LSU-Harvard Research Could Help Millions Gain Access to Credit
September 20, 2022
Increasing Fairness in Lending
Less than half of all Americans have access to prime credit because of their credit score. But new LSU and Harvard University research shows a lot more people could become eligible for loans at lower interest rates if lenders used artificial intelligence, or AI, and alternative data, such as education and employment history.
Smarter underwriting algorithms would especially benefit recent college graduates and young people with short credit histories, as well as people with low or no credit scores. The latter can be a particular challenge for residents in Louisiana, who on average have the second-lowest credit scores in the nation.
“There are systemic issues in our credit system,” said Dimuthu Ratnadiwakara, assistant professor of finance at LSU. “Even though the vast majority of the U.S. population—80 percent—never defaulted on a loan, less than half have access to prime credit. With smarter credit models, lenders could approve almost twice as many borrowers, with fewer defaults.”
The researchers looked at outcomes for people who applied for more than three million personal loans between 2014 and 2021—some of whom were approved, some denied.
“Traditional models tend to lock anyone with a low credit score—including many young people, college-educated people, low-income people, Black and Hispanic people and anyone who lives in an area where there are more minorities, renters and foreign-born—out of the credit market,” Ratnadiwakara said. “What we found is that some of them are more creditworthy than their credit scores suggest.”
Since the primary way Americans build wealth is by owning a home, access to a mortgage can decide the financial security of individuals, families and generations.
“We’re proud to have bank partners originating loans in Louisiana and the LSU-Harvard study showed our AI lending platform helps banks and credit unions lend more inclusively to creditworthy borrowers. The benefits of AI and alternative data are larger where credit scores are relatively low [such as in Louisiana].”
Don Carmichael, manager of machine learning at Upstart, a financial technology company and online lending marketplace