In addition to credit underwriting, FinRegLab’s comment letter highlights three AI use cases with particularly important financial inclusion implications: (1) delivery of financial advice and coaching; (2) identity verification, fraud detection, and anti-money laundering activities; and (3) back-office applications to increase the nimbleness of model and product development.

We therefore urge that a national inclusion strategy reflect the importance of data and technology in impacting financial inclusion, fairness, and equity in today’s rapidly changing environment.

Over the past three decades, customer-permissioned data flows have become critical to a growing range of consumer financial products and services as well as to public research focusing on household financial health, markets for consumer financial products and services, and the role of consumer financial activity in the nation’s economy.

FinRegLab’s testimony provides a general overview of the state of ML/AI adoption across various financial services use cases; potential benefits and risks to customers, providers, and the broader economy; and the ways that federal financial regulatory frameworks are shaping ML/AI adoption in this sector.

These data flows are critical to a growing range of consumer financial products and services. Modernizing the regulatory frameworks governing these flows is important both to mitigate current risks and frictions and to encourage future applications that produce greater inclusion, competition, and customer-friendly innovation, particularly for historically underserved consumers.

Coordinated action is critical between federal regulators to continue moving the growing ecosystem for customer-directed transfers toward adoption of safer technologies and practices without undermining consumers’ § 1033 rights or frustrating the law’s potential benefits for competition and innovation.

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

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.

FinRegLab CEO Melissa Koide testified in the Task Force’s hearing on “Equitable Algorithms: How Human-Centered AI Can Address Systemic Racism and Racial Justice in Housing and Financial Services.”

We recognize the breadth of urgent issues facing the Consumer Financial Protection Bureau and the nation at this time, but believe that resolving critical questions about access to financial data would substantially benefit consumers, small businesses, and financial services providers in helping to recover from the Covid-19 pandemic, address longstanding racial wealth gaps, and make U.S. financial systems more generally inclusive, competitive, and responsive to customer needs.