“Use of AI in government is still a burgeoning field, with promising potential in detecting fraud, increasing efficiency and responsiveness, and generally improving service to citizens. To reach these goals and avoid missteps, governments have a series of lessons to learn: they must adopt policies that protect privacy, train public officials to understand the technologies being implemented, including the risks, and implement guardrails to prevent biases in the data from further disenfranchising disadvantaged groups.”

“Today, roughly 50% of U.S. consumers are estimated to have signed up for financial apps or other products that frequently rely on data aggregators to collect information via customer-authorized transfers, with substantial growth in the first months of the COVID-19 pandemic.”

FinRegLab Deputy Director Kelly Thompson Cochran testified in the Task Force’s hearing on “Preserving the Right of Consumers to Access Personal Financial Data.”

Machine learning models are being used to evaluate the creditworthiness of tens of thousands of consumers and small business owners each week in the U.S., increasing the urgency of answering key questions about their performance, governance, and regulation.

The Board of Governors of the Federal Reserve System, the Federal Deposit Insurance Corporation, and the Office of the Comptroller of the Currency released a guide for community banks on conducting diligence of financial technology companies. Drawing on existing guidance that articulates risk management expectations for third-party relationships, the guide highlights areas where required diligence processes can be adapted to reflect constraints related to doing business with early or expansion stage companies.

As recent developments have renewed interest in harnessing new data and analytical techniques for credit underwriting, stakeholders are asking questions about potential benefits and risks particularly for addressing racial equity issues. FinRegLab Deputy Director Kelly Thompson Cochran wrote an article summarizing recent initiatives and issues for an edition of the Community Development Innovation Review titled “Fintech, Racial Equity, and an Inclusive Financial System” that has been published by the Federal Reserve Bank of San Francisco and the Aspen Institute.

“‘[T]he predictiveness of the cash flow scores and attributes was generally at least as strong as the traditional credit scores and credit bureau attributes,’ suggesting it’s a reliable complement to or replacement for traditional scoring.”

“Policymakers are actively building their understanding of the implications of AI/ML on model governance, fairness, explainability, and financial inclusion”

FinRegLab’s forthcoming research will help to inform the extent to which current laws and regulations are able to be satisfied in light of the emergence of more complex underwriting models, how well tools to develop and monitor those models perform in identifying effective ways to pursue greater inclusion and fairness, and considerations for policy and market developments that can support the safe, inclusive, and nondiscriminatory adoption of machine learning.

“AI adoption and data maturity within enterprises have seen significant growth in the past decade. With each passing day, new enterprise AI use cases come to life in more organizations and more industries.”