why are VA loans — particularly those with low FICO and high LTV profiles — prepaying so much faster than historically, and faster than FHA loans with comparable characteristics?

For the full output of our AI MBS Agent, read on or watch our two-minute demo video:

Workflow with Agentic AI

1. Baseline Comparison

Based on the data, I can now provide a comprehensive analysis of VA vs FHA loan prepayment trends since 2020:

Key Findings:

1. Current Prepayment Speeds (as of September 2025):

– VA loans: 11.74% CPR

– FHA loans: 8.68% CPR

– VA loans are currently prepaying about 3 percentage points faster than FHA loans

2. Historical Trends:

2020-2021 (Refinance Wave):

– Both loan types experienced very high prepayment speeds

– Peak CPRs:

* FHA: 38.48% (April 2021)

* VA: ~30-31% (Late 2021)

– VA loans consistently showed higher prepayment sensitivity during this period

2022-2023 (Rising Rate Environment):

– Both loan types saw significant slowdown in prepayment speeds

– Lowest CPRs:

* FHA: 4.42% (February 2023)

* VA: 5.07% (February 2023)

– The spread between VA and FHA prepayment speeds narrowed but remained positive

2024-2025 (Current Environment):

– Moderate prepayment speeds for both loan types

– VA loans maintain faster prepayment speeds

– Recent range:

* FHA: 5-10% CPR

* VA: 7-14% CPR

3. Key Observations:

– VA loans consistently prepay faster than FHA loans across different rate environments

– The spread between VA and FHA prepayment speeds tends to widen in more favorable refinancing environments

– Both loan types show similar seasonal patterns but with different magnitudes

– VA loans show greater prepayment sensitivity to market conditions

4. Portfolio Sizes:

– FHA: Currently ~7.45 million loans with $1.47 trillion in balance

– VA: Currently ~3.77 million loans with $1.03 trillion in balance

This analysis suggests that investors should expect consistently faster prepayment speeds from VA loans compared to FHA loans, particularly during periods of favorable refinancing opportunities. The higher prepayment sensitivity of VA loans likely reflects the generally better credit quality of VA borrowers and their greater ability to refinance when rates are favorable.

2. High-Credit Borrowers

Based on the analysis of high-FICO (740+) VA and FHA loans, here are the key findings:

1. Current Prepayment Speeds (September 2025):

– High-FICO VA: 9.37% CPR

– High-FICO FHA: 5.96% CPR

– Spread: 3.41% faster for VA

2. Portfolio Characteristics (as of September 2025):

High-FICO VA Loans:

– Average FICO: 779

– Average WAC: 3.85%

– Average CLTV: 58.1%

– Balance: $443.4B (43% of VA portfolio)

– Loan Count: 1.47M loans

High-FICO FHA Loans:

– Average FICO: 769

– Average WAC: 4.55%

– Average CLTV: 58.4%

– Balance: $188.0B (13.8% of FHA portfolio)

– Loan Count: 879K loans

3. Key Trends (2024-2025):

Prepayment Speed Range:

– High-FICO VA: 4.26% to 14.76% CPR

– High-FICO FHA: 3.56% to 8.68% CPR

Seasonal Patterns:

– Both show seasonal increases in spring/summer months

– VA loans show more pronounced seasonal effects

– Peak prepayment typically occurs in October/November

4. Notable Observations:

– The prepayment speed differential between VA and FHA remains significant even at high FICO levels

– High-FICO VA loans have consistently lower WACs (about 70bps lower)

– Similar CLTV profiles between high-FICO VA and FHA

– VA has a much larger proportion of high-FICO borrowers (43% vs 13.8% for FHA)

– High-FICO VA loans show greater prepayment sensitivity to market conditions

5. Recent Trends:

– Both loan types show increasing prepayment speeds in 2025

– The spread between VA and FHA prepayment speeds has remained relatively stable

– High-FICO VA loans maintain faster prepayment speeds even in rising rate environments

This analysis suggests that even when controlling for credit quality (FICO ≥ 740), VA loans consistently prepay faster than FHA loans. This is likely due to:

1. Lower average note rates (WAC)

2. VA’s no-down-payment benefit making it easier to refinance

3. Generally better borrower demographics and income profiles

4. More streamlined refinancing options for VA loans

Investors should expect continued faster prepayment speeds from high-FICO VA loans compared to high-FICO FHA loans, particularly during favorable rate environments or seasonal peaks.

