Course Description:

This session will discuss the practical application of AI and machine learning in financial crime programs. We will explore model validation, compliance considerations, business use cases and share lessons from early adaptation.

Instructor: Stephanie Surratt

Course Description:

Identify key components of AI governance: model validation, explainability, audit trails, and regulatory compliance. This session will explore how institutions can embed compliance and governance into AI systems by focusing on model validation, explainability, auditability, and transparency.

Instructor: Stephanie Surratt

Course Description:

Implementing AI is not just a technology initiative, it’s an enterprise-wide transformation. This session explores how risk, compliance, and data science teams can work together to ensure responsible, scalable, and ethically sound AI adoption. We will discuss governance structures, team roles, and how to break down silos for better decision-making and risk alignment.

Instructor: Stephanie Surratt

Course Description:

This session will explore how AI can reduce escalation rates/ false positives, improve typology detection. We will walk through use cases and show how AI supports—not replaces investigative judgment.

Instructor: Stephanie Surratt

Course Description:

In the race to use AI, AI unfortunately isn’t a silver bullet. Deploying to the wrong use case without a strategic fit can backfire. This session discusses how to evaluate where AI truly adds value, how to assess readiness, and how to avoid the hype trap.

Instructor: Stephanie Surratt