The company is also something of a regulatory guinea pig: Upstart was the first business to receive a no-action letter from the Consumer Financial Protection Bureau. The letter essentially said the bureau had no plans to take any regulatory action against the company in return for detailed information about its loans and operations.
Though the bureau didn’t recreate Upstart’s results on its own, it said the company had approved 27 percent more applicants than the traditional model, while the average interest rates they paid were 16 percent lower. For example, “near prime” customers with FICO scores from 620 to 660 were approved about twice as frequently, according to company data. Younger and lower-income applicants also fared better.
Upstart, which also agreed to be monitored by two advocacy groups and an independent auditor, takes into account more than 1,000 data points inside and outside a consumer’s credit report. It has tweaked its modeling at times — it no longer uses the average incoming SAT and ACT scores of a borrower’s college — but includes the person’s college, area of study and employment history. (Nurses rank well, for example, because they’re rarely unemployed, Mr. Girouard said.) The amount that borrowers are asking for may also be a factor: If they are seeking more than Upstart’s algorithms believe is appropriate, that may work against them.
Other companies work in a similar way, although the methods and data they use vary.
TomoCredit, for example, will issue a Mastercard credit card to applicants — even those with no credit score — after receiving permission to peer at their financial accounts; it analyzes more than 50,000 data points, such as monthly income and spending patterns, savings accounts and stock portfolios. Within two minutes, consumers are approved for anywhere from $100 to $10,000 in credit, to be paid off weekly. On-time payments help build users’ traditional credit files and scores.
Zest AI, a Los Angeles company that already works with banks, auto lenders and credit unions, is also working with Freddie Mac, which recently began using the company’s tools to evaluate people who may not fit squarely inside traditional scoring models.
Jay Budzik, Zest AI’s chief technology officer, said the company went deep into applicants’ credit reports, and might incorporate information from a loan application, such as the mileage or potential resale value of a used car. It can also look at consumers’ checking accounts.
“How frequently are they getting close to zero?” Mr. Budzik said. “Those things are helpful in creating an additional data point on a consumer that is not in the credit report.”
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