Can machines be more fair than humans at determining credit risk?

Credit ratings have long been the key measure of how likely a U.S. consumer is to repay any loan, from mortgages to credit cards. But the factors that FICO and other companies that create credit scores rely on--things like credit history and credit card balances--often depend on having credit already. In recent years, a crop of startup companies have launched on the premise that borrowers without such histories might still be quite likely to repay, and that their likelihood of doing so could be determined by analyzing large amounts of data, especially data that has traditionally not been part of the credit evaluation. These companies use algorithms and machine learning to find meaningful patterns in the data, alternative signs that a borrower is a good or bad credit risk. These companies are still young, but to date, there isn't clear evidence that these approaches have greatly expanded the credit available, and lenders using them often charge high interest rates, according to a report by the National Consumer Law Center, a consumer advocacy group.