When banks and fintechs offer mortgages online using automated underwriting, they charge creditworthy minorities more than white applicants — the same way human loan officers do.

That's the conclusion of a study conducted by professors at the University of California, Berkeley.

As more online lenders and banks automate their lending — using mathematical models to make loan decisions rather than officers — the question of whether those algorithms can be unbiased or potentially introduce new and unintended forms of discrimination is a critical one.

This Berkeley study offers a new answer: So far, lending algorithms in digital mortgages are biased in exactly the same way humans are, possibly because developers build the logic human lenders use into their software.

The report, Consumer-Lending Discrimination in the Era of FinTech, found that lending officers and software-based underwriting engines both charge Latino and African-American loan applicants interest rates that are six to nine basis points higher than white applicants who have the same FICO score and loan-to-value ratio. The higher interest rate was the same, whether it was a loan officer, a bank’s online lending arm or a fintech mortgage lender like Quicken or SoFi.

Read more in American Banker.