AML & Fraud Alert Triage Automation
Triage quality, not queue size, is what finds the fraud
Alerts are enriched, scored, and drafted into investