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Category: Case Study

Case Study: How one investor moved to loan level analysis while reducing their costs

Learn more about how one mortgage investor successfully overhauled their analytics computational processing with RiskSpan. The investor migrated from a daily pricing and risk process that relied on tens of thousands of rep lines to one capable of evaluating each of the portfolio’s more than three-and-a-half million loans individually (and how they actually saved money in the process). 

The Situation 

A RiskSpan client was managing a large investment portfolio of mortgage servicing rights (MSR) assets, residential loans and securities. The investor runs a battery of sophisticated risk management analytics that rely on stochastic modeling. Option-adjusted spread, duration, convexity, and key rate durations are calculated based on more than 200 interest rate simulations.

They used rep lines for one main reason: they needed a way to manage computational loads on the server and improve calculation speeds. Secondarily, organizing the loans in this way simplified their reporting and accounting requirements to a degree (loans financed by the same facility were grouped into the same rep line).  

This approach had some significant downsides. Pooling loans by finance facility was sometimes causing loans with different balances, LTVs, credit scores, etc., to get grouped into the same rep line. This resulted in prepayment and default assumptions getting applied to every loan in a rep line that differed from the assumptions that likely would have been applied if the loans were being evaluated individually. 

The Challenge 

The main challenge was the investor’s MSR portfolio—specifically, the volume of loans trying to be run. The client has close to 4 million loans spread across nine different servicers. This presented two related but separate sets of challenges. 

The first set of challenges stemmed from needing to consume data from different servicers whose file formats not only differed from one another but also often lacked internal consistency. Even the file formats from a single given servicer tended to change from time to time. This required RiskSpan to continuously update its data mappings and (because the servicer reporting data is not always clean) modify QC rules to keep up with evolving file formats.  

The second challenge related to the sheer volume of compute power necessary to run stochastic paths of Monte Carlo rate simulations on 4 million individual loans and then discount the resulting cash flows based on option adjusted yield across multiple scenarios. 

And so there were 4 million loans times multiple paths times one basic cash flow, one basic option-adjusted case, one up case, and one down case—it’s evident how quickly the workload adds up. And all this needed to happen on a daily basis. 

To help minimize the computing workload, this client had been running all these daily analytics at a rep-line level—stratifying and condensing everything down to between 70,000 and 75,000 rep lines. This alleviated the computing burden but at the cost of decreased accuracy because they could not look at the loans individually.

The Solution 

The analytics computational processing RiskSpan implemented ignores the rep line concept entirely and just runs the loans. The scalability of our cloud-native infrastructure enables us to take the three-and-a-half million loans and bucket them equally for computation purposes. We run a hundred loans on each processor and get back loan-level cash flows and then generate the output separately, which brings the processing time down considerably. 

For each individual servicer, RiskSpan leveraged its Smart Mapper technology and Configurable QC feature in its Edge Platform to create a set of optimized loan files that can be read and rendered “analytics-ready” very quickly. This enables the loan-level data to be quickly consumed and immediately used for analytics without having to read all the loan tapes and convert them into a format that an analytics engine can understand. Because RiskSpan has “pre-processed” all this loan information, it is immediately available in a format that the engine can easily digest and run analytics on. 

An investor in any mortgage asset benefits from the ability to look at and evaluate loan characteristics individually. The results may need to be rolled up and grouped for reporting purposes. But being able to run the cash flows at the loan level ultimately makes the aggregated results vastly more meaningful and reliable. A loan-level framework also affords whole-loan and securities investors the ability to be sure they are capturing the most important loan characteristics and are staying on top of how the composition of the portfolio evolves with each day’s payoffs. 


Case Study: Using Snowflake to Create Single Family Credit Risk Grids for a Federal Agency

The Client

Government Sponsored Enterprise (GSE)

The Problem

The client sought to transition its ERCF spot capital reporting process from legacy systems and processes to a new, fully integrated system with automated processes. 

This required the re-creation and automation in Snowflake of a legacy report for FHFA consisting of 30 credit risk and risk factor grids rolled up from the loan level.

