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Category: Case Study

Residential Mortgage REIT: End to End Loan Data Management and Analytics

An inflexible, locally installed risk management system with dated technology required a large IT staff to support it and was incurring high internal maintenance costs.

Absent a single solution, the use of multiple vendors for pricing and risk analytics, prepay/credit models and data storage created inefficiencies in workflow and an administrative burden to maintain.

Inconsistent data and QC across the various sources was also creating a number of data integrity issues.

The Solution

An end-to-end data and risk management solution. The REIT implemented RiskSpan’s Edge Platform, which provides value, cost and operational efficiencies.

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

Deliverables 

Consolidating from five vendors down to a single platform enabled the REIT to streamline workflows and automate processes, resulting in a 32% annual cost savings and 46% fewer resources required for maintenance.


GSE: Earnings Forecasting Framework Development

A $100+ billion government-sponsored enterprise with more than $3 trillion in assets sought to develop an end-to-end earnings forecast framework to project and stress-test the future performance of its loan portfolio. The comprehensive framework needed to draw data from a combination of unintegrated systems to compute earnings, capital management requirements and other ad hoc reporting under a variety of internal and regulatory (i.e., DFAST) stress scenarios. 

Computing the required metrics required cross-functional team coordination, proper data governance, and a reliable audit trail, all of which were posing a challenge.  

The Solution

RiskSpan addressed these needs via three interdependent workstreams: 

Data Preparation

RiskSpan consolidated multiple data sources required by the earnings forecast framework. These included: 

  • Macroeconomic drivers, including interest rates and unemployment rate 
  • Book profile, including up-to-date snapshots of the portfolio’s performance data 
  • Modeling assumptions, including portfolio performance history and other asset characteristics 

Model Simulation

Because the portfolio in question consisted principally of mortgage assets, RiskSpan incorporated more than 20 models into the framework, including (among others): 

  • Prepayment Model 
  • Default Model 
  • Delinquency Model 
  • Acquisition Model: Future loans 
  • Severity Model  
  • Cash Flow Model 

Business Calculations and Reporting

Using the data and models above, RiskSpan incorporated the following outputs into the earnings forecast framework: 

  • Non-performing asset treatment 
  • When to charge-off delinquent loans 
  • Projected loan losses under FAS114/CECL  
  • Revenue Forecasts 
  • Capital Forecast 

Client Benefits

The earnings forecast framework RiskSpan developed represented a significant improvement over the client’s previous system of disconnected data, unintegrated models, and error-prone workarounds. Benefits of the new system included:  

  • User Interface – Improved process for managing loan lifecycles and GUI-based process execution  
  • Data Lineage – Implemented necessary constraints to ensure forecasting processes are executed in sequence and are repeatable. Created a predefined, dynamic output lineage tree (UI-accessible) to build robust data flow sequence used to facilitate what-if scenario analysis. 
  • Run Management – Assigned a unique run ID to every execution to ensure individual users across the institution can track and reuse execution results 
  • Audit Trail – Designed logging of forecasting run details to trace attributes such as version changes (Version control system – GIT, SVN), timestamp, run owner, and inputs used (MySQL/Oracle Databases for logging)  
  • Identity Access Management – User IDs and access is now managed administratively. Metadata is captured via user actions through the framework for audit purposes. Role-based restrictions now ensure data and forecasting features are limited to only those who require such permissions 
  • Golden Configuration – Implemented execution-specific parameters passed to models during runtime. These parameters are stored, enabling any past model result to be reproduced if needed 
  • Data Masking – Encrypted personally identifiable information at-rest and in transit 
  • Data Management – Execution logs and model/report outputs are stored to the database and file systems 
  • Comprehensive User and Technical Documentation – RiskSpan created audit-ready documentation tied to logic changes and execution. This included source-to-target mapping documentation and enterprise-grade catalogs and data dictionaries. Documentation also included: 
      • Vision Document 
      • User Guides 
      • Testing Evidence 
      • Feature Traceability Matrix 

Large Asset Manager: Implementation of Comprehensive, Fully-Managed Risk Management Reporting System

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.

The 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. ​

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

The Edge We Provided

”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  


GSE: Datamart Design and Build

The Problem

A government-sponsored enterprise needed a centralized data solution for its forecasting process, which involved cross-functional teams from different business lines.​

The firm also sought a cloud-based data warehouse to host forecasting outputs for reporting purposes with faster querying and processing speeds.​

The firm also needed assistance migrating data from legacy data sources to new datamarts. The input and output files and datasets had different sources and were often in different formats. Analysis and transformation were required prior to designing, developing and loading tables.  

