Collecting comprehensive and accurate consumer loan data can help your organization assess and mitigate potential fair lending risk.
With mortgage-related loans, Home Mortgage Disclosure Act (HMDA) data collection and reporting has been in place since 1975. While analyzing mortgage-related lending data certainly still presents a challenge for many financial services organizations, analyzing and monitoring consumer loans for fair lending risk has proven to be even more difficult for a variety of reasons.
The most obvious challenge is the prohibition from gathering information on an applicant’s race, gender, and ethnicity, thereby requiring a proxy methodology. Add in fintech partnerships and other third-party channels, pricing, and underwriting models, and the risk and effort significantly increase.
While HMDA-reportable loans require lenders to provide details related to race, ethnicity, and gender, consumer loans don’t require that same level of information, which can make monitoring for fair lending risk more complex and a heavier lift. In fact, many organizations haven’t yet assessed their credit card, indirect, or fintech portfolios because of these complexities. Even more, the complexities of artificial intelligence and machine learning within pricing and underwriting models can increase risk exponentially.
However, doing nothing with consumer loan data is not an option for financial services organizations. Regulators continually scrutinize organizations for noncompliance, and the last 12 months have brought many fair lending-related enforcement actions.
Looking at the bigger picture, a single fair lending violation can tarnish an organization’s reputation, result in civil money penalties or orders of consumer redress, and potentially derail strategies for growth and expansion. And beyond the regulatory aspects, isn’t it good for organizations to know and understand who is and is not receiving products and where those products are concentrated?