The Solution

RiskSpan led a cross-functional effort including the data and reporting teams to implement a fully automated report using data and SQL in Snowflake.

The Deliverables

  • Loan attributes re-mapped from legacy data to Snowflake data
  • Reverse-engineered logic mapping attribute values to grid cohorts​
  • Complex and efficient SQL developed in Snowflake to transform loan-level spot capital data into cohorts for credit risk grids​
  • Conversion of 13 million loan records into more than 2,200 grid cells in less than 3 minutes​
  • Design and execution UAT​ in cooperation with the business team
  • Fully automated FHFA credit risk report populated by calling SQL

Case Study: Hadoop to Snowflake Migration

The Client

Government Sponsored Enterprise (GSE)

The Problem

The client sought to improve the performance and forecasting capabilities of its loan valuation and forecast engine. As part of this strategic initiative, the client planned to migrate the underlying platform from Hadoop to the Snowflake Data Cloud to achieve an increase in data loading and querying speeds and an overall optimization of system performance.​

RiskSpan identified a need for project management and implementation planning, as well as data pipeline and ETL migration analysis to ensure a successful integration of the Snowflake data cloud into the loan valuation and forecast engine.​​

The Solution

RiskSpan led the data migration effort for the loan valuation engine and integrated its pipelines from multiple data sources. The RiskSpan team also executed planning, testing, and overall project management of the implementation effort to ensure a high quality, on-schedule delivery.

The Deliverables

  • An integrated project plan with transition from current state to target state and production parallel
  • A system and data flow comparing existing state to target state
  • SQL code to efficiently compare 13 million records and more than 100 attributes loaded to Snowflake with legacy data in just 2 minutes.
  • Review of target state database ETL patterns
  • Review of loan valuation engine output using data in Snowflake
  • Comprehensive report presented to Senior Management

How Rithm Capital leverages RiskSpan’s expertise and Edge Platform to enhance data management and achieve economies of scale

 

BACKGROUND

 

One of the nation’s largest mortgage loan and MSR investors was hampered by a complex data ingestion process as well as slow and cumbersome on-prem software for pricing and market risk.

A complicated data wrangling process was taking up significant time and led to delays in data processing. Further, month-end risk and financial reporting processes were manual and time-pressured. The data and risk teams were consumed with maintaining the day-to-day with little time available to address longer-term data strategies and enhance risk and modeling processes.

 

OBJECTIVES

  1. Modernize Rithm’s mortgage loan and MSR data intake from servicers — improve overall quality of data through automated processes and development of a data QC framework that would bring more confidence in the data and associated use cases, such as for calculating historical performance.

  2. Streamline portfolio valuation and risk analytics while enhancing granularity and flexibility through loan-level valuation/risk.

  3. Ensure data availability for accounting, finance and other downstream processes.

  4. Bring scalability and internal consistency to all of the processes above.

THE SOLUTION



THE EDGE WE PROVIDED

By adopting RiskSpan’s cloud-native data management, managed risk, and SaaS solutions, Rithm Capital saved time and money by streamlining its processes

Adopting Edge has enabled Rithm to access enhanced and timely data for better performance tracking and risk management by:

  • Managing data on 5.5 million loans, including source information and monthly updates from loan servicers (with ability in the future to move to daily updates)
  • Ingesting, validating and normalizing all data for consistency across servicers and assets
  • Implementing automated data QC processes
  • Performing granular, loan-level analysis​

 


With more than 5 million mortgage loans spread across nine servicers, Rithm needed a way to consume data from different sources whose file formats varied from one another and also often lacked internal consistency. Data mapping and QC rules constantly had to be modified to keep up with evolving file formats. 

Once the data was onboarded Rithm required an extraordinary amount of compute power to run stochastic paths of Monte Carlo rate simulations on all 4 million of those loans individually and then discount the resulting cash flows based on option adjusted yield across multiple scenarios.

To help minimize the computing workload, Rithm had been running all these daily analytics at a rep-line level—stratifying and condensing everything down to between 70,000 and 75,000 rep lines. This alleviated the computing burden but at the cost of decreased accuracy and limited reporting flexibility because results were not at the loan-level.