3. Low-Credit Borrowers

Based on the analysis of low-FICO (<640) VA and FHA loans, here are the key findings:

1. Current Prepayment Speeds (September 2025):

– Low-FICO VA: 21.04% CPR

– Low-FICO FHA: 12.07% CPR

– Spread: 8.97% faster for VA

2. Portfolio Characteristics (as of September 2025):

Low-FICO VA Loans:

– Average FICO: 611 – Average WAC: 4.77%

– Average CLTV: 66.2%

– Average Age: 52.3 months

– Balance: $77.5B (8.3% of VA portfolio)

– Loan Count: 316K loans

Low-FICO FHA Loans:

– Average FICO: 609

– Average WAC: 4.83%

– Average CLTV: 60.4%

– Average Age: 62.8 months

– Balance: $290.7B (21.3% of FHA portfolio)

– Loan Count: 1.49M loans

3. Key Trends (2024-2025):

Prepayment Speed Range:

– Low-FICO VA: 9.08% to 27.54% CPR

– Low-FICO FHA: 6.31% to 12.73% CPR

Notable Patterns:

– Much higher volatility in VA prepayment speeds

– Larger seasonal effects in VA loans

– Consistently wider spread between VA and FHA vs high-FICO segment

4. Key Differences:

Portfolio Composition:

– FHA has a much larger concentration in low-FICO loans (21.3% vs 8.3% for VA)

– FHA’s low-FICO portfolio is nearly 4x larger than VA’s by balance

Performance Characteristics:

– VA loans show significantly higher prepayment speeds despite similar credit profiles

– VA loans exhibit greater prepayment volatility

– VA loans have slightly better average FICO scores within the low-FICO segment

– VA loans have higher CLTVs but lower WACs

5. Recent Trends:

– Both loan types show increasing prepayment speeds in 2025

– VA prepayment speeds are consistently higher and more volatile

– Seasonal patterns are more pronounced in VA loans

– The spread between VA and FHA prepayment speeds has remained wide

6. Notable Observations:

– The prepayment speed differential between VA and FHA is actually larger in the low-FICO segment than in the high-FICO segment

– Low-FICO VA loans show surprisingly high prepayment speeds despite lower credit scores

– FHA loans show more consistent, less volatile prepayment patterns

– VA loans maintain faster speeds despite higher CLTVs

This analysis reveals that the prepayment behavior difference between VA and FHA loans is even more pronounced in the low-FICO segment than in the high-FICO segment. This could be due to:

1. VA’s stronger refinancing program benefits for lower-credit borrowers

2. More flexible VA streamline refinance requirements

3. Better servicing support for VA borrowers

4. Lower average note rates in the VA program

5. Potentially different borrower demographics within the same FICO band

Investors should expect:

– Higher and more volatile prepayment speeds from low-FICO VA loans

– More stable, lower prepayment speeds from low-FICO FHA loans

– Larger seasonal effects in VA loans

– Greater market sensitivity in VA loans

4. High-LTV Loans

Based on the analysis of high-LTV (≥95%) VA and FHA loans, here are the key findings:

1. Current Prepayment Speeds (September 2025):

– High-LTV VA: 12.37% CPR

– High-LTV FHA: 7.77% CPR

– Spread: 4.60% faster for VA

2. Portfolio Characteristics (as of September 2025):

High-LTV VA Loans:

– Average FICO: 714

– Average WAC: 4.33%

– Average CLTV: 70.9%

– Average Original LTV: 100.3%

– Average Age: 52.9 months

– Balance: $607.2B (61.1% of VA portfolio)

– Loan Count: 2.11M loans

High-LTV FHA Loans:

– Average FICO: 679

– Average WAC: 4.61%

– Average CLTV: 62.9%

– Average Original LTV: 97.6%

– Average Age: 71.7 months

– Balance: $1.04T (72.0% of FHA portfolio)

– Loan Count: 5.08M loans

3. Key Trends (2024-2025):

Prepayment Speed Range:

– High-LTV VA: 5.60% to 19.73% CPR

– High-LTV FHA: 4.50% to 8.89% CPR

Notable Patterns:

– Higher volatility in VA prepayment speeds

– Strong seasonal patterns in VA prepayments

– More stable FHA prepayment patterns

– Consistent spread between VA and FHA

4. Key Differences:

Portfolio Composition:

