Reed Smith In-depth

Financial institutions and financial technology companies are increasingly using artificial intelligence (“AI”), including machine learning, to offer and deliver products and services to consumers. The U.S. Consumer Financial Protection Bureau (the “CFPB”) continues to issue legal interpretations and policy guidance on AI and machine learning innovations that portend increasing supervisory scrutiny and enforcement actions based on the AI and machine learning algorithms used by companies that are subject to the supervision of the CFPB.

Authors: Will Atherton

The CFPB reoriented its approach to innovation in May 2022 by creating a new Office of Competition and Innovation to replace the Office of Innovation and Operation Catalyst and by eliminating its No-Action Letter and Compliance Assistance Sandbox programs. The CFPB terminated a no-action letter at the request of the recipient, in part, ostensibly to facilitate time-sensitive AI innovation, and issued guidance clarifying how the CFPB views the interplay between AI and the Equal Credit Opportunity Act (“ECOA”) and its implementing regulation.

This Reed Smith commentary describes the major elements and context for the CFPB’s most recent AI actions and concludes with suggested steps companies that are subject to the jurisdiction of the CFPB or the federal banking and credit union regulators (the “federal financial institution regulators” or the “regulators") should consider taking in order to diminish or avoid adverse actions related to their use of AI, including machine learning algorithms.

I. The CFPB and Federal Regulators Focus on Impacts of AI

Financial institutions and financial technology companies (“financial entities”) often employ some form of AI, machine learning or data analytics in connection with credit underwriting, risk management, cybersecurity, customer experience and service, fraud detection, reporting of suspicious transactions, and more. The COVID-19 pandemic accelerated adoption and use of digital and AI-based applications and catapulted AI into the center of business operations. AI has emerged as an important part of the core business strategies of financial entities. For example, financial entities’ are increasingly using AI in their credit underwriting processes, and the size and complexity of AI data sets and models that employ machine learning to help refine underwriting decisions have grown alongside the growth of AI in underwriting.