FTC Warning
A 2016 study by credit reporting agency Experian found that women had higher credit scores, less debt, and a lower rate of late mortgage payments than men. Still, the Federal Trade Commission has warned that women may continue to face difficulties in getting credit.
Freddy Kelly, chief executive officer of Credit Kudos, a London-based credit scoring startup, pointed to the gender pay gap, where women are typically paid less than men for performing the same job, as one reason lenders may be stingy with how much they let women borrow.
Using complex algorithms that take into account hundreds of variables should lead to more just outcomes than relying on error-prone loan officers who may harbor biases against certain groups, proponents say.
“It’s hard for humans to manually identify these characteristics that would make someone more creditworthy,” said Paul Gu, co-founder of Upstart Network Inc., a tech firm that uses artificial intelligence to help banks make loans.
Upstart uses borrowers’ educational backgrounds to make lending decisions, which could run afoul of federal law. In 2017, the Consumer Financial Protection Bureau told the company it wouldn’t be penalized as part of an ongoing push to understand how lenders use non-traditional data for credit decisions.
AI Push
Consumer advocates reckon that outsourcing decision-making to computers could ultimately result in unfair lending practices, according to a June memorandum prepared by Democratic congressional aides working for the House Financial Services Committee. The memo cited studies that suggest algorithmic underwriting can result in discrimination, such as one that found black and Latino borrowers were charged more for home mortgages.
Linda Lacewell, the superintendent of the New York Department of Financial Services, which launched an investigation into Goldman’s credit card practices, described algorithms in a Bloomberg Television interview as a “black box.” Wozniak and Hansson said they struggled to get someone on the phone to explain the decision.
“Algorithms are not only nonpublic, they are actually treated as proprietary trade secrets by many companies,” Rohit Chopra, an FTC commissioner, said last month. “To make matters worse, machine learning means that algorithms can evolve in real time with no paper trail on the data, inputs, or equations used to develop a prediction.
“Victims of discriminatory algorithms seldom if ever know they have been victimized,” Chopra said.
This article was provided by Bloomberg News.