– Both programs have significant high-LTV concentration

– FHA has larger absolute exposure but similar relative exposure

– VA allows higher original LTV (100%+) compared to FHA

Performance Characteristics:

– VA loans consistently prepay faster despite higher LTVs

– VA borrowers have notably higher FICO scores (+35 points)

– VA loans have lower WACs (-28bps)

– VA loans are significantly younger (-19 months)

5. Recent Trends:

– Both loan types show increasing prepayment speeds in 2025

– VA prepayment speeds remain more volatile and responsive to seasonality

– Peak prepayment periods occur in spring/summer months

– The spread between VA and FHA has remained relatively stable

6. Notable Observations:

– High-LTV concentration is a defining characteristic of both programs

– VA’s higher prepayment speeds persist despite higher LTVs

– Better credit quality of VA borrowers likely contributes to faster speeds

– Younger average loan age in VA portfolio may influence prepayment behavior

– Lower WACs in VA portfolio suggest more efficient refinancing history

Implications for Investors:

1. Portfolio Management:

– Expect faster and more volatile prepayments from high-LTV VA loans

– Consider seasonal patterns in VA prepayments for timing decisions

– Factor in the significant WAC differential when evaluating prepayment risk

2. Risk Considerations:

– Higher prepayment risk in VA despite similar LTV profiles

– Greater market sensitivity in VA portfolio

– More stable, predictable prepayment patterns in FHA

3. Valuation Impact:

– Need to apply larger prepayment speed differentials for VA vs FHA

– Consider stronger seasonal effects in VA valuations

– Account for credit quality differences within high-LTV segment

The analysis suggests that even in the high-LTV segment, VA loans maintain significantly faster prepayment speeds than FHA loans, driven by:

1. Better borrower credit quality

2. Lower note rates

3. More flexible refinancing options

4. Younger loan age

5. More efficient VA refinancing program

These factors appear to overcome the theoretical constraints of high LTV ratios on refinancing ability.

5. Program Features as Drivers

The data strongly supports that program features drive faster VA prepayment speeds. Here’s the detailed analysis:

1. Early Prepayment Behavior (0-24 months):

– VA loans age 13-24 months: 25.29% CPR

– FHA loans age 13-24 months: 17.04% CPR

– The 8.25% difference reflects VA’s more flexible refinancing rules

– No seasoning requirement for VA vs 210 days for FHA

2. Refinance Incentive Response:

– High incentive (>100bp) VA: 50.43% CPR

– High incentive (>100bp) FHA: 26.55% CPR

– The dramatic difference (23.88%) shows VA borrowers can more easily act on rate incentives

– Even medium incentive (0-100bp) shows large gap:

* VA: 29.59% CPR

* FHA: 18.37% CPR

3. Credit Quality Impact:

– VA average FICO: 715-724 across age buckets

– FHA average FICO: 672-681 across age buckets

– Better VA credit quality + easier program rules = faster speeds

4. Cost Structure Effects:

VA Advantages:

– Lower funding fee (0.5%)

– No ongoing MIP

– Can include closing costs

– Results in lower all-in refinancing costs FHA Constraints:

– Higher upfront MIP (1.75%)

– Ongoing annual MIP

– More restricted closing cost inclusion

– Higher total refinancing costs

5. LTV Considerations:

– VA has no maximum LTV for IRRRL

– FHA generally limited to 97.75% LTV

– Current data shows:

* VA high-LTV loans (>95%): 12.37% CPR

* FHA high-LTV loans (>95%): 7.77% CPR

6. Program Flexibility Evidence:

Response to Rate Environment:

– VA loans show stronger reaction to refi incentives

– Higher CPRs across all incentive buckets

– Faster response to market opportunities

Seasoning Effects:

– VA shows earlier prepayment spikes

– More pronounced early-age prepayment behavior

– Less constrained by seasoning rules

7. Servicer Impact:

– VA servicers may be more proactive due to program ease

– Lower documentation requirements reduce operational friction

– Simpler approval process enables faster execution

The data demonstrates that VA’s program features create a more efficient refinancing mechanism:

1. Lower Costs:

– Reduced fees

– No ongoing MIP

– More included costs

2. Fewer Restrictions:

– No LTV limits

– No seasoning requirement

– Limited credit checks

3. Simpler Process:

– Less documentation

– Faster approval

– Lower operational burden

These advantages manifest in:

– Consistently faster prepayment speeds

– Stronger response to rate incentives

– Earlier refinancing activity

– Higher refinancing success rates

For investors, this means:

1. Expect faster VA prepayments across market cycles

2. Anticipate stronger response to refinancing opportunities

3. Factor in program differences when valuing VA vs FHA MBS

4. Consider seasoning rules when evaluating young loans

The combination of program features and data evidence clearly shows that VA’s more flexible refinancing rules are a primary driver of faster prepayment speeds compared to FHA.

