CFPB Blogs on Alternative Creditworthiness Data
A recent CFPB blog post by the Director of the CFPB Office of Innovation and the Assistant Director for Fair Lending provides an update on what the CFPB has learned from having issued a No Action Letter to a company that uses alternative data and machine learning in making credit underwriting and pricing decisions.
In 2017, the CFPB had issued a Request for Information regarding the use of alternative data and modeling techniques in the credit process, which WBK covered here. Later in 2017, the CFPB announced a No-Action Letter for a company that uses alternative data and machine learning in making credit underwriting and pricing decisions. As part of that No Action Letter, the recipient company agreed to allow the CFPB to share key highlights from simulations and analyses that it conducted pursuant to its model risk management and compliance plan.
As the CFPB now reports, the results from the access-to-credit comparisons show, among other things, that the tested model significantly expands access to credit in some consumer segments compared to the compared traditional model because it approves more applicants and yields lower average APRs for approved loans. In particular, under the tested model, the results provided reflect that: (1) “near prime” consumers with FICO scores from 620 to 660 are approved approximately twice as frequently; (2) applicants under 25 years of age are 32 percent more likely to be approved; and (3) consumers with incomes under $50,000 are 13 percent more likely to be approved. Additionally, with regard to fair lending testing, the approval rate and APR analysis results provided for minority, female, and 62 and older applicants show no disparities that would have required further fair lending analysis under the compliance plan.
The blog post finishes by stating that the CFPB encourages lenders to develop innovative means of increasing fair, equitable, and nondiscriminatory access to credit, particularly for credit invisibles and those whose credit history or lack thereof limits their credit access or increases their cost of credit, while maintaining a compliance management program that appropriately identifies and addresses risks of legal violations.
