This report examines broad implications of using AI in financial services. While recognizing the potentially significant benefits of AI for the financial system, the report argues that four types of challenges increase the importance of model transparency: data quality issues; model opacity; increased complexity in technology supply chains; and the scale of AI systems’ effects. The report suggests that model transparency has two distinct components: system transparency, where stakeholders have access to information about an AI system’s logic; and process transparency, where stakeholders have information about an AI system’s design, development, and deployment.