6. Scenario Stress Test

Based on the current portfolio characteristics and historical refinancing behavior, here’s how a 100bps rate drop would likely impact VA vs FHA prepayment speeds:

1. Current Portfolio Rate Distribution:

VA Portfolio:

– 7.5%+ WAC: $4.6B (0.4%)

– 7.0-7.49% WAC: $18.5B (1.8%)

– 6.5-6.99% WAC: $71.1B (6.9%)

– 6.0-6.49% WAC: $105.3B (10.2%)

– 5.5-5.99% WAC: $118.7B (11.5%)

– <5.5% WAC: $713.0B (69.2%)

FHA Portfolio:

– 7.5%+ WAC: $23.6B (1.6%)

– 7.0-7.49% WAC: $68.5B (4.7%)

– 6.5-6.99% WAC: $163.8B (11.2%)

– 6.0-6.49% WAC: $158.4B (10.8%)

– 5.5-5.99% WAC: $149.0B (10.2%)

– <5.5% WAC: $903.5B (61.5%)

2. Projected Prepayment Speeds by Refi Incentive:

Based on current behavior with similar incentives:

High Incentive (>100bps):

– VA: 50-55% CPR

– FHA: 25-27% CPR

– Differential: ~25-28% faster for VA

Medium Incentive (50-100bps):

– VA: 35-40% CPR

– FHA: 20-25% CPR

– Differential: ~15% faster for VA

Low Incentive (0-50bps):

– VA: 25-30% CPR – FHA: 15-18% CPR

– Differential: ~10-12% faster for VA

3. Portfolio Impact of 100bps Rate Drop:

VA Portfolio:

– ~$200B would move to >100bps incentive

– ~$119B would move to 50-100bps incentive

– Weighted average CPR projection: 32-37% CPR

FHA Portfolio:

– ~$256B would move to >100bps incentive

– ~$149B would move to 50-100bps incentive

– Weighted average CPR projection: 18-22% CPR

4. Key Factors Driving Divergence:

Program Features:

– VA IRRRL has no LTV restrictions

– VA has lower funding fees

– VA has simpler documentation requirements

– VA has no seasoning requirement

Borrower Characteristics:

– VA has higher average FICOs (714 vs 675)

– VA has lower average WACs

– VA has younger average loan age

5. Projected Timeline of Response:

Months 1-3:

– VA speeds increase rapidly to 30-35% CPR

– FHA speeds lag, reaching 15-18% CPR

– Differential: 15-17% CPR

Months 4-6:

– VA peaks at 35-40% CPR – FHA reaches 20-22% CPR

– Differential: 15-18% CPR

Months 7-12:

– VA moderates to 30-35% CPR

– FHA stabilizes at 18-20% CPR

– Differential: 12-15% CPR

6. Risk Factors to Consider:

Upside Risks (faster speeds):

– Pent-up refinance demand

– Improved technology/processing

– Competitive lender environment

Downside Risks (slower speeds):

– Housing market uncertainty

– Credit tightening

– Operational capacity constraints

7. Investment Implications:

1. Premium Pools:

– Expect significantly faster VA prepayments

– Greater price impact on VA premium pools

– Wider VA-FHA OAS spreads

2. Current Coupon Pools:

– Moderate prepayment differential

– Less price impact

– Smaller OAS spread widening

3. Discount Pools:

– Minimal impact on speeds

– Limited price effect

– Stable OAS spreads

Recommendations:

1. Portfolio Positioning:

– Reduce exposure to premium VA pools

– Consider FHA alternatives in premium segment

– Maintain VA exposure in current coupon/discount segments

2. Risk Management:

– Increase prepayment protection in VA holdings

– Consider specified pool stories

– Monitor servicer behavior closely

3. Relative Value:

– Look for opportunities in FHA premium pools

– Consider VA-FHA switches in premium segment

– Evaluate specified pool pay-ups

The analysis suggests a significant divergence in VA vs FHA prepayment speeds following a 100bps rate drop, with VA speeds likely to be 12-25% CPR faster depending on the segment and timing. This differential is driven by both program features and borrower characteristics, with the impact most pronounced in premium pools.

