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While artificial intelligence has been selectively implemented to help banks fight financial crime, recent breakthroughs in AI technology promise new capabilities that are broadening its adoption. Improved abilities to identify hidden patterns, synthesize structured and unstructured data, and explain analytical findings are boosting investigator efficiency and detecting new types of money laundering. The increased adoption of machine learning, including generative AI, is breaking the tradeoffs historically required between increasing the effectiveness of and driving down the cost of financial crime compliance (FCC).

Written with David Choi, a Partner in Oliver Wyman's Global Anti-Financial Crime practice, this report looks at:

• AI Adoption Trends: The Risk and Compliance function is at the forefront of AI adoption in Financial Services. FCC executives are using Boardroom and C-Suite interest in AI to transform AML and fraud detection and investigation using the different strengths of machine learning, graph analytics, and GenAI.
• Use Cases for FCC Capabilities: How AI is being applied in transaction monitoring, KYC, sanctions, and investigations. Case studies from leading banks illustrate the tangible benefits of AI, such as reduced false positives, enhanced detection of complex money laundering schemes, and improved operational efficiency.
• Implementation Considerations: Principles for successful AI implementation that emphasize the importance of organizational buy-in, data quality, regulatory clarity, and AI governance.
• Future Outlook: Looking ahead to how AI will enable a more integrated approach to financial crime compliance.

The report includes survey data on the effectiveness of AI across the different FCC capabilities, GenAI adoption rates across different functions within banking , where GenAI is being applied within the Risk and Compliance function, and common pitfalls and key success factors encountered in implementing GenAI programs.