The Rise of Artificial Intelligence in Financial Crime Prevention

Introduction

The use of artificial intelligence (AI) and machine learning in financial crime prevention and detection is becoming increasingly prevalent. Banks are leveraging these technologies to drive efficiency and effectiveness in identifying and preventing financial crimes.

Key Strategies for AI Adoption

• Segmentation and Advanced Segmentation: AI and machine learning can be used to replace old screening rules that are increasingly wrong or irrelevant, making segmentation and advanced segmentation low-hanging fruits.
• Risk-Based Approach: A risk-based approach, as implemented by Citibank’s GIU team, detects anomalies in transactions by incorporating external data to identify financial crime risks.
• Partnerships for External Data: Banks seek partnerships with data consortiums and public-private partnerships to leverage external data and enhance their financial crime prevention capabilities.

The Importance of Effective Governance

Effective oversight and governance are crucial when adopting advanced technologies like machine learning to ensure that models work as intended and regulatory risks are minimized.

Conclusion

The article highlights the growing use of AI and machine learning in financial crime prevention and detection, with a focus on increasing efficiency and effectiveness while managing governance risks.