A new report by FinRegLab traces institutions’ growing use of new data sources, artificial intelligence, and other innovations in identity proofing and financial crimes monitoring, highlighting the potential benefits and risks to consumers particularly as the sector responds to escalating fraud and scams activity since the pandemic.

A new report by FinRegLab underscores the importance of continuing technology and data improvements to expand credit access among smaller, younger businesses. From Crisis to Opportunity: Financing for Underserved Small Businesses since COVID-19 analyzes recent lending trends, including initiatives focusing on underserved businesses, lender technology adoption, and the effect of high inflation and interest rates on both loan demand and supply. 

FinRegLab announced that it is conducting new empirical research to evaluate the inclusion impact of machine learning credit models, including those built with bank account data as well as traditional credit report information. The organization has also been invited by the Office of the Comptroller of the Currency (OCC) to co-chair a new Technology Working Group within the OCC’s Project REACh initiative.

A new report by FinRegLab details strategies for improving data sources for credit scoring and underwriting of Kenyan micro and small enterprises (MSEs), finding that tapping multiple non-conventional (or alternative) data sources is particularly important to expanding credit access and growth among women-owned MSEs.

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.