- H2O Driverless AI builds alert-scoring and behavior models from your own transaction, customer, and case-outcome data, with reason codes on every prediction.
- The scoring layer runs beside your existing transaction monitoring system as a prioritization and challenger layer, so your rules, thresholds, and reporting process stay in place.
- An agent can gather customer history, linked transactions, and policy text for a flagged alert, then draft a summary your analyst reviews and approves.
- Every model ships with documentation for model risk review, and H2O MLOps keeps lineage, drift monitoring, and an audit trail.
Score alerts before they pile into your queue
Your transaction monitoring rules catch known laundering patterns, and they do. They also fill the queue with alerts that turn out to be legitimate activity, and each one costs your team time before it can be closed.
Launderers adapt faster than rules get rewritten. Structuring, mule networks, and layered payments sit between thresholds, so the gaps surface in look-backs and regulatory findings rather than in today's queue.
Anything you change has to survive scrutiny. Model risk, internal audit, and your regulator ask why an alert was closed, what data the model used, and how it is monitored, and a score nobody can explain does not pass that review.
How AI anti-money laundering scoring works
Your financial crime team builds explainable models in H2O Driverless AI, runs them beside the monitoring system you already have, and uses an agent to assemble the case file. Your analysts spend their time deciding, not gathering.
Step 1: start from the data you already hold
Begin with alert history and case outcomes from your monitoring system, joined to transactions, customer profiles, and KYC attributes. Alerts from existing systems become model inputs instead of being replaced.
Step 2: learn the patterns behind productive alerts
H2O Driverless AI trains classification models on which past alerts led to escalation and which were closed. Automatic feature engineering builds behavior features such as transaction velocity, repeated cash deposits just under a threshold, and changes against a customer's own history and peer group.
Step 3: score and prioritize every alert
Each alert receives a risk score and reason codes that show which behaviors drove it. Your policy decides how scores route work: high-risk alerts move to the front, and nothing closes automatically unless you say so.
Step 4: assemble the case with an agent
For alerts that need investigation, an agent in Enterprise h2oGPTe collects customer history, linked transactions, and the relevant policy or regulatory guidance, then drafts a case summary with a suggested risk level. Your analyst reviews the evidence, makes the decision, and owns any suspicious activity report.
Step 5: document, deploy, and monitor
H2O Driverless AI writes model documentation while the model trains. H2O MLOps deploys the scoring pipeline, tracks model drift, and keeps lineage and an audit trail.
Keeping model risk and audit satisfied
H2O Driverless AI produces reason codes for every prediction, so model risk and internal audit can see what drove a score, not just the number. The scoring layer runs beside your existing monitoring system, so your rules, thresholds, and reporting process stay in place while you test it as a challenger.