Enter RiskSpan’s Edge Platform.

Combining the strength of RiskSpan’s subject matter experts, quantitative analysts, and technologists together with the power of the Edge platform, RiskSpan has helped Rithm achieve its objectives across the following areas: 

Data management and performance reporting

  • Data intake and quality control for 9 servicers across loan and MSR portfolios
  • Servicer data enrichment
  • Automated data loads leading to reduced processing time for rolling tapes
  • Ongoing data management support and resolution
  • Historical performance review and analysis (portfolio and universe)

Valuation and risk

  • Daily reporting of MSR, mortgage loan and security valuation and risk analytics based on customized Tableau reports
  • MSR and whole loan valuation/risk calculated based at the loan-level leveraging the scalability of the cloud-native infrastructure
  • Additional scenario analysis and other requirements needed for official accounting and valuation purposes

Interactive tools for portfolio management

  • Fast and accurate tape cracking for purchase/sale decision support
  • Ad-hoc scenario analyses based on customized dials and user-settings

The implementation of these enhanced data and analytics processes and increased ability to scale these processes has allowed Rithm to spend less time on day-to-day data wrangling and focus more on higher-level data analysis and portfolio management. The quality of data has also improved, which has led to more confidence in the data that is used across many parts of the organization.


LET US BUILD YOUR SOLUTION

Models + Data management = End-to-end Managed Process

The economies of scale we have achieved by being able to consolidate all of our portfolio risk, interactive analytics, and data warehousing onto a single platform are substantial. RiskSpan’s experience with servicer data and MSR analytics have been particularly valuable to us.

          — Head of Analytics


RS Edge Platform Implementation Streamlined Processes Reducing Client Resource Support Needs by 46%-VERSION 2

Asset Manager | New York, NY

RiskSpan Applications Provided

MARKET RISK ANALYTICS

MODELS & FORECASTING

MODEL VALIDATION

GOVERNANCE

ABOUT THE CLIENT

A leading provider of capital and services to the mortgage and financial services industries that leverage their proven investment expertise and identity and invest in assets that offer attractive risk-adjusted returns while also protecting our existing portfolio and generating long-term value for our investors.


PROBLEM

An asset manager sought to replace an inflexible risk system provided by a Wall Street dealer. ​The portfolio was diverse, with a sizable concentration in structured securities and mortgage assets. ​

The legacy analytics system was rigid with no flexibility to vary scenarios or critical investor and regulatory reporting.


CHALLENGE

Lacked a single-solution

Data integrity issues

Inflexible locally installed risk management system

No direct connectivity to downstream systems

Models + Data management = End-to-end Managed Process


HIGHLIGHTS

GET STARTED

5 Vendors → Single Platform

32% Annual Cost Savings

Private Label SecuritiesIncreased Flexibility

Additional

DOWNLOAD CASE STUDY


SOLUTION

RiskSpan’s Edge Platform delivered a cost-efficient and flexible solution by bundling required data feeds, predictive models for mortgage and structured products, and infrastructure management. ​

The Platform manages and validates the asset manager’s third-party and portfolio data and produces scenario analytics in a secure hosted environment.


TESTIMONIAL

”Our existing daily process for calculating, validating, and reporting on key market and credit risk metrics required significant manual work… [Edge] gets us to the answers faster, putting us in a better position to identify exposures and address potential problems.” 

          — Managing Director, Securitized Products


EDGE PROVIDED

END-TO-END DATA AND RISK MANAGEMENT PLATFORM 

  • Scalable, cloud native technology
  • Increased flexibility to run analytics at loan level; additional interactive / ad-hoc analytics
  • Reliable accurate data with frequent updates

COST AND OPERATIONAL EFFICIENCIES GAINED

  • Streamlined workflows | Automated processes
  • 32% annual cost savings
  • 46% fewer resources needed for maintenance
  •  



RS Edge Platform Implementation Streamlined Processes Reducing Client Resource Support Needs by 46%-VERSION 1

 

AT-A-GLANCE

An asset manager sought to replace an inflexible risk system provided by a Wall Street dealer. ​The portfolio was diverse, with a sizable concentration in structured securities and mortgage assets. ​

The legacy analytics system was rigid with no flexibility to vary scenarios or critical investor and regulatory reporting.


