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.

The market context report finds that current market conditions may present a unique window to build on substantial interest among industry, advocates, and policymakers in using non-conventional data sources such as digital wallet information and supply chain records for credit scoring and underwriting.

FinRegLab Deputy Director Kelly Cochran joins RegFi cohosts Jerry Buckley and Caroline Stapleton for a conversation about how machine learning – including generative artificial intelligence – is used by consumer lenders and the evolving regulatory response. Kelly begins with a helpful distinction between the technologies commonly included under the broad “AI” moniker, noting that many of these algorithmic models are not new to credit underwriting. The discussion then pivots to the growing focus on generative AI and the need to balance its potential benefits with consumer protection, data privacy and explainability considerations. Kelly suggests that existing rules in the financial services regulatory framework can be adapted to address many of these issues and serve as a model for AI policymaking in other sectors.

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.

FinRegLab hosted a webinar with senior federal financial regulators in January 2024 to discuss the growing use of artificial intelligence and machine learning in financial services, including credit underwriting.

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.

This policy analysis explores the regulatory and public policy implications of the increasing use of machine learning models and explainability and fairness techniques for credit underwriting in depth, particularly for model risk management, consumer disclosures, and fair lending compliance.

This additional study of COVID-19 loan forbearances probes the benefits of combining short-term payment relief with longer term assistance plans for the most vulnerable consumers struggling with credit card debt.

CEO Melissa Koide served as a witness on Wednesday, September 20, 2023 before the United States Senate Committee on Banking, Housing, and Urban Affairs hearing on “Artificial Intelligence in Financial Services.” She shared learnings and insights from FinRegLab’s work on ML/AI, based on our research and policy and market dialogue regarding fairness, inclusion, and explainability in machine learning in credit underwriting.