The use of AI in credit underwriting could be an important step forward in expanding the availability and reducing the cost of consumer financial services. Conventional underwriting systems were built prior to important changes in the availability of data, consumer demographics, and advanced analytics. AI can capture and process broader and deeper data sets, and use both more sophisticated analytical tools and powerful computing capabilities to generate more accurate credit underwritten.
AI systems also analyze alternative data to identify new patterns and correlations across data sets that are not captured by conventional models. Alternative data draws from sources such as bill payments for mobile phones and rent, and electronic transactions such as deposits, withdrawals or transfers. This information could show a track record of meeting obligations that may not turn up in a credit history. As a result, some who now cannot get reasonably priced credit may see more access or lower borrowing costs.
AI credit underwriting systems can better predict consumer performance. They use more diverse data sets and credit standards compared to conventional credit scores. AI can expand access to credit for millions of Americans who simply have no credit score at all. The relatively slow adoption of these practices by traditional banks reflects not a lack of interest or aptitude, but a regulatory, examination, and enforcement regime that overly discourages risk-taking and thwarts innovation.