The Consumer Financial Protection Board (CFPB) is now urging lenders to develop innovative means of increasing fair access to credit through the use of alternative data and machine learning to potentially discover “relationships not otherwise discoverable through methods that have been traditionally used in credit scoring.”

Here is an excerpt from a story published in the National Law Review:

In the blog post, 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.”

The blog post concludes with the CFPB’s statement that it is “currently reviewing comments to its proposed No-Action Letter, Trial Disclosure, and Product Sandbox policies.” In September 2018, the Bureau proposed significant revisions to its “Policy to Encourage Trial Disclosure Programs” which sets forth the Bureau’s standards and procedures for exempting individual companies, on a case-by-case basis, from applicable federal disclosure requirements to allow those companies to test trial disclosures. (Upstart’s no-action letter was issued under these procedures.) In December 2018, the CFPB issued proposed revisions to its 2016 final policy on issuing no-action letters, together with a proposal to create a new “product sandbox.”

The CFPB, in its July 2019 fair lending report, discussed supervisory reviews of alternative credit scoring models. It stated that in 2018, the Office of Fair Lending recommended supervisory reviews of third-party scoring models that would “focus on obtaining information about the models and compliance systems of third-party scoring companies for the purpose of assessing fair lending risks to consumers and whether the models are likely to increase access to credit. Observations from these reviews are expected to further the Bureau’s interest in identifying potential benefits and risks associated with the use of alternative data and modeling techniques.” The Bureau commented that while a significant focus of its interest is on how alternative data and modeling can expand credit access for credit invisibles, it is also interested in other potential direct or indirect benefits to consumers, “including enhanced creditworthiness predictions, more timely information about a consumer, lower costs, and operational improvements.”