Key takeaways
- H2O Driverless AI trains alert-scoring models on your own past screening dispositions, with reason codes on every score.
- H2O works beside the name and payment screening engine you already run: your lists, matching rules and thresholds stay in place.
- Enterprise h2oGPTe drafts an alert summary that cites the list entry, the customer or payment record and the policy it relied on, for your analyst to accept or change.
- Every model ships with documentation and runs under monitoring, in four deployment options.
Screening alerts outpace the team that clears them
Your screening engine compares customer names and payment parties against sanctions lists. To avoid missing a true match it has to match loosely, so common names, transliterations and partial addresses fill the queue.
A payment alert holds a payment until someone clears it. Every hour an analyst spends on a clear false positive is an hour a legitimate customer waits, while a real match sits in the same queue. Lists change often, and each change sends a new wave of alerts through your customer and payment records, and each disposition has to stand up later when an examiner or internal audit asks why an alert was closed.