Questions or Need Support?

Our team is standing by to help you make the most of these new features. Contact us at techsupport@riskspan.com or reach out to your dedicated account manager.

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RiskSpan Launches Agentic AI for MBS Data — Instant, Transparent Insights for Agency MBS Professionals

Arlington, VA – September 2, 2025 – RiskSpan, a leading provider of data analytics solutions for the structured finance industry, today announced the release of Agentic AI for MBS Data.

Watch a one-minute demo video or read on to learn about our new, AI-powered MBS analyst that delivers instant, sourced, and context-rich analysis in plain English.

Solving the MBS Data Bottleneck

While Agency MBS loan data is readily available, meaningful insights for many users often remain locked behind complex SQL or Python queries. This creates a barrier for traders, portfolio managers, and risk teams who need answers quickly to avoid missing opportunities.

“Our clients told us they needed faster, clearer access to this data,” said Suhrud Dagli, RiskSpan’s Co-Founder and Chief Innovation Officer. “Agentic AI removes the technical barrier and delivers the kind of immediate, transparent insight that our clients have come to demand.”

A 24/7 AI-Powered MBS Analyst

With Agentic AI for MBS Data, users simply ask a question in plain English — such as “Which Ginnie Mae 2020 production pools had the fastest speeds in Q2?” — and get back clear, visualized, and fully-cited answers in seconds. Ask Complex Questions, Get Instant Answers – No SQL or Python required.

Key benefits include:

Full Transparency – All sources cited for confidence and compliance.

Deeper Insights – Surfaces patterns and drivers you might not think to look for.

Narrative-Ready Reporting – Polished summaries and visuals for executives and clients.

Efficiency – Eliminates dependence on technical teams for everyday queries.

Designed for How MBS Professionals Think

From spotting prepayment anomalies to explaining performance shifts, Agentic AI identifies not just the what but the why. It transforms hours of manual analysis into seconds of actionable insight, enabling faster, more informed decision-making.

Agentic AI for MBS Data is available now. RiskSpan is offering live demos to showcase how the platform transforms the way MBS data is interrogated and reported.

About RiskSpan

RiskSpan delivers a single analytics solution for structured finance and private credit investors of any size to confidently make faster, more precise trading and portfolio risk decisions and meet reporting requirements with fewer resources, and less time spent managing multiple vendors and internal solutions. 

Learn more at www.riskspan.com. 

Humans in the Loop: Ensuring Trustworthy AI in Private ABF Deal Modeling

As generative AI becomes a powerful tool in Private asset-backed finance (ABF), the need for precision and transparency is more critical than ever. At RiskSpan, we’re applying Large Language Models (LLMs) to automate and accelerate private ABF deal modeling and surveillance. But speed is only half the battle—accuracy is non-negotiable.

That’s where Human-in-the-Loop (HITL) validation plays a vital role. While the RiskSpan platform incorporates sophisticated AI guardrails, we believe the right blend of automation and expert oversight ensures results that are not just fast—but reliable, auditable, and production-ready.

The AI-Powered Workflow: What’s Automated

Our private ABF modeling and surveillance system uses LLMs to handle several critical tasks:

Data Extraction: Parsing offering memos, indentures, and loan tapes to extract structural and financial data.
Deal Code Generation: Producing executable waterfall models based on extracted rules.
Database Ingestion: Uploading validated deal terms and triggers into the RiskSpan system of record.
Surveillance Automation: Running periodic deal performance analyses and compliance checks.

But What About Hallucinations?

