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Alternative data gets a thumbs up: Update on the CFPB’s first no-action letter

  • The CFPB published more information about the use of alt data in credit decisioning.
  • The results are positive for lender Upstart and for the industry in general.
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Alternative data gets a thumbs up: Update on the CFPB’s first no-action letter

Alternative data has the ability to increase access to credit and decrease its cost. But, given how early it is in the cycle and how important this is for lenders and borrowers alike, regulators have tread carefully and intentionally approaching the use of alternative data for credit decisions.

First, seek to learn: In 2017, the Consumer Financial Protection Board requested information from the industry to learn more about how lenders are using alternative data.

  • CFPB to get involved: This also signaled to the industry that the CFPB might consider future activity to encourage responsible use of alternative data and lower unnecessary barriers to its use.
  • Choosing to do nothing is doing something: Later in 2017 a no-action letter was issued to Upstart, a digital lender that uses alternative data and machine learning models to issue credit decisions.
  • The no-action signal: While the no-action letter didn’t endorse alternative data, ML, or Upstart specifically, it did communicate to the lending community that the CFPB sought to learn more about the positive aspects of alternative data. Upstart began sharing more and more information with the Bureau over the past 22 months.

After analyzing more of Upstart’s historical lending data, the CFPB has recently issued “An update on credit access and the Bureau’s first No-Action Letter.” As part of this letter, Upstart agreed to allow the Bureau to share some highlights of what it found.

  • More and cheaper credit: The results show that the tested model approves 27 percent more applicants than the traditional model, and yields 16 percent lower average APRs for approved loans.
  • Positive results with lower bias: This reported expansion of credit access occurs across all tested race, ethnicity, and sex segments resulting in the tested model increasing acceptance rates by 23 percent to 29 percent and decreasing average APRs by 15 percent to 17 percent.

Tearsheet sat with Upstart co-founder and CEO Dave Girouard to learn more about the results the CFPB shared and what it means for Upstart and the industry.

Did the increases in access to credit surprise you when you looked at the data?

Actually, it didn’t! Since the beginning of Upstart, we worked to expand access to credit by using alternative data, artificial intelligence, and machine learning algorithms to determine a borrower’s creditworthiness. Early on, we conducted a study that showed us that more than four in five Americans have never defaulted on a loan, yet less than half have access to prime credit. We knew there had to be a better way of determining a borrower’s creditworthiness.

For example, as the CFPB included in the blog post, Upstart’s model approved almost twice as many consumers with FICO scores from 620 to 660 compared to a traditional model. This is an important group of borrowers who are currently underserved by traditional credit underwriting models. 

What does this mean for future borrowers?

Upstart’s model provides higher approval rates and lower interest rates for every traditionally underserved demographic. Because of the increased accuracy of our model, we are able to pass the savings onto our borrowers in the form of lower interest rates. We constantly hear stories from our borrowers who have successfully paid off their credit card debt. Unsecured personal loans are the fastest growing category in consumer lending, and we anticipate that more people will rely on these loans rather than paying the high fees and high interest rates of credit cards.

What does this mean for Upstart moving ahead?

Instead of competing with banks, we prefer to focus our efforts on partnering with banks and other financial institutions so that more consumers can benefit from our AI lending model, which makes credit more affordable to the underserved.

We’ve recently announced that First Federal Bank of Kansas City and First National Bank of Omaha are using Powered by Upstart, our white-labeled lending application, to power their lending programs. In addition, we just closed a $358 million securitization which shows that capital markets continue to demonstrate their growing appetite for the superior underwriting delivered by AI-powered lending.

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