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Category: Case Study

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. 

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.  


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.

Top Hedge Fund Administrator: Risk Metrics & Performance Reports via Tableau and the Cloud​

A leading hedge fund administrator sought a better way to provide compliance reporting and overnight risk and portfolio reporting for its clients.

Reporting at this scale requires extraordinarily flexibility in computational bandwidth.

The Solution

RiskSpan delivered computation and distribution via the cloud of all required analytics and risk metrics to all relevant parties using the flexibility and attractive visualization of a seamless Tableau integration.

  • Ingestion, validation, and integration of disparate data sources (rates, implied volatility data and terms and conditions from six data vendor sources)
  • Reporting, distribution and publishing of the client’s full range of risk metrics, including VaR, custom aggregation, scenario analyses, interest rate shocks and other stress testing — all readily viewable to every client stakeholder via the cloud using Tableau.

The Edge We Provided

A fully hosted, outsourced solution. The administrator’s highly dynamic reports are delivered by way of a secure, hosted environment to a large number of diverse, institutional clients.

Institutionally Focused Broker-Dealer: Prepayment Analysis

An institutional-broker dealer needed a solution to analyze agency MBS prepayment data.

The Solution

The Edge Platform has been adopted and is actively used by the Agency trading desk to analyze Agency MBS prepayment data, to discover relationships between borrower characteristics and prepayment behavior.  

Commercial Bank: CECL Model Validation

A commercial bank required an independent validation of its CECL models. The models are embedded into three platforms (Trepp, Impairment Studio and Evolv) and included the following:

  • Trepp Default Model (Trepp DM) is used by the Bank to estimate the PD, LGD and EL of the CRE portfolio
  • Moody’s ImpairmentStudio – Lifetime Loss Rate (LLR) Model is used to calculate the Lifetime Loss Rate for the C&I portfolio
  • EVOLV – Lifetime Loss Rate (LLR) model is used to calculate the Lifetime Loss Rate for Capital Call and Venture Capital loans within the Commercial and Industrial (C&I) segment, Non-rated Commercial loans, Consumer as well as Municipal loans
  • EVOLV – Base Loss Rate (BLR) model is used to calculate quantitative allowance for 1-4 Family commercial loans and Personal loans for commercial use within the C&I segment Residential loans, HELOC and Indirect vehicle.

The Solution

Because the CECL models are embedded into three platforms, RiskSpan conducted an independent, comprehensive validation of all three platforms.

Our validation included components typical of a full-scope model validation, focusing on a conceptual soundness review, process verification and outcomes analysis.


RiskSpan was given access to the models’ platforms, and workpapers, along with the models’ development documentation, and weekly Q&A sessions with the model owners.

Our review evaluated:

i. the business requirements and purpose of the model, and the metrics that used by the developer to select the best model and evaluate its success in meeting these requirements will be judged.

ii. the identification and justification for

  (a) any theoretical basis for the model structure;

  (b) the use of specific developmental data;

  (c) the use of any statistical or econometric technique to estimate the model; and

  (d) the criteria used to identify and select the best model among alternatives.

iii. the reasonableness of model-development decisions, documented assumptions, data adjustments, and model-performance criteria as measured at the time of development.

iv. Process verification to determine the accuracy of data transcription, adjustment, transformation and model code.

RiskSpan produced a written validation report detailing its validation assessments, tests, and findings, and providing a summary assessment of the suitability of the models for their intended uses as an input to the bank’s CECL process, based upon the Conceptual Soundness Review and Process Verification.

Regional Bank: AML/BSA Model Validation

A large regional bank required a qualified, independent third party to perform risk-based procedures designed to provide reasonable assurance that its FCRM anti-money laundering system’s transaction monitoring, customer risk rating, and watch list filtering applications were functioning as designed and intended.

The Solution

RiskSpan reviewed existing materials, past audits and results, testing protocols and all documentation related to the bank’s model risk management standards, model setup and execution. We inventoried all model data sources, scoring processes and outputs related to the AML system.

The solution consisted of testing each of the five model segments: Design and Development; Input Processing; Implementation; Output and Use; and Performance.

The solution also quantified risk and exposure of identified gaps and limitations and presented sound industry practices and resolutions. 


  • A sustainable and robust transaction monitoring tuning methodology, which documented the bank’s approach, processes to be executed, frequency of execution, and the governance structure for executing tuning and optimization in the AML model. This included collecting and assessing previous regulatory feedback.
  • A framework that included a formal, documented, consistent process for sampling and analysis procedures to evaluate the ALM system’s scenarios and change control documentation.
  • A process for managing model risk consistent with the bank’s examiner expectations and business needs.

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


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 

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