Every large financial institution has thousands to millions of alerts. False positives dominate and can overwhelm processing. Existing tools are often legacy rules engines + workflow, versus intelligent modern AI-based reasoning. Regulators care deeply about consistency and explainability and this is something that Thunk.AI excels at.
Workflow goal
Efficiently investigate AML alerts, recommend dispositions, and produce consistent, regulator-defensible narratives with human approval.
Outputs & Metrics
Alert clearance time
Investigator throughput
SAR quality consistency
False positive reduction
End-to-End Agentic Flow
1
Alert Intake
• Ingest transaction alert + triggering rule
2
Context Assembly
• Pull customer profile, historical behavior, peer group
• Retrieve prior alerts and dispositions
3
Investigation Reasoning
• Apply typologies
• Identify inconsistencies or benign explanations
4
Disposition Recommendation
• Clear / escalate / monitor
5
Narrative Drafting
• Draft SAR / STR narrative with citations
6
Human Review & Approval
• Investigator edits, approves, or overrides
7
Learning Loop
• Feedback improves future recommendations
Platform features highlighted
Multi-system reasoning
Explainable decision chains
Human-in-the-loop controls
Regulatory-grade documentation
Control Points
Mandatory human approval
Full decision trace
Read-only access to core banking data