Alternative Data Can Be Good for the Credit Business, Regulators Say
Consumers could obtain "more favorable terms based on enhanced assessments of repayment capacity," according to a joint statement
Friday, December 13, 2019
By Ted Knutson
U.S. regulatory agencies have issued a joint statement recognizing that alternative data can be beneficial in credit underwriting.
Alternative data - defined as information other than that provided in consumer credit files or on loan applications - has been touted as a tool for making credit more accessible, particularly to under-served populations.
“The agencies recognize alternative data's potential to expand access to credit and produce benefits for consumers. To the extent firms are using or contemplating using alternative data, the agencies encourage responsible use of such data,” said the December 3 statement, a three-page document from the Federal Reserve Board, Consumer Financial Protection Bureau (CFPB), Federal Deposit Insurance Corp., National Credit Union Administration and Office of the Comptroller of the Currency.
At the same time, citing “prior developments in the evolution of credit underwriting” including credit scoring methods, the agencies said that “the use of alternative data and analytical methods also raises questions regarding how to effectively leverage new technological developments that are consistent with applicable consumer protection laws” such as fair lending statutes and the Fair Credit Reporting Act.
The statement came exactly a year after the same group of regulators - excluding the CFPB, and joined by the Treasury Department's Financial Crimes Enforcement Network (FinCEN) - similarly supported experimentation and development of emerging technologies in Bank Secrecy Act/Anti-Money Laundering compliance. Artificial intelligence and digital identity innovations “can strengthen BSA/AML compliance approaches, as well as enhance transaction monitoring systems,” said the December 2018 pronouncement.
Technology and Market Reality
Chi Chi Wu, staff attorney of the National Consumer Law Center (NCLC), a public interest group, deemed the statement on alternative data to be a “measured approach.” She pointed to social media and big data as ways that consumer protection risks could arise.
Alison Melick, an expert on credit and enterprise risk management with regulatory advisory firm Promontory, an IBM company, said the regulators are acknowledging how the economy is changing and how technology can enhance analyses of borrowers' willingness and ability to repay loans.
“The agencies recognize that use of alternative data may improve the speed and accuracy of credit decisions and may help firms evaluate the creditworthiness of consumers who currently may not obtain credit in the mainstream credit system,” the regulators said. “Using alternative data may enable consumers to obtain additional products and/or more favorable pricing/terms based on enhanced assessments of repayment capacity.
“These innovations reflect the continuing evolution of automated underwriting and credit score modeling, offering the potential to lower the cost of credit and increase access to credit.”
In August, in an update on credit access, the CFPB recounted its request for information on alternative data and modeling techniques and a no-action letter to “a company that uses alternative data and machine learning in making credit underwriting and pricing decisions,” both in 2017. The lender, Upstart Network, “agreed to a model risk management and compliance plan that requires it to analyze and appropriately address risks to consumers, as well as assess the real-world impact of alternative data and machine learning.”
Among the reported statistics: “the tested model approves 27% more applicants than the traditional model and yields 16% lower average APRs [annual percentage rates] for approved loans.”
The bureau noted that an estimated 26 million Americans are “credit invisible, meaning they have no credit history with a nationwide consumer reporting agency,” while 19 million more “have a credit history that has gone stale, or is insufficient to produce a credit score under most scoring models. Without a sufficient credit history, consumers face barriers to accessing credit, or pay more for credit.”
CFPB went on to say it encouraged development of “innovative means of increasing fair, equitable, and nondiscriminatory access to credit, particularly for credit invisibles and those whose credit history or lack thereof limits their credit access or increases their cost of credit, while maintaining a compliance management program that appropriately identifies and addresses risks of legal violations.”
Cash Flow Data
The December joint statement brought attention to firms' automating the use of cash flow data in consumer credit evaluations.
“While this is a rapidly developing area of innovation, analysis of cash flow data generally focuses on assessing whether a borrower is able to meet new or existing recurring obligations by evaluating income and expense activity over time,” the statement said. “The evaluation of a borrower's income and expenses to help determine repayment capacity is a well-established part of the underwriting process. Improving the measurement of income and expenses through cash flow evaluation may be particularly beneficial for consumers who demonstrate reliable income patterns over time from a variety of sources rather than a single job . . .
“Many factors associated with the use of alternative data, including those discussed for cash flow data, may increase or decrease consumer protection risks.”
The agencies said that further information may be forthcoming as they gain “a deeper understanding of alternative data usages.” NCLC attorney Wu calls for more guidance on consumer control over alternative data, especially bank account transactions.
“The interagency statement notes that consumers can expressly permission access to their cash flow data,” Wu noted. She would like to see explicit assurance that such permission is not abused or used for a purpose not intended, such as targeted marketing or debt collection.