AI-Augmented AML Investigations on Databricks

Legacy AML workflows often rely on rule-based detections, manual data gathering, and stitching together evidence across multiple systems. This demo shows how Databricks supports an AI-augmented investigation workflow for anti-money laundering teams, from alert triage through suspicious activity report (SAR) filing.

You’ll see how investigators can prioritize cases, review rules-based and machine learning (ML) risk scores, analyze related transactions and entity networks, and use multi-agent chat to support analysis. The demo also shows executive dashboards, team performance views, graph exploration, and AI-assisted SAR/STR generation.

What you’ll learn from watching:

• Explore a governed workflow for AML investigations across alert triage, case review, and SAR filing
• View queue priority, criticality, and turnaround times in Databricks dashboards
• Review a single case view that brings together case notes, due diligence, media screening, SAR history, transaction trends, and entity relationships
• Use multi-agent chat to analyze patterns, compare them to policy guidance, and support escalation or dismissal decisions
• Generate SAR/STR reports with AI-assistance that prepopulates content, preserves a traceable record of the decision process