The Solution

RiskSpan built and now maintains a new centralized datamart (in both Oracle and Amazon Web Services) for the client’s revenue and loss forecasting processes. This includes data modeling, historical data upload, and the monthly recurring data process.

The Deliverables

  • Analyzed the end-to-end data flow and data elements​
  • Designed data models satisfying business requirements​
  • Processed and mapped forecasting input and output files​
  • Migrated data from legacy databases to the new sources ​
  • Built an Oracle datamart and a cloud-based data warehouse (Amazon Web Services) ​
  • Led development team to develop schemas, tables and views, process scripts to maintain data updates and table partitioning logic​
  • Resolved data issues with the source and assisted in reconciliation of results

GSE: ETL Solutions

The Problem

The client needed ETL solutions for handling data of any complexity or size in a variety of formats and/or from different upstream sources.​

The client’s data management team extracted and processed data from different sources and different types of databases (e.g. Oracle, Netezza, Excel files, SAS datasets, etc.), and needed to load into its Oracle and AWS datamarts for it’s revenue and loss forecasting processes. ​

The client’s forecasting process used very complex large-scale datasets in different formats which needed to be consumed and loaded in an automated and timely manner.

The Solution

RiskSpan was engaged to design, develop and implement ETL (Extract, Transform and Load) solutions for handling input and output data for the client’s revenue and loss forecasting processes. This included dealing with large volumes of data and multiple source systems, transforming and loading data to and from data marts and data ware houses.

The Deliverables

  • Analyzed data sources and developed ETL strategies for different data types and sources​
  • Performed source target mapping in support of report and warehouse technical designs​
  • Implemented business-driven requirements using Informatica ​
  • Collaborated with cross-functional business and development teams to document ETL requirements and turn them into ETL jobs ​
  • Optimized, developed, and maintained integration solutions as necessary to connect legacy data stores and the data warehouses

Case Study: Web Based Data Application Build

The Client

Government Sponsored Enterprise (GSE)

The Problem

The Structured Transactions group of a GSE needed to offer a simpler way for broker-dealers to  create new restructured securities (improved ease of use), that provided flexibility to do business at any hour and reduce the dependence on Structured Transactions team members’ availability. 

The Solution

RiskSpan led the development of a customer-facing web-based application for a GSE. Their structured transactions clients use the application to independently create pools of pools and re-combinable REMIC exchanges (RCRs) with existing pooling and pricing requirements.​

RiskSpan delivered the complete end-to-end technical implementation of the new portal.

The Deliverables

  • Development included self-service web portal that provides RCR, pool-of-pool exchange capabilities, reporting features ​
  • Managed data flows from various internal sources to the portal, providing real-time calculations​
  • Latest technology stack included Angular 2.0, Java for web services​
  • Development, testing, and config control methodology featured DevOps practices, CI/CD pipeline, 100% automated testing with Cucumber, Selenium​
  • GIT, JIRA, Gherkin, Jenkins, Fisheye/Crucible, SauceLabs, for config control, testing, deployment

Case Study: Web Based Data Application Build

The Client

GOVERNMENT SPONSORED ENTERPRISE (GSE)

The Problem

The Structured Transactions group of a GSE needed to offer a simpler way for broker-dealers to  create new restructured securities (improved ease of use), that provided flexibility to do business at any hour and reduce the dependence on Structured Transactions team members’ availability. 


The Solution

RiskSpan led the development of a customer-facing web-based application for a GSE. Their structured transactions clients use the application to independently create pools of pools and re-combinable REMIC exchanges (RCRs) with existing pooling and pricing requirements.​

RiskSpan delivered the complete end-to-end technical implementation of the new portal.


The Deliverables

  • Development included self-service web portal that provides RCR, pool-of-pool exchange capabilities, reporting features ​
  • Managed data flows from various internal sources to the portal, providing real-time calculations​
  • Latest technology stack included Angular 2.0, Java for web services​
  • Development, testing, and config control methodology featured DevOps practices, CI/CD pipeline, 100% automated testing with Cucumber, Selenium​
  • GIT, JIRA, Gherkin, Jenkins, Fisheye/Crucible, SauceLabs, for config control, testing, deployment

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Case Study: RiskSpan Edge Platform Agency MBS Module

The Client

Multiple Agency Traders and the Research & Strategy Division of a Major Investment Bank

The Problem

RiskSpan leverages its extensive expertise to help clients rapidly access the drivers of prepayment risk and prepayment trends. Our analytical platform provides ultimate flexibility and speed to perform quickly turn securities level data into information to based decisions.