Generative models are powerful but imperfect. Without the right controls, they can fabricate securitization tranches or fees that are not present in the deal documents. They can also misinterpret waterfall rules or omit critical override conditions or generate semantically incorrect code for cashflow models. To address these challenges, RiskSpan employs a multi-layered safeguard framework, combining asset class based extraction; LLM-as-Judge; Rule-Based Guardrails and Inline Human Review

Humans in the Loop: Three Layers of Oversight

We’ve embedded human validation at three key points in the deal lifecycle:

Pre-Modeling Validation: before LLM-generated outputs are finalized, RiskSpan analysts review extracted terms and model prompts—correcting anything misaligned with the source documents.
Inline Oversight: during waterfall code generation, humans validate AI-generated logic in context, ensuring correct treatment of subordination, triggers, caps/floors and other.
Post-Deployment Monitoring Surveillance: outputs are reviewed both by the RiskSpan team and client-side structuring or credit teams. Feedback is looped back into model tuning and prompt optimization.

Looking Ahead: RAG and Continuous Improvement

We’re actively exploring Retrieval-Augmented Generation (RAG) to reduce hallucinations even further. By grounding AI responses in deal-specific material such as offering documents, trustee reports, and internal risk memos—we aim to: 1) eliminate off-topic responses. 2) increase trust in model-derived outputs and 3) enable deeper customization per issuer or asset class.

The Takeaway

AI is transforming how private ABF deals are modeled and monitored—but it must be grounded and guided by human expertise and built for institutional rigor. At RiskSpan, we’re not just accelerating workflows—we’re raising the bar for accuracy and trust in AI-assisted private structures. Human-in-the-loop is not a fallback—it’s a strategic pillar. Want to see how our AI platform works in action? Reach out to schedule a demo or contact your RiskSpan representative to learn more.

Design Smarter — How AI is Changing UX from Idea to Execution

AI is revolutionizing everything, and the UX design process is no exception. From the earliest conceptual ideas all the way through to final execution, the transformation is not just about speeding up workflows but also about enhancing creativity and collaboration.

Here’s how.

Initial Ideation

Every UX journey begins with the ideation process. AI tools like Claude have become a go-to starting point for brainstorming and generating initial design prompts. By feeding basic requirements and user journeys into the AI, I can quickly generate a list of potential features and pain points. For example, when working on a new ETL tool, Claude helped identify potential difficulties in data mapping, handling large datasets, and ensuring data accuracy during the transformation process. These pain points helped Claude generate a list of requirements and user journeys, which were then used to create a first-pass prototype

This initial step is crucial as it sets the foundation for the entire design process.

Rapid Prototyping

Once the ideation phase is complete, the next step is creating a first-pass prototype. Claude has helped me here by generating quick, functional prototypes that provide a visual representation of the overall application. Although not fully functional, these prototypes nevertheless offer a solid starting point for further refinement. This rapid prototyping capability allows me to iterate quickly and incorporate feedback more efficiently.

After the initial prototype is created, I import it into Figma for refinement. This is where the design gets polished with logos, color schemes, and other branding elements. This is a highly collaborative phase of the process, where designers work closely with developers and test users to finalize the look and feel of the application. This step ensures that the design is not only functional but also visually appealing.

Code Development

The final stage involves turning the refined design into a working application. Here, remarkably, AI tools like Claude and Cursor (an AI-enhanced version of VS Code) can actually generate and refine the code itself. By providing the AI with an image of the final design, it can produce a close approximation of the user interface, which can then be fine-tuned by developers. For example, I might ask Claude to generate a sample layout based on the refined design and then use Cursor to make specific changes, such as adjusting font sizes and colors. This significantly reduces the time and effort required to build the front end of the application.

Real-World Application and Testing — Collaboration and Continuous Improvement

The iterative nature of AI tools allows for rapid prototyping and testing, leading to a more efficient development cycle. While AI-generated code might not be perfect, the ability to quickly identify and fix bugs makes the process much faster than traditional methods. For instance, I used Cursor to highlight and fix errors in the code by simply providing and asking it to correct the issues.

But collaboration remains supremely important. AI tools facilitate cross-functional teamwork by making it easier to share prototypes and gather feedback. This collaborative approach ensures that the final product meets the needs of all stakeholders. Additionally, the iterative nature of AI tools means that the design can continuously evolve based on user feedback and testing.

—————-

AI is not just a tool for speeding up the UX design process; it’s a catalyst for innovation and collaboration. By leveraging AI for ideation, prototyping, and code development, designers can create smarter, more efficient workflows that lead to better user experiences. The future of UX design is not just about working faster but also about working smarter.

From AI Hype to Helpful Assistant: AI Agents are coming soon to the RiskSpan Platform!

