FinRegLab today released new empirical research demonstrating that adopting machine learning techniques and incorporating cash flow data into credit underwriting can significantly increase predictiveness and expand credit access for consumers—without increasing lenders’ default risk.

A new empirical study from FinRegLab finds that cash-flow data can help lenders underwrite small businesses more accurately, particularly when evaluating early-stage companies and financially constrained entrepreneurs that often struggle to access credit because lenders consider them to be higher risk.

As federal policymakers struggle to address the nation’s growing shortage of affordable housing, a new report by FinRegLab analyzes the potential benefits of automating and updating federal insurance programs for manufactured home loans.

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.

Mission-based lenders are increasingly using electronic bank account data and technology platforms to expand credit access to underserved entrepreneurs, according to a new FinRegLab working paper that details both current practices and future challenges as lenders work to scale their lending programs. Transforming Small Business Credit: Technology and Data Adoption in Mission-Based Lending focuses on […]

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.

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.