Building Better AI, Faster: Governing and Optimizing Models in Financial Crime
Overview
With 90% of banks now supporting the use of AI for anti-financial crime compliance, many teams are finding that building models is only the beginning. The real challenge lies in validating, operationalizing, governing, and optimizing them over time. Join ACAMS and leading AML and fraud practitioners for a practical discussion on how institutions are managing the full AI model lifecycle, from development and validation to ongoing monitoring, retraining, and documentation. Panelists will share real-world lessons on what works, what doesn’t, and how teams are navigating evolving governance expectations, tooling decisions, and operational complexity. You’ll leave with actionable guidance to improve efficiency, oversight, and confidence in the models you rely on every day.
Learning Objectives
Gain practical insight into how AML and fraud teams define, operationalize, and scale AI model governance across the full lifecycle, balancing control, compliance, and innovation without slowing delivery.
Learn how teams balance detection effectiveness, explainability, and operational efficiency, reducing manual effort while improving oversight, and which tooling and controls make this trade-off workable in real-world financial crime environments.
Explore how teams monitor models in production, detect performance degradation, and determine when and how to retrain or tune models to maintain accuracy, resilience, and regulatory confidence over time.
ING Wholesale Banking
Wintrust Financial Corporation
Topics
- Governance and Reporting
- Artificial Intelligence
- Cloud Services
- Financial Crime Controls
- Technology
Industries
- Banks
- Financial Services
Regions
- North America
- Europe and Central Asia
- Jurisdictions
Level
- Basic
- Intermediate
- Advanced