When agentic AI first hit the scene, we were intrigued—but skeptical. Was this just another over-hyped trend or something that could drive real value?

Fast forward a few months, and we’ve got our answer.

At RiskSpan, we’ve quietly integrated AI agents into our internal workflows through a dedicated Agent Desktop. These agents are now core to how we manage our business—monitoring client health, tracking system usage and perhaps most impressively, performing deep research across the massive datasets we store. What began as an experiment has become indispensable.

The real breakthrough is manifest, however, when Agents proactively uncover insights, flag anomalies, and automate routine analyses. Our Dev and Client teams are saving hours and making faster, more informed decisions because relevant information finds them.

Coming soon, our clients will be able to use Agents in the RiskSpan Platform to query their own data, analyze GSE performance data and run on-demand analysis instantly—all without waiting on a queue or building custom reports.

Designed for portfolio risk, surveillance, analyzing loan-level data, or exploring market trends, the AI agents will help you go from question to answer in seconds. Check out a sample below and reach out to learn more!

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!

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.

GenAI Applications for Loans and Mapping Data

RiskSpan is actively furthering the advancement of several GenAI applications aimed at transforming how mortgage loan and private credit investors work and maximizing their efficiency and performance. They include:

1. Tape-Cracking 3.0: Making RiskSpan’s Smart Mapper Even Smarter

RiskSpan’s Edge Platform currently uses machine learning techniques as part of its Smart Mapper ETL Tool. When a new portfolio is loaded in a new format, the fuzzy logic that powers the Platform’s recommended mappings gets continually refined based on user activity.

In the coming months, the Platform’s existing ML-driven ETL process will be further refined to leverage the latest GenAI technology.

GenAI lends additional context to the automated mapping process by incorporating an understanding not only of the data in an individual column, but also of surrounding data as well as learned characteristics of the asset class in question. The resulting evolution from simply trying to ensure the headers match up a more holistic understanding of what the data actually is and the meaning it seeks to convey will be a game changer for downstream analysts seeking to make reliable data-driven investment decisions.

RiskSpan made several updates in 2023 to help users automate the end-to-end workflow for loan valuation and surveillance. AI-based data loading combined with the Platform’s loan risk assumptions and flexible data model will enable users to obtain valuation and risk metrics simply by dragging and dropping a loan file into the application.

2. Modeling Private Credit Transactions

Many financial institutions and legal advisors still spend an extraordinary amount of time reading and extracting relevant information from legal documents that accompany structured private credit transactions.

RiskSpan has partnered with clients to develop a solution to extract key terms from private credit and funding transactions. Trained multimodal AI models are further extended to generate executable code valuations. This code will be fully integrated into RiskSpan’s risk and pricing platform.

The application solves a heretofore intractable problem in which the information necessary to generate accurate cash flows for private credit transactions is spread across multiple documents (a frequent occurrence when terms for individual classes can only be obtained from deal amendments).

Execution code for cash flow generation and valuation utilizes RiskSpan’s validated analytics routines, such as day count handling, payment calculations, discounting, etc.

3. “Insight Support”

Tech support is one of today’s most widely known (and widely experienced) GenAI use cases. Seemingly all-knowing chatbots immediately answer users’ questions, sparing them the inconvenience of having to wait for the next available human agent. Like every other company, RiskSpan is enhancing its traditional tech support processes with GenAI to answer questions faster and and embed user-facing AI help within the Platform itself. But RiskSpan is taking things a step further by also exploring how GenAI can upend and augment its clients’ workflows.

RiskSpan refers to this workflow augmentation as “Insight Support.”

With Insight Support, GenAI evaluates an individual user’s data, dynamically serves up key insights, and automatically completes routine analysis steps without prompting. The resulting application can understand an individual user’s data and recognize what is most important to identify and highlight as part of a loan data analysis workflow.

Insight Support, for example, can leverage insights obtained by the AI-driven “Smarter Mapping” process to identify what specific type of collateral reporting is necessary. It can produce reports that highlight outliers, recognize the typical analytical/valuation run settings a user would want to apply, and then execute the analytical run and summarize the results in management-ready reporting. All in the name of shortening the analysis time needed to evaluate new investment opportunities.

Conclusion

Considered collectively, these three applications are building toward having RiskSpan’s SaaS platform function as a “virtual junior analyst” capable of handling much of the tedious work involved in analyzing loan and structured product investments and freeing up human analysts for higher-order tasks and decision making.