The Solution

The RiskSpan Edge Platform is used by the Agency Trading desk to slice and dice data and look for patterns among various bonds using the graphical interface. The RiskSpan Edge Platform offers users access to current and historical data on Ginnie Mae, Fannie Mae, and Freddie Mac (“Agencies”) pass-throughs as well as other data sets.

The tool provides a flexible user interface that supports analysis of prepayment data and actionable reporting. The database includes all monthly pool level data published by the Agencies dating back to 1995.  This data includes pool factors, geographic concentrations and supplemental pool level collateral information. The Prepayment Analytics tool provides a flexible user interface that supports intuitive analysis of the prepayment data and actionable reporting delivered quickly to decision‐makers. The database includes all monthly data published by the Agencies for all months back to 1995, including factors, geographic breakdowns and supplemental disclosure information.

The Deliverables

RiskSpan provides the tools for comprehensive Agency MBS analysis.

  • Visualizing data with integrated graphing and charting
  • Researching new prepayment trends
  • Creating user-defined data tables
  • Exporting customized charts and graphs for marketing purposes


Case Study: Loan-Level Capital Reporting Environment​

The Client

Government Sponsored Enterprise (GSE)

The Problem

A GSE and large mortgage securitizer maintained data from multiple work streams in several disparate systems, provided at different frequencies. Quarterly and ad-hoc data aggregation, consolidation, reporting and analytics required a significant amount of time and personnel hours. ​

The client desired configurable integration with source systems, automated acquisition of over 375 million records and performance improvements in report development.

 

The Solution

The client engaged RiskSpan Consulting Services to develop a reporting environment backed by an ETL Engine to automate data acquisition from multiple sources. 

The Deliverables

  • Reviewed system architecture, security protocol, user requirements and data dictionaries to determine feasibility and approach.​
  • Developed a user-configurable ETL Engine, developed in Python, to load data from different sources into a PostgreSQL data repository hosted on Linux server. The engine provides real-time status updates and error tracking.​
  • Developed the reporting module of the ETL Engine in Python to automatically generate client-defined Excel reports, reducing report development time from days to minutes​
  • Made raw and aggregated data available for internal users to connect virtually any reporting tool, including Python, R, Tableau and Excel​
  • Developed a user interface, leveraging the API exposed by the ETL Engine, allowing users to create and schedule jobs as well as stand up user-controlled reporting environments​


Case Study: RS Edge – Analytics and Risk

The Client

Large Life Insurance Company – Investment Group

 

The Problem

The Client was shopping around for an analytics and risk platform to be used by both the trading desk and risk managers.

RiskSpan Edge Platform enabled highly scalable analytics and risk modeling providing visibility and control to address investment analysis, risk surveillance, stress testing and compliance requirements.

The Solution

Initially, the solution was intended for both the trading desk (as pre-trade analysis) as well as risk management (running scenarios on the existing portfolio).  Ultimately, the system was used exclusively by risk management and used heavily by mid-level risk management. 

Cloud Native Risk Service

We have transformed portfolio risk analytics through distributed cloud computing. Our optimized infrastructure powers risk and scenario analytics at speed and cost never before possible in the industry.

Perform advanced portfolio analysis to achieve risk oversight and regulatory compliance with confidence. Access reliable results with cloud-native interactive dashboards that satisfy investors, regulators, and clients.

Two Flexible Options
Fund Subscriber Service + Managed Service

Each deployment option includes on-demand analytics, standard batch and over-night processing or a hybrid model to suit your specific business needs. Our team will work with customers to customize deployment and delivery formats, including investor-specific reporting requirements.

Easy Integration + Delivery
Access Your Risk

Accessing the results of your risk run is easy via several different supported delivery channels. We can accommodate your specific needs – whether you’re a new hedge fund, fund-of-funds, bank or other Enterprise-scale customer.

“We feel the integration of RiskSpan into our toolkit will enhance portfolio management’s trading capabilities as well as increase the efficiency and scalability of the downstream RMBS analysis processes.  We found RiskSpan’s offering to be user-friendly, providing a strong integration of market / vendor data backed by a knowledgeable and responsive support team.”

The Deliverables

  • Enabled running various HPI scenarios and tweaked the credit model knobs to change the default curve, running a portfolio of a couple hundred non-agency RMBS
  • Scaling the processing power up/down via the cloud, and they would iterate through runs, changing conditions until they got the risk numbers they needed
  • Simplified integration into their risk reporting system, external to RiskSpan


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