On February 16, 2017 the Consumer Financial Protection Bureau (CFPB) issued a request for information (RFI) on the potential use of alternative data and modeling techniques in the credit process. The goal of the CFPB’s inquiry is to explore options that will expand access to credit for consumers who are credit invisible or who lack enough credit history to obtain a credit score.
In May 2015 the CFPB released “Data Point: Credit Invisibles” which was a study into the number of Americans with limited credit histories and the correlation with certain income levels and race. The CFPB’s RFI cites information from the “Data Point: Credit Invisibles” study including 26 million Americans are credit invisible, meaning they have no file with the major credit bureaus and 19 million are unscorable because their credit file is either too sparse or stale to generate a score. The RFI also notes that most of those who are credit invisible are disproportionately Black and Hispanic, low-income, and young adults. The lack of credit information or a credit score means credit invisible consumers have difficulty accessing mainstream credit products.
The CFPB is seeking information on alternative data and modeling techniques that can be used in place of traditional credit file or score information. Examples of alternative data could include: 1) Loan repayment data; 2) Monthly phone, rent, insurance or utility payment history; 3) Checking account transaction and cash flow; 4) Frequency in change of residence, employment, phone number and email address; 5) Education and occupation; and 6) Behavioral data about shopping, browsing, and use of devices and social media.
Alternative data and modeling techniques could increase credit accessibility to the credit invisible; however, there are also potential risks. The RFI includes both potential benefits and risks. Benefits include access to credit, enhanced creditworthiness predictions, more timely information, lower costs, and better service and convenience. Risks include privacy concerns, data quality issues, lack of transparency and ability to control information, difficulty to change credit standing through behavior, harder to educate and explain, unintended negative effects on certain groups, discrimination, and potential violation of laws (e.g. ECOA, FCRA, UDAAP).
The RFI notes “[t]he CFPB seeks not only to understand the benefits and risks stemming from use of alternative data and modeling techniques but also to begin to consider future activity to encourage their responsible use and lower unnecessary barriers, including any unnecessary regulatory burden or uncertainty that impedes such use.” Areas the CFPB plans to explore include:
• Access to credit
• Complexity of the process
• Impact on costs and service
• Implications for privacy and security
• Impact on specific groups
The CFPB’s request for information is available here.