GenAI is the future of data and analytics and is therefore the future of RiskSpan’s Edge Platform. By revolutionizing the way data is analyzed, AI-created and -validated models, dashboards, and sorted data are already allowing experts to redirect their attention away from time-consuming data wrangling tasks and toward more strategic critical thinking. The more complete adoption of fully optimized AI solutions throughout the industry, made possible by a rising generation of “AI-native” data scientists will only accelerate this phenomenon.

RiskSpan’s commitment to pushing the boundaries of innovation in the Loan and Structured Product Space is underscored by its strategic approach to GenAI. While acknowledging the challenges posed by GenAI, RiskSpan remains poised for the future, leveraging its expertise to navigate the evolving landscape. As the industry anticipates the promised benefits of GenAI, RiskSpan’s vision and applications stand as a testament to its role as a thought leader in shaping the future of data analytics.

Stay tuned for more updates on RiskSpan’s innovative solutions, as we continue to lead the way in harnessing the power of GenAI for the benefit of our clients and the industry at large.

RiskSpan’s Top 3 GenAI Applications for 2024

In the dynamic landscape of fixed-income securities, the role of generative artificial intelligence (GenAI) has become increasingly prominent. This transformative force is shaping the future of data, analytics, and predictive modeling, presenting both challenges and opportunities for industry leaders.

First, the challenges:

Managing GenAI applications in a responsible and ethical manner requires developers to be mindful of data security, data integrity, respecting intellectual property, and compliance standards, among other considerations. To this end, RiskSpan:

Maintains control over its data within its AWS instance and shares data with AI models solely for processing requests
Employs data encryption during transit and at rest to ensure confidentiality and access controls to restrict unauthorized data access within the AWS environment.
Affirms client ownership of inputs and outputs generated by the AI model’s API, ensuring data integrity and compliance with regulatory requirements.
Supports common compliance standards, including GDPR and HIPAA.

Standing at the forefront of this evolution within the loans and structured products space, RiskSpan is actively furthering the advancement of three specific GenAI applications aimed at transforming how market participants work and maximizing their efficiency and performance.

1. Modeling Private Credit Transactions

Execution code for cash flow generation and valuation utilizes RiskSpan’s validated analytics routines, such as day count handling, payment calculations, discounting, etc.

2. Tape-Cracking 3.0: Making RiskSpan’s Smart Mapper Even Smarter

In the coming months, the Platform’s existing ML-driven ETL process will be further refined to leverage the latest GenAI technology.

3. “Insight Support”

RiskSpan refers to this workflow augmentation as “Insight Support.”

Conclusion

Stay tuned for more updates on RiskSpan’s innovative solutions, as we continue to lead the way in harnessing the power of GenAI for the benefit of our clients and the industry at large.

Use Case: RiskSpan’s Agentic AI for MBS Data Tool

RiskSpan Launches Agentic AI for MBS Data — Instant, Transparent Insights for Agency MBS Professionals

Humans in the Loop: Ensuring Trustworthy AI in Private ABF Deal Modeling

The AI-Powered Workflow: What’s Automated

But What About Hallucinations?

Humans in the Loop: Three Layers of Oversight

Looking Ahead: RAG and Continuous Improvement

The Takeaway

Design Smarter — How AI is Changing UX from Idea to Execution

Initial Ideation

Rapid Prototyping

Code Development

Real-World Application and Testing — Collaboration and Continuous Improvement

From AI Hype to Helpful Assistant: AI Agents are coming soon to the RiskSpan Platform!

AI-Powered Code Reviews

The Problem: Time-consuming pull requests

The Solution: An AI Merge Agent

Why this matters for our development lifecycle

What’s Next?

AI Prompt Structuring — Does it Even Matter?

GenAI Applications for Loans and Mapping Data

1. Tape-Cracking 3.0: Making RiskSpan’s Smart Mapper Even Smarter

2. Modeling Private Credit Transactions

3. “Insight Support”

Conclusion

RiskSpan’s Top 3 GenAI Applications for 2024

1. Modeling Private Credit Transactions

2. Tape-Cracking 3.0: Making RiskSpan’s Smart Mapper Even Smarter

3. “Insight Support”

Conclusion

Company

Products

Security & Compliance