Data Analytics Can Help Reduce Racial Bias in Lending, Policymakers Argue
The shift to a data-driven world has sparked concerns about bias and discrimination, with critics like the Yahoo Finance and the Federal Trade Commission voicing fears. However, policymakers argue that data analytics can also help expose and reduce human bias in areas such as employment and transportation.
In the realm of consumer finance, the Consumer Financial Protection Bureau (CFPB) has employed algorithms to infer a borrower's race based on other loan application data. This is because creditors are prohibited from directly collecting data on an applicant's race. The CFPB's use of data analytics is a step towards reducing racial bias in lending.
In 2013, the CFPB sued Ally Financial for racial bias in auto loan interest rates, marking the largest such suit in history. The lawsuit revealed that minority borrowers often paid $200 to $300 more than similarly situated white borrowers due to dealership discretion in pricing. The US Department of Justice (DOJ) filed this lawsuit, representing the largest legal case of its kind. Ally Financial, which buys retail installment contracts from over 12,000 automobile dealers in the U.S., found that dealerships could increase interest rates beyond Ally's calculated 'buy rate' for profit, disproportionately affecting minority borrowers.
Despite fears of computerized decision-making, the wider deployment of fair algorithms can help reduce unfair consumer discrimination. By automating dealership decision-making, racial bias in interest rate pricing could potentially be prevented. Policymakers should encourage the use of data analytics to promote fairness and equality in our increasingly data-driven world.
