In this episode of the Consumer Finance Podcast, Chris Willis, co-leader of Troutman Pepper Locke’s Consumer Financial Services Regulatory practice, delves into the current state of machine learning and artificial intelligence (AI) models in underwriting and fraud detection. Chris provides an overview of the regulatory expectations set by the Consumer Financial Protection Bureau, including the historical context and recent developments. He discusses the importance of fair lending considerations, the use of less discriminatory alternative analysis, and the skepticism around certain types of alternative data. Chris also explores the potential impact of state regulations and the need for a long-term approach to fair lending risk. Tune in to stay informed about the evolving landscape of AI and machine learning in consumer finance.

Transcript: The FinReg Frontier: AI and Machine Learning in Consumer Finance (PDF)

Photo of Chris Willis Chris Willis

Chris is the co-leader of the Consumer Financial Services Regulatory practice at the firm. He advises financial services institutions facing state and federal government investigations and examinations, counseling them on compliance issues including UDAP/UDAAP, credit reporting, debt collection, and fair lending, and defending…

Chris is the co-leader of the Consumer Financial Services Regulatory practice at the firm. He advises financial services institutions facing state and federal government investigations and examinations, counseling them on compliance issues including UDAP/UDAAP, credit reporting, debt collection, and fair lending, and defending them in individual and class action lawsuits brought by consumers and enforcement actions brought by government agencies.