7 Kenneth P. Brevoort, Philipp Grimm, and Michelle Kambara, Data Point: Credit Invisibles (Washington: Consumer Financial Protection Bureau, May 2015), https://files.consumerfinance.gov/f/201505_cfpb_data-pointcredit-invisibles.pdf.
5  3  Federal Advisory Council, which includes a range of banking institutions from across the country, recently noted that nontraditional data and the application of AI have the potential “to improve the accuracy and fairness of credit decisions while also improving overall credit availability.” 8 To harness the promise of machine learning to expand access to credit, especially to underserved consumers and businesses that may lack traditional credit histories, it is important to be keenly alert to potential risks around bias and inequitable outcomes.