This document provides comprehensive guidance on leveraging AI in banking, addressing regulatory challenges, managing model risks and implementing data governance. It explores different organizational structures to enable AI, including centralized, decentralized and hub-and-spoke models. The paper also highlights the uses of AI in banking, particularly in financial crime and fraud detection, and emphasizes the importance of self-service analytics and open source technology.

What will you learn when you read this document?

• How can artificial intelligence be used effectively in the banking sector and what are the related legal challenges?
• What is Model Risk Management and why is it so important in the implementation of AI in banking?
• What role does data governance play in the adoption of AI in banking and what are the different organizational structures that can be used?
• What are some key use cases for AI in banking, especially in the detection of financial crime and fraud?
• Why is self-service analytics and open source technology important in the context of artificial intelligence in banking?