5 Vendors → Single Platform

32% Annual Cost Savings

Private Label SecuritiesIncreased Flexibility

Additional Ad-hoc Analytics


”Our existing daily process for calculating, validating, and reporting on key market and credit risk metrics required significant manual work… [Edge] gets us to the answers faster, putting us in a better position to identify exposures and address potential problems.” 

          — Managing Director, Securitized Products 

LET US BUILD YOUR SOLUTION

Models + Data management = End-to-end Managed Process

 

CHALLENGES

Lacked a single-solution

Data integrity issues

Inflexible locally installed risk management system

No direct connectivity to downstream systems


SOLUTIONS

RiskSpan’s Edge Platform delivered a cost-efficient and flexible solution by bundling required data feeds, predictive models for mortgage and structured products, and infrastructure management. ​

The Platform manages and validates the asset manager’s third-party and portfolio data and produces scenario analytics in a secure hosted environment. 


 

EDGE WE PROVIDED

End-to-end data and risk management platform

  • Scalable, cloud native technology
  • Increased flexibility to run analytics at loan level; additional interactive / ad-hoc analytics
  • Reliable accurate data with frequent updates

Cost and operational efficiencies gained

  • Streamlined workflows | Automated processes
  • 32% annual cost savings
  • 46% fewer resources needed for maintenance


Institutionally Focused Broker-Dealer: Product Service

As a new MBS operation, this institutional broker-dealer needed trade capture and analytics functionality, particularly for risk management purposes. The broker-dealer also required an application to track MBS pass-through positions in real-time, given the active trading style of its pass-through desk (an average of 3 trades per minute).

The Solution

The client adopted the Edge Platform and RiskSpan provided custom development services that included:

  • A real-time  pass-through matrix  Start-of-Day/ Intra-day firm-wide position upload (taking a feed from a proprietary books-and-records system)
  • Real-time trade capture from Bloomberg and internal sources

The pass-though desk actively used the pass-through matrix for several years. When the client developed its own internal solution, it continued using the Edge Platform to run daily risk scenarios on the firm’s positions.

Total development time for all these projects was about 6 weeks.


National Property and Casualty Insurance Carrier : Claims Platform Migration

A national property and casualty insurance carrier was struggling with an antiquated claims platform. Built on the IBM AS400 Mainframe system, the existing platform was unable to scale up to the growing needs of the organization and was based on legacy code plagued with significant “technical debt.

The Solution

RiskSpan partnered with the client to identify and vet out a cloud-based SaaS solution provider to function as the system of record for all claims processed within the organization. This partnership ran from the discovery phase all the way through the production roll-out and post roll-out business-as-usual phase.  

RiskSpan also assisted with the data migration ETL project necessary to transfer existing open and recent claims to the new platform. All existing interfaces with internal systems and third parties were reconfigured to be functional with the new claims platform. 

SPEAK TO AN EXPERT

Client Benefit

Cloud adoption enabled the client to improve its technology capability score from AM Best Ratings, a key metric for evaluating the health of insurance carriers. 

Project deliverables included: 

  • Documentation of the current state of all internal and external interfaces  
  • Design and Solution Architecture for impacted interfaces 
  • API Design and Data Normalization across the Claims enterprise 
  • API Interfaces using Reactive Programming principles 
  • Implementation of the required security compliance for all data transmissions to and from the public cloud 
  • Integration with Azure identity systems for SSO Integration 
  • Automated Integration testing 
  • Data Lake on SQL Server to facilitate data migration 
  • Financial Reporting from Data Lake using Tableau Server 
  • Agile Project Delivery with 4-week sprints using SAFe Release Trains. 
  • Technology Stack – Java, Spring Cloud, Spring Boot, Spring Security, AS400 DataQueues, Azure SSO, JIRA, Jenkins, Gradle, OAuth 2, Tomcat, GIT, Gerrit, JSON, XML, SQL, Stored Procedures. 


