Artificial intelligence has the potential to make Customer Risk Assessment methodologies more dynamic, data-driven, and responsive to changes in customer behavior. However, the use of AI also introduces important challenges involving data quality, model governance, explainability, bias, validation, documentation, and regulatory defensibility. This webinar will provide attendees with a practical understanding of how AI can be incorporated into the Customer Risk Assessment process while maintaining the transparency, oversight, and controls necessary for an effective BSA/AML compliance program.

What We Will Cover:
• How AI and machine learning can supplement traditional Customer Risk Assessment methodologies
• The difference between static customer risk scoring and more dynamic AI-supported approaches
• How AI can use customer, product, service, geographic, transactional, and behavioral information to identify risk patterns and changes that traditional methodologies may overlook
• The risks associated with data quality, bias, inappropriate correlations, and overreliance on automated customer risk scoring
• Why explainability, transparency, human judgment, and appropriate overrides are essential to a defensible AI-enhanced methodology
• How validation, ongoing monitoring, governance, and third-party oversight should be incorporated into the use of AI in Customer Risk Assessment
• What documentation and controls institutions should maintain—and what auditors and examiners are likely to expect when evaluating an AI-supported Customer Risk Assessment methodology

Who Should Attend: BSA/AML Compliance Officers, BSA/AML Operational Officers, Other BSA/AML Personnel, Auditors, Risk Management

AirDate: October 15, 2026 @ 2pm EST