CFPB Reports Alternative Credit Model Could Increase Credit Access

Written By: Joel Palmer, Op-Ed Writer

The Consumer Financial Protection Bureau (CFPB) has found that an alternative access-to-credit model could approve 27 percent more applicants than the traditional model.

CFPB shared highlights from simulations and analyses conducted by Upstart Network Inc., a company that uses alternative data and machine learning in making credit underwriting and pricing decisions. 

Two years ago, CFPB issued a no-action letter to Upstart Network, which agreed to allow the bureau to share key highlights from simulations and analyses it conducted into whether alternative data, like education and employment history, would have an impact on credit decisions.

In addition to the increase in approved applicants, the analysis also showed that the alternative model resulted in:

•A 16 percent lower average APR for approved loans.

•“Near prime" consumers with FICO scores from 620 to 660 being approved approximately twice as frequently.

•Applicants under 25 years of age being 32 percent more likely to be approved.

•Consumers with incomes under $50,000 being 13 percent more likely to be approved.

“This 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%,” CFPB reported.

 “In many consumer segments, the results provided show that the tested model significantly expands access to credit compared to the traditional model.”

CFPB estimates that 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.  Without a sufficient credit history, consumers face barriers to accessing credit, or pay more for credit.

“The bureau 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.”

At the direction of the Federal Housing Finance Agency (FHFA), Fannie and Freddie has assessed, over the past two years, the potential impact of updating credit score requirements. Based on these assessments, FHFA concluded that any change in credit score requirements will have little impact on mortgage processors and underwriters, as well as mortgage borrowers.

At the same time, support has been widespread for using alternative credit scores. Several bills have been introduced in Congress over the last several years related to credit scores. The Consumer Finance Protection Bureau has pushed for alternative scores, as has The Urban Institute.

A year ago, FHFA tabled an initiative to change credit scoring models. The agency said it was “shifting its focus” to implementing Section 310 of the Economic Growth, Regulatory Relief, and Consumer Protection Act. Section 310, also known as The Credit Score Competition Act, directs FHFA to create a process to validate and approve new credit scoring models for use by Fannie and Freddie.

In December, FHFA released a proposed rule to validate and approve third-party credit score models used by the GSEs.

About the Author

As an NAMP® Opinion Editorial Contributor, Joel Palmer is a freelance writer who spent 10 years as a business and financial reporter and another 10 years in marketing for the insurance and financial services industries. He regularly writes about the mortgage industry, as well as residential and commercial real estate, investments, and retirement income planning. He has also ghostwritten books on starting a business, marketing, and retirement income planning.

Opinion-Editorial (Op-Ed) Disclaimer For NAMP® Library Articles: The views and opinions expressed in the NAMP® Library articles are those of the authors and do not necessarily reflect any official NAMP® policy or position. Examples of analysis performed within this article are only examples. They should not be utilized in real-world application as they are based only on very limited and dated open source information. Assumptions made within the analysis are not reflective of the position of NAMP®. Nothing contained in this article should be considered legal advice.