White Paper on Data Wrangling for Banking Risk Models

Michael Watson Ph.D Partner
Read Time: 1 minute apprx.
banking & finance data science stress testing white papers

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Fallout from the 2008-2009 financial crisis included the emergence of a new regulatory landscape intended to safeguard the U.S. banking system from a systemic collapse. In 2012, the Federal Reserve Board of Governors (Fed), began to require the largest U.S. Bank Holding Companies (BHCs) to file a Comprehensive Capital Analysis and Review (CCAR), with stress tests intended to assess the capital adequacy of these BHCs in times of crisis. By 2015, CCAR and stress tests, now known as DFAST (after the Dodd-Frank Act Stress Tests) were expanded to include U.S. BHCs with between $10 and $50 billion in consolidated assets and foreign banks, whose exempt status expired.

For banks, CCAR reporting and DFAST stress testing are complex and data intensive endeavors with some of the following challenges:

  • DFAST requires credit modeling and risk assessment at a granular level over vast amounts of data
  • There is often a need for third-party data from sources such as Trepp to supplement internal data
  • Retrieving, maintaining, & standardizing both internal and external data is usually difficult and time-consuming
  • Subsets of data selected for reporting and testing must reflect the existing portfolio of loans at the bank

We’ve released a white paper that talks about how you can handle these issues.