How Do New Federal Standards Measure DQ’s Impact on Risk Calculations?

By Richard Wilson, Vice President, Product Strategy, Harte-Hanks Trillium Software

The May 24th announcement by the Federal Housing Finance Agency (FHFA) of a common framework of standards for data collection and management is welcome news for a lot of reasons.  This framework will be used by all US banks, Fannie Mae and Freddie Mac, and will operate across the US Mortgage industry to facilitate information exchange, data collection, reporting, problem tracking, etc.

However, the following statement within the framework particularly stands out:

(The) common data standards framework will increase efficiency for lenders while enabling the Enterprises to manage risk more effectively.

In light of this, there is now recognition of the direct link connecting data quality and risk calculation in a specific line of business, and this is important for several reasons – most significant is that banks will soon have the tools to measure (and peer review) exactly how much poor data quality adds in costs to their mortgage risk operations. 

Everyone has seen examples and ‘use cases’ measured in billions of dollars, but the idiosyncrasies of each bank’s operations have masked the overall costs to the industry (and its customers)- which are staggering.

It’s tempting to go back and ‘game out’ the scenarios of what might have happened had Fannie Mae and Freddie Mac had ‘the right tools at the right time’ to detect the poor quality of the data that was being used in the essentially corrupt mortgage book they were being fed a couple of years ago.  One imagines that even the most rudimentary data quality metrics tracking the input from NINO (No Income No Asset) loans would have operated as ‘a canary in the mineshaft’ and provided early warning of the home loan market collapse.

Data is a shared resource of a bank’s operations and risk functions – but data coming from multiple ‘downstream’ systems needs common standards and metrics to be ‘fit for purpose’ in establishing accurate risk exposures. The metrics and governance processes s being put in place by the FHFA measure data are designed to lay bare the critical link between problems in data and erroneous risk calculations. 

This new ‘direct connect’ between data accuracy and risk management should function like the “Rosetta Stone” and enable shared understanding of critical data assets at certain key junctures – and establish a much closer alignment between the ‘facts on the ground’ an d the true nature of the bank’s risk exposures.