Executive Summary: 

Financial institutions are increasingly leveraging artificial intelligence (AI)/machine learning (ML) models to enhance decision-making, customer insights and operational efficiencies. This shift from traditional to AI-driven models introduces unique challenges that require significant updates to existing model risk management (MRM) frameworks. This paper highlights key AI/ML risks and risk cultures between Silicon Valley, the purveyors of the AI/ML technology, and the financial services industry, a regulated industry with a mature model risk practice. We will first explore the nature of AI/ML models and the nuances of model risk as applicable to financial services and follow this with a discussion about AI/ML risk across various stages of the model lifecycle. Our goal is to provide practical guidelines to integrate AI/ML capabilities into MRM frameworks, drawing on best practices from larger institutions while considering the resource constraints that are typical of smaller institutions.

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