National Property and Casualty Insurance Carrier: Customer Self Service Portal

A national property and casualty insurance carrier needed to modernize its customer selfservice portal. The complexity of the existing portal made it difficult for customers to find what they were looking for, resulting in declining customer engagement. The existing system was also inflexible and failed to align with the company’s new products and brand identity. 

The Solution

RiskSpan led the development of a state-of-the-art, web-based application enabling customers to resolve a full range of self-service needs without resorting to customer service callThe solution was developed using the Design Thinking approach, putting users first and offering a simplified and seamless experience when servicing policies.  

Client Benefits

The technical architecture laid the foundation for all future web-based applications to be developed within the organization. 

The new portal was sufficiently flexible to support rapid deployment of new features relating to the changing product landscape owing to increases in recent catastrophic events. 

Other client deliverables included: 

  • Technical Architecture for the proposed JavaScript front-end platform which would be supported by a micro-services based backend system 
  • Proof of Concept delivery with Rapid Prototyping 
  • Implemented a React based Front end application with reusable component libraries 
  • Component libraries have been implemented in the organization’s new branding aesthetic and will be made available through an internal component repository that can be leveraged by all applications. 
  • Optimized solution to meet future performance demands and scaling to ensure a consistent user experience 
  • A/B Testing capabilities to ensure continual user engagement 
  • Agile Project Delivery with 2-week sprints using SAFe Release Trains 
  • Kanban Development adopted post production roll-out with interrupt capacity planning to rapidly address issues that might come up in production. 
  • Unified code-base to support application delivery over mobile apps as well 
  • Technology Stack – Typescript, React.js, React Native, Material UI, Redux, Java, Spring Boot, Spring Security, JIRA, Jenkins, Gradle, OAuth 2, Node.js, Tomcat, GIT, Gerrit, JSON. 


Top 10 National Mortgage Servicer: MSR Pricing Model Review, Analysis and Enhancements

One of the nation’s leading mortgage lenders had recently acquired several large MSR portfolios and required assistance reviewing, documenting and recommending enhancements to the underlying assumptions of the model used to price the MSR portfolios at acquisition.

Requiring review and documentation included collateral assumptions, cost and revenue assumptions, and prepayment (CDR/CRR/CPR) assumptions.

The Solution

RiskSpan comprehensively analyzed the cash flow impact of each major assumption (e.g., CDR/CRR/CPR, servicing advances, fees, cost) — the collateral assumptions in the model as well as documented forecast vs. actual outcomes.

RiskSpan worked in concert with the servicer’s finance and pricing teams to collect and analyze roll rates and to forecast actual loan-level data around losses, servicing advances, servicing fees, ancillary fees, PIF, and scheduled principal payments.  

Deliverables 

A comprehensive pricing model validation report that included the following:

  • Consolidated CDR-, CRR-, CPR-related pricing model data, including balance, delinquency status, recapture, scheduled payments, default, etc. for all acquired portfolios. The resulting dataset could be used both for deal tracking and pricing model validation 
  • Documentation of the calculation and location of pricing model fields.
  • Reconciliation of the different methods for calculating CDR, CRR, and CPR.
  • Deep dives into model predictions of short sales and foreclosure turn-times
  • Loan-state transition model forecasts and comparison of the model variables between two version of the forecast, including shift analyses.
  • Drivers of forecast variance. 
  • Identification of dials responsible for short sale and foreclosure turn forecast shifting.
  • SAS-based streamlined process for comparing model variables for sub-segment and sub-models in loan state
  • Transition Model:  Incorporation of actual and forecast into pricing models to compare with original pricing model cash flow results for acquired portfolios
  • Creation and standardization of the pricing model validation report output.
  • Automation of reporting.  
  • Improvement of the process by creating a calculation template that could be easily replicated for other portfolios. 
  • Documentation of the validation process and comprehensive review of the validation results with the servicer’s risk team, finance team and pricing team management.


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