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GenAI Applications for Loans and Private Credit

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.


Celebrating Women’s Contributions by the Numbers

Because we’re a data company after all. RiskSpan commemorates International Women’s Day by taking note of the remarkable people behind these numbers.

Martha Stewart

Votes for Women

Serena Williams

Women's March in DC

Girls Who Code

Title IX

Sally Ride

Womens Rights

Taylor Swift

Sandra Day O'connor

Kathryn Blgelow

Betty White


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

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

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

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

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

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

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

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

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

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

### 

About RiskSpan, Inc. 

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

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


What is the Draw of Whole Loan Investing?

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

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

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

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

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

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

Who is investing in whole loans currently?

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

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

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

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

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

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

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

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

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


RiskSpan, Dominium Advisors Announce Market Color Dashboard for Mortgage Loan Investors


ARLINGTON, Va., January 24, 2024 – RiskSpan, the leading tech provider of data management and analytics services for loans and structured products, has partnered with tech-enabled asset manager Dominium Advisors to introduce a new whole loan market color dashboard to RiskSpan’s Edge Platform.

This new dashboard combines loan-level market pricing and trading data with risk analytics for GSE-eligible and non-QM loans. It enables loan investors unprecedented visibility into where loans are currently trading and insight on how investors can currently achieve excess risk-adjusted yields.

Dashboard

The dashboard highlights Dominium’s proprietary loan investment and allocation approach, which allows investors to evaluate any set of residential loans available for bid. Leveraging RiskSpan’s collateral models and risk analytics, Dominium’s software helps investors maximize yield or spread subject to investment constraints, such as a risk budget, or management constraints, such as concentration limits.

“Our strategic partnership with RiskSpan is a key component of our residential loan asset management operating platform ,” said Peter A. Simon, Founder and CEO of Dominium Advisors. “It has enabled us to provide clients with powerful risk analytics and data management capabilities in unprecedented ways.”

“The dashboard is a perfect complement to our suite of analytical tools,” noted Janet Jozwik, Senior Managing Director and Head of Product for RiskSpan’s Edge Platform. “We are excited to be a conduit for delivering this level of market color to our mortgage investor clients.”

The market color dashboard (and other RiskSpan reporting) can be accessed by registering for a free Edge Platform login at https://riskspan.com/request-access/.

### 

About RiskSpan, Inc. 

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

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

About Dominium Advisors Dominium Advisors is a tech-enabled asset manager specializing in the acquisition and management of residential mortgage loans for insurance companies and other institutional investors. The firm focuses on newly originated residential mortgage loans made to high quality borrowers – GSE eligible, jumbo and non-QM. Its proprietary loan-level software makes possible the construction of loan portfolios that achieve investor defined objectives such as higher risk-adjusted yields and spreads or limited exposure to tail risk events. Learn more at dominiumadvisors.com.


The future of analytics pricing is RiskSpan’s Usage-based delivery model

Usage-based pricing model brings big benefits to clients of RiskSpan’s Edge Platform

Analytic solutions for loans, MSRs and structured products are typically offered as software-as-a-service (SaaS) or “on-prem” products, where clients pay a monthly or annual fee to access the software and its features. The compute needed to run analytic workloads is typically purchased in advance and is fixed regardless of the need or use case.  

However, this traditional pricing model is not always the best fit for the dynamic and diverse needs of analytics users. It is technologically outdated and does not meet users where they are – with varying data volumes, usage patterns, and analytical complexity requirements that fluctuate with the markets. It is simply wasteful for companies to pay for unused, fixed-fee compute capacity, year-after-year in long-term, set price contracts, when their needs don’t require it. 

Usage-based pricing is a trend that reflects the evolving nature of analytics and the increasing demand for more flexible, transparent, and value-driven pricing models.

RiskSpan has just announced the release of industry-innovating usage-based pricing that allows clients to scale up or down, based on their needs. Further, clients of the RiskSpan platform will now benefit from access to the full Edge Platform, including data, models and analytics – eliminating the need to license individual product modules. The Platform supports loans, MSRs and securities, with growing capabilities around private credit. Analyzing these assets can be compute- and data-intensive because of the need for collateral (loan-level) data and models to price, value, and calculate risk metrics.

A Single Platform
Integrated Data | Trade Analytics | Risk Management

Core Engine

Usage-based pricing is an innovative alternative approach based on user-configured workloads. It enables RiskSpan to invoice its clients according to how much compute they actually need and use, rather than a fixed fee based on the modules they purchased during the last budget cycle.  

Usage-based pricing benefits RiskSpan clients in several ways, including: 

    • Lower Costs: Clients pay only for what they need, rather than being locked into an expensive contract that may not suit their current or future situation.

    • Cost-Sharing Across the Enterprise: Clients can share costs across the enterprise and better manage expense based on usage by internal functions and business units.

    • Transparency: Clients can monitor their usage and directly link their analytics configuration and usage to their results and goals. They can also better control their spending, as they can track their usage and see how it affects their bill.

    • Flexibility: Clients can experiment with different features and options of the Platform, as they are not restricted by a predefined package or plan.

Usage-based pricing is not a one-size-fits-all solution, and it may not be suitable for every organization. Based on needs, large enterprise workloads will require specific, customized licensing and may benefit from locked in compute that comes with volume discounts.

Bottom Line on RiskSpan’s Usage-based Pricing Model

CONS of Traditional Fixed Fee Pricing PROS of Usage-Based Pricing
Flat-fee pricing models force customers to pay for unused capacity​. Lower Costs — Pay only for what you use, not the wasted capacity of a dedicated cluster
Unused capacity cannot be shared across the enterprise, which translates into wasted resources and higher costs. Cost Sharing — Costs can be shared across the enterprise to better manage expense based on usage by your internal functions and business units
Fixed pricing models make it difficult for customers to scale up or down as needed. Transparency — Transparent pricing that fits your specific analytics workload (size, complexity, performance)
Traditional “product module-based” purchasing runs the risk of over-buying on features that will not be used. Flexibility — Scale up and scale down your use as new and in-place features become useful to you under different market conditions

With the introduction of usage-based pricing, RiskSpan is adding core value to its Edge Platform and a low-cost entry point to bring its solution to a wider base of clients. Its industry-leading capabilities solve challenges facing various users in the loans, MSR, and structured portfolio domains. For example:

    1. Loan/MSR Trader seeks analytics to support bidding on pools of loans and/or MSRs. Their usage is ad-hoc and will benefit from usage-based pricing. Traders and investors can analyze prepay and credit performance trends by leveraging RiskSpan’s 20+ years of historical performance datasets.

    1. Securities Trader (Agency or Non-Agency) wants more flexibility to set their prepay or credit model assumptions to run ad-hoc scenario analysis not easily handled by their current vendor.

    1. Risk Manager wants another source of valuation for periodic MSR and loan portfolios to enhance decision making and compare against the marks from their third-party valuation firm. 

    1. Private Credit Risk Manager needs a built-for-purpose private credit analytics system to properly run risk metrics. Users can run separate and run ad hoc analysis on these holdings.

For more specific information about how RiskSpan will structure pricing with various commitment levels, click below to tell us about your needs, and a representative will be in touch with you shortly. 


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

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.

2. 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.

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.


Connect with us at SFVegas 2024

Click Here to book a time to connect

RiskSpan is delighted to be sponsoring SFVegas 2024!

Connect with our team there to learn how we can help you move off your legacy systems, streamline workflows and transform your data.

SFA-Attendees
Click Here to book a time to connect

Don’t miss these RiskSpan presenters at SFVegas 2024

Bernadette Kogler

Housing Policy:
What’s Ahead
Mon, Feb 26th, 1:00 PM

Tom Pappalardo

Future of Fintech
Wed, Feb 28th, 9:15 AM

Divas Sanwal Photo (3)

Divas Sanwal

Big Data & Machine Learning: Impacts on Origination
Wed, Feb 28th, 11:05 AM

Can’t make the panels?

Click here to make an appointment to connect. Or just stop by Booth 13 in the exhibit hall!


Impact of Mr. Cooper’s Cyber Security Incident on Agency Prepayment Reporting

Amid the fallout of the cyberattack against Mr. Cooper on October 31st was an inability on the large servicer’s part to report prepayment activity to investors.

According to Freddie Mac, the incident “resulted in [Mr. Cooper’s] shutting down certain systems as a precautionary measure. As a result, Freddie Mac did not receive loan activity reporting, which includes loan payoffs and payment corrections, from Mr. Cooper during the last few days of the reporting period related to October loan activity.”

Owing to Mr. Cooper’s size, were curious to measure what (if any) impact its missing days of reporting might have on overall agency speeds.

Not a whole lot, it turns out.

This came as little surprise given the very low prepayment environment in which we find ourselves, but we wanted to run the numbers to be sure. Here is what we found.

We do not know precisely how much reporting was missed and assumed “the last few days of the reporting period” to mean 3 days.

Assuming 3 days means that Mr. Cooper’s reported speeds of 4.5 CPR to Freddie and 4.6 CPR to Fannie likely should have been 5.2 CPR and 5.4 CPR, respectively. While these differences are relatively small for to Mr. Cooper’s portfolio (less than 1 CPR) the impact on overall Agency speeds is downright trivial — less than 0.05 CPR.

Fannie MBSFreddie MBS
Sch. Bal.195,221,550,383168,711,346,228
CPR (reported)4.64.5
CPR (estimated*)5.45.2
*assumes three days of unreported loan activity and constant daily prepayments for the month

Fannie Mae and Freddie Mac will distribute scheduled principal and interest when servicers do not report the loan activity. Prepayments that were not reported “will be distributed to MBS certificateholders on the first distribution date that follows our receipt and reconciliation of the required prepayment information from Mr. Cooper.”


Watch Suhrud Dagli Discuss AI in Securities Analytics at Chartis Research RiskTech100 Conference

Day 3 - 9.55 - Using AI in securities analytics
Watch Recording

(Register for Day 3)

Register to watch: www.risktech100.com


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