WASHINGTON–The use of alternative credit data for underbanked consumers has led to nearly 30% more loans being approved, according to a CFPB program that has been testing digital platforms.
The agency has released results from the Upstart Network, which has been using the alternative data since 2017. Upstart is a consumer lender that uses both traditional and alternative data as part of its underwriting processes, and it has been working with the CFPB following a Request for Information Regarding Use of Alternative Data and Modeling Techniques in the Credit Process.
Among the findings in the test:
- 27% more applicants were approved than under the traditional model
- The approved loans charged 16% lower average APRs
- “Near prime” consumers with FICO scores from 620 to 660 were approved approximately twice as frequently
- Applicants under 25 years of age were 32% more likely to be approved
- Consumers with incomes under $50,000 were 13% more likely to be approved
Increased Credit Access
“Alternative data includes information not typically found in core credit files of nationwide consumer reporting agencies and may indicate a likelihood of meeting obligations on time that a traditional credit history may not reflect,” the CFPB said. “In addition to the use of alternative data, increased computing power and the expanded use of machine learning can potentially identify relationships not otherwise discoverable through methods that have been traditionally used in credit scoring. As a result of these innovations, some consumers who now cannot obtain favorably priced credit may see increased credit access or lower borrowing costs.”
As a condition for receiving a No-Action Letter from the CFPB, Upstart Network agreed to a model risk management and compliance plan that required it to analyze and appropriately address risks to consumers, as well as assess the real-world impact of alternative data and machine learning. In turn, Upstart Network agreed to provide the Bureau with information comparing outcomes from its underwriting and pricing model (tested model) against outcomes from a hypothetical model that uses traditional application and credit file variables and does not employ machine learning (traditional model).
“The reported expansion of credit access reflected in the results provided occurs across all tested race, ethnicity, and sex segments resulting in the tested model increasing acceptance rates by 23-29% and decreasing average APRs by 15-17%,” the CFPB said.
Much Work to Be Done
The Bureau said there is much more work to be done, and that it estimates 26 million Americans are credit invisible, meaning they have no credit history with a nationwide consumer reporting agency.
“Another estimated 19 million consumers have a credit history that has gone stale, or is insufficient to produce a credit score under most scoring models,” the CFPB said. “Without a sufficient credit history, consumers face barriers to accessing credit, or pay more for credit.”