AI Agents in Financial Crime Compliance: Threat or Trusted Ally?

Filmed live at ACAMS, watch this panel discussion to learn how leading banks are applying agentic AI safely and practically, with humans still firmly in control.

What You’ll Learn

What AI agents really are and what people get wrong

AI in compliance is often misunderstood as a replacement for investigators. This session breaks down the reality, pointing out that AI doesn’t make decisions, but filters, analyzes, and surfaces risk so humans can act faster and more effectively.

Why success isn’t about cost reduction

Learn why leading institutions are shifting away from cost savings as the primary goal and are instead focusing on reducing noise, improving signal quality, and identifying more true risk.

How AI actually improves investigations

See how AI helps investigators by:

Aggregating and analyzing large volumes of data in seconds
Highlighting high-risk patterns humans might miss
Reducing time spent on repetitive case prep

So teams can focus on judgment-driven, high-value decisions.

Where to draw the line between AI and human decision-making

Understand how organizations define “smart checkpoints” – keeping humans responsible for high-risk, high-impact decisions like SAR filings, customer actions, and regulatory accountability.

Why AI governance is becoming a new core capability

As AI adoption grows, so does the need for continuous testing and validation, monitoring for model drift, and dedicated oversight roles. Learn how leading teams are evolving their operating models to manage AI safely at scale.

Why You Should Watch It

Hear how banks are applying AI in the real world

This session features senior leaders from M&T Bank and TD Bank Group sharing practical experiences on what works, what doesn’t, and what’s still evolving.

Move beyond hype to practical implementation

AI is often framed as all-or-nothing. This panel explains how institutions are taking a phased, use-case-driven approach, which involves starting small and scaling responsibly.

Understand how to improve detection without losing control

Learn how to reduce false positives while maintaining (and even strengthening) human oversight through structured workflows, checkpoints, and governance frameworks.

Prepare for increasing regulatory expectations

Gain insight into what regulators expect around explainability, traceability, and auditability and how to bring them along on your AI journey from the start.

Learn how to build trust in AI over time

Trust isn’t static. Discover how organizations maintain confidence in AI systems through transparency, continuous monitoring, and ongoing model validation.