Case Study: Securitization Disclosure File Creation Process

The Client

Private Label Mortgage-Backed Security Issuer 

The Problem

The client issues private label MBS with sources from multiple origination channels. In accordance with industry requirements, the client needed to create and make available to securitization counterparties a loan-level data file (the “ASF File”) which has been defined and endorsed by the Structured Finance Industry Group. ​

The process of extraction and aggregation was inefficient and inconsistent with data from various originators, due diligence vendors and service providers.

RiskSpan consulting services streamlined extraction and aggregation, and reconciling the data used in this process.

The Solution

RiskSpan automated and improved the client’s processes to aggregate loan level data and perform data quality business rules. RiskSpan also designed, built, tested, and delivered an automated process to perform quality control business rules and produce the ASF File, while producing a reconciled file meeting ASF File standards and specifications.

Data Lineage

RiskSpan has experience working with various financial institutions on data lineage and its best practices. RiskSpan has also partnered with industry-leading data lineage solution providers to harness technical solutions for data lineage.

Master Data Management

RiskSpan has mastered the best practices around MDM model working with client’s disparate data sources. With ever increasing regulatory and compliance needs for financial institutions it’s important to have a single view of the client reference data.

Imputation and Analysis Guide
Data Quality

It’s increasingly important to reduce inefficiency in the data process and one of the key criteria to achieve the same is to ensure Data is of highest quality for downstream or any other analytical application usage. Riskspan experience in data quality stems from working with raw loan and transactional data from some of the world’s largest financial institutions.

The Deliverables

  • Created and documented data dictionary, data mapping, business procedures and business flows​
  • Gathered criteria and knowledge, from various client departments, to assess the reasonableness of data used in the securitization process ​
  • Documented client-specific business logic and business rules to reduce resource dependency and increase organizational transparency​
  • Enforced business rules through an automated mechanism, reducing manual effort and data scrub process time​
  • Delivered exception reporting which enabled the client to track, measure and report inaccuracies in data from due diligence firm​
  • Eliminated maintenance and dependency on ad hoc data sources and manual work-arounds​

Talk Scope

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