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 is extending its investigation of the adoption of artificial intelligence in financial services through a policy analysis focused on the growing use of machine learning models for underwriting credit and a January 17 webinar with senior federal financial regulators to discuss generative AI and other recent developments.

“Melissa discusses her organization’s work in evaluating the explainability of complex machine learning algorithms in credit underwriting, model governance and adverse action notices. The conversation covers the CFPB’s recent guidance on credit denial by lenders using artificial intelligence as well as the role explainability plays in promoting fairness and inclusion in lending, methods and tools emerging for lenders to explain credit-related decisions based on AI models, and the ongoing work required to adapt to the digital economy and evolving regulatory landscape.”

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.

FinRegLab is launching an initiative with support from the U.S. Department of Commerce Minority Business Development Agency (MBDA) to improve credit access for minority-owned companies and other underserved small businesses through the use of non-traditional data sources and mission-based lenders.

A new research paper underscores the importance of evaluating whether the most vulnerable and distressed borrowers need longer term repayment plans soon after they first enroll in natural disaster or other emergency relief programs with credit card lenders.

FinRegLab has issued two papers that examine lenders’ ability to build, understand, and manage machine learning models to ensure that they can be trusted to underwrite applications for credit by millions of consumers and small businesses.

FinRegLab today issued a research brief entitled “The Countdown Clock for Student Loan Forbearances” that highlights the need for quick action by consumers, federal agencies, servicers, and counselors to prepare for the August 2023 end of pandemic-era forbearances on federal student loans.

Consumer Reports released a video series exploring biases in machine learning algorithms and data sets and the resulting unfair practices faced by communities of color. The series is designed to educate consumers on the risks hidden in seemingly “neutral” technologies. FinRegLab CEO Melissa Koide is featured in the Mortgage Lending episode of the series.