Cut Up to 89% of AML Transaction Monitoring False Positives

Because static, narrow rules chase yesterday’s behavior while customers and criminals both change faster.

• Static, narrow rules miss full behavior → broad net = noise.

• Legitimate behavior shifts faster than tuning cycles.

• Criminals adapt under fixed thresholds

• No learning loop from analyst dispositions.

Cutting false positives requires behaviourally adaptive, explainable learning layered on top of existing scenarios— not just another threshold pass.

Free capacity compounds directly into stronger risk coverage and lower unit costs.

• Deeper investigations → richer SAR narratives.
• Clear aging backlogs → fresher risk signals.
• Faster FIU look‑backs → regulator confidence.
• Higher analyst morale → lower churn.
• Lower cost per true positive → better KPIs.

Result: More genuine risk surfaced; less money & talent burned on noise.

• Tier‑1 EU Bank: Baseline >92% non‑suspicious → up to 89% reduction in false positives after go‑live.
• Backlog Cleared: 18k aging alerts → <500 fresh queue in 4 weeks.
• Analyst Leverage: Same headcount → 3.4× more time per truly suspicious case.
• Look‑Back Effort: ~80% less manual review (structured AI narratives).
• Precision Lift: From ~9% to 55–70% (alerts worth human eyes).
   

(Explainable. Controllable. Deployable.)

• Transparent Model: Interpretable tree ensemble (feature contribution per alert). 
• On‑Prem / Private Cloud: No customer data leaves your environment.
• Full Audit Trail: Versioned models, disposition lineage, immutable logs.
• Reviewer Trust: “Why scored Clear?” panel + counter‑factual suggestions.
• Governance Ready: Change control pack auto‑generated for model updates.
• Regulator Dialog: Plain‑language rationales; LLM summaries cite source fields.

Result: Reduced challenge time; faster approval of tuning changes.

(Beyond “yet another tuning tool.”)

• Active Learning Loop: Analysts teach the model daily → rapid precision lift.
• Error De‑Amplification: Does not blindly replicate past bias; flags inconsistency. 
• Behavior Drift Defense: Automatic re‑tuning vs customer & adversary shifts. 
• Narrative Automation: LLM creates standardized case notes (saves review time). 
• Fast Time to Value: API + flat files → first lift inside 30–60 days. 
• Adoption Focus: In‑app “Why” & confidence scores build analyst trust quickly. 

Outcome: Sustainable alert quality improvement, not a one‑off rules purge.