Cut Up to 89% of AML Transaction Monitoring False Positives
Layer Kroton Active Learning AI on top of your current scenarios to separate noise from genuinely suspicious activity—without ripping out existing systems.
Historical alert ingestion + active learning loop → high precision in 6–12 weeks
In 30 seconds / EXECUTIVE SUMMARY
Kroton’s Active Learning layer cuts AML transaction‑monitoring false positives by up to 89% in 6–12 weeks, lifts precision from ~9% to 55–70%, collapses alert backlogs (e.g., 18k → <500 in 4 weeks), and frees 50%+ of analyst time. It’s explainable, regulator‑ready (tree ensembles, SHAP, monotonicity constraints) and deployable on‑prem or in a private cloud. Run the 2‑minute ROI calculator to see your “false‑positive tax.”
Why Are 90%+ of Alerts Still 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.
If You Reclaim 50%+ of Analyst Time You Can:
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.
THE KROTON FALSE POSITIVE REDUCTION LAYER
Proven Outcomes
RETURN ON INVESTMENT (ROI) CALCULATOR
Frequently Asked Questions
Please reach us at info@h3m.io if you cannot find an answer to your question.
We layer an Active Learning model on top of your existing rules/scenarios. It ingests historical alerts and investigator decisions, then re‑ranks or suppresses low‑value alerts while preserving high‑risk ones. No rip‑and‑replace.
Typical results: up to 89% FP reduction in 6–12 weeks, precision lifting from ~9% to 55–70%, alert backlogs collapsing (e.g., 18k → <500 in 4 weeks), and 50%+ analyst time freed.
Historical alerts, investigator outcomes (TP/FP/SAR), customer & transaction features, and scenario metadata (thresholds, parameters). Optional: case disposition notes to improve label quality.
We apply label‑quality governance (gold sets, double‑annotation on edge cases, agreement metrics, drift checks) before feedback is allowed to update models.
Models are explainable (tree ensembles + SHAP, monotonicity constraints where needed) with full audit trails of every optimisation decision, threshold move, and model version.
We run PSI/KS/Wasserstein tests per segment monthly (or as agreed). When tolerances are breached, guard‑railed Bayesian optimisation/bandits re‑allocate thresholds with human approval.
No—active learning surfaces uncertain/novel patterns for analyst review, and off‑policy evaluation ensures new policies are validated against historical logs before going live.
Via champion–challenger in shadow using off‑policy estimators (e.g., IPS/DR) to compare SAR yield per analyst hour, backlog impact, and rank stability—before deployment.
On‑prem or private cloud. We align with your data‑residency, security, and model‑risk governance requirements.
Yes. We regularise and calibrate models for low‑data settings, and can start with semi‑supervised / rules‑plus‑re‑ranking approaches.
With clean data access, 4–6 weeks to first measurable lift; 6–12 weeks for the full optimisation cycle and operating playbooks.
Your current “false‑positive tax”: analyst minutes, backlog cost, and potential savings from FP reduction and precision lift. It gives an order‑of‑magnitude business case you can take to ExCo.
Both. We can batch‑ingest via secure flat files or stream via APIs. Output (re‑ranked alerts, suppression flags, scores, SHAP explanations) can be pushed back the same way.
Yes. Feature flags / kill‑switches let you revert to pure rules instantly, and every threshold/model change is versioned for straightforward rollback
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