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Articles Tagged with: Loans

ABA Landing Page — 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.

Resi Loan Investor? We Have You Covered There, Too!

Riskspan
  • Outsource the Heavy Lifting of Consolidating and Mapping Servicer Data: Powered by Smart Mapping and Optimized QC rules, RiskSpan automates data ingestion across multiple servicers and data sources:
  • Dynamic Query/Filter Loan Data and Historical Performance Metrics:  Analyze loan data using query/filter and custom composition reports; Generate customized data visualization reports
  • Loan Bid Analysis Trading Quality Risk Models, Loan-Level Valuations: RiskSpan has purpose-built tools and models to support active buyers/sellers of whole loans  
     
  • Portfolio Risk Management Powerful Scalability for Daily Analytics.

Why Private Credit Investors Choose RiskSpan

  • Eliminate manual surveillance bottlenecks that delay critical performance insights.

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

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

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

Unlock the Power of AI for Private Credit Investing

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

Product Summary

Introductory Presentation (coming soon)

Model Documentation (coming soon)

Built for Speed, Scale and Affordability

Cloud-Native for 15 Years

Get a Free Trial or Demo

Resources

view all

Private Credit Investors

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


RiskSpan’s April 2025 Models & Market Call: Credit Model v7, Prepay Volatility, and Credit Trends to Watch

Register here for our next monthly model update call: Thursday, May 15th at 1:00 ET.

Note: This post contains highlights from our April 2025 monthly modeling call, which delivered insights into the current economic climate, mortgage model enhancements, and borrower behavior trends. You can register here to watch a recording of the full 28-minute call.

Here’s what you missed:

Market Overview: A Climate of Volatility

With mortgage rates rebounding to 7%, the panel began by acknowledging the choppy waters ahead, flagging 2025 as a year likely to see persistent rate volatility. As recession risks grow and consumer stress indicators rise, modeling accuracy becomes more important than ever.

Notably, consumers are already strained:

  • Rising consumer debt burdens
  • Increased use of personal loans and second liens for debt consolidation
  • Spikes in HEL/HELOC originations and securitizations
  • Climbing Non-QM delinquencies, particularly among 2022–2023 vintages

Model Update: Credit Model v. 7.0

RiskSpan’s newly released Credit Model v7 marks a significant upgrade in loan performance modeling:

  • Delinquency Transition Matrix core structure
  • The model projects:
    • Monthly CDR, CPR, and delinquency balances (0 through REO)
    • Loss severities, liquidated balances, and P&I flows
  • Modular components include:
    • State Transition Model
    • Severity and Liquidation Timeline Modules
  • The model is fully integrated within RiskSpan’s platform, enabling custom inputs for whole loans and securities

This model empowers users with granular delinquency and cash flow forecasting, critical for managing portfolios amid market uncertainty.


Key findings here included:

  • Daily prepay data showing extreme volatility, but offering early trend visibility
  • Trend lines derived from daily data offering good proxies for future behavior
  • Notable discrepancies within MBS-level data, especially among higher-coupon pools

RiskSpan’s continued focus on benchmarking these data sources helps refine both near-term and long-term modeling strategies.


Prepayment Behavior of Top-Tier Borrowers

The panel spotlighted borrowers with FICO scores over 800, revealing some counterintuitive dynamics:

  • Initial refinance activity is higher in the 800+ cohort—”fastest out of the gate”
  • But post-seasoning, refinance rates fall below those of the 700–750 FICO group
  • This “crossover pattern” reflects a phenomenon the team called “Accelerated Burnout”
  • Assumed strategic behavior, like exploiting lender credits, may amplify early refinance intensity

These insights underscore the nonlinear and evolving nature of borrower behavior, especially under fluctuating rate environments.


Model Performance: Staying on Track

RiskSpan’s Prepayment Model continues to track closely with actuals, validating its calibration even in today’s turbulent landscape. Combined with Credit Model v7, clients now have powerful tools for capturing credit and prepayment risk with more accuracy than ever.

Be sure to register for next month’s model update call on Thursday, May 15th 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.



Insurance Solutions

Insurance Solutions

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

Ready to see it in action?

What's holding your portfolio back?

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

RiskSpan changes that.

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

What we solve

Fragmented Data. Unified.

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

  • Clean data. Cloud-native. Instantly available.

  • Snowflake and Databricks-ready.


Compliance Without the Headache

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

  • SOC 1 and SOC 2 certified

  • Full coverage of loans and securities

Surveillance that Saves Time

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

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

  • Real-time dashboards and alerts


Smarter Scenarios Across All Assets

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

  • VaR, tail risk, correlation, credit risk

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


One Connected Investment + Risk Workflow

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

  • Pre-trade credit memos through post-trade surveillance

  • API, dashboard, and reporting access for every team

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

What's under the hood

  • AI-Powered Document Parsing & Deal Modeling

  • End-to-End CECL Processing

  • Regulatory Reporting Engine

  • Cross-Asset Stress Testing

  • SOC 2 Secure + Cloud Native

  • Easy API + Web UI Access

AI-Driven Data Extraction & Structuring

Turn Unstructured Deal Data into Actionable Intelligence

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

Advanced Cash Flow Modeling for Private ABF Portfolios

Scalable, Customizable AI-Powered Cash Flow Analytics

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

Private ABF Portfolio Risk & Surveillance

Comprehensive Risk Management & Real-Time Monitoring

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

Why Insurers Choose RiskSpan for Private ABF Analytics

  • Eliminate manual surveillance bottlenecks that delay critical performance insights.

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

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

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

Unlock the Power of AI for Private Credit Investing

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

Product Summary

Introductory Presentation (coming soon)

Model Documentation (coming soon)

Built for Speed, Scale and Affordability

Cloud-Native for 15 Years

Get a Free Trial or Demo

Resources

view all

Private Credit Investors

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


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.



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.

Resi Loan Investor? We Have You Covered There, Too!

Riskspan

  • Outsource the Heavy Lifting of Consolidating and Mapping Servicer Data: Powered by Smart Mapping and Optimized QC rules, RiskSpan automates data ingestion across multiple servicers and data sources:
  • Dynamic Query/Filter Loan Data and Historical Performance Metrics:  Analyze loan data using query/filter and custom composition reports; Generate customized data visualization reports
  • Loan Bid Analysis Trading Quality Risk Models, Loan-Level Valuations: RiskSpan has purpose-built tools and models to support active buyers/sellers of whole loans  
     
  • Portfolio Risk Management Powerful Scalability for Daily Analytics.

Why Private Credit Investors Choose RiskSpan

  • Eliminate manual surveillance bottlenecks that delay critical performance insights.

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

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

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

Unlock the Power of AI for Private Credit Investing

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

Product Summary

Introductory Presentation (coming soon)

Model Documentation (coming soon)

Built for Speed, Scale and Affordability

Cloud-Native for 15 Years

Get a Free Trial or Demo

Resources

view all

Private Credit Investors

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


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

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


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.


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.


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