Marc Stein, who runs Underwrite.AI, writes algorithms capable of teaching themselves.
The program learns from each correlation it finds, whether it’s determining someone’s favorite books or if they are lying about their income on a loan application. And using that information, it can predict whether the applicant is a good risk.
Digital lenders are pulling in all kinds of data, including purchases, SAT scores and public records like fishing licenses.
If we looked at the delta between what people said they made and what we could verify, that was highly predictive,” Stein says.
As part of the loan application process, some lenders have prospective borrowers download an app that uploads an extraordinary amount of information like daily location patterns, the punctuation of text messages or how many of their contacts have last names
“FICO and income, which are sort of the sweet spot of what every consumer lender in the United States uses, actually themselves are quite biased against people,” says Dave Girouard, the CEO of Upstart, an online lender.
Government research has found that FICO scores hurt younger borrowers and those from foreign counties because people with low incomes are targeted for higher-interest loans. Girouard argues that new, smarter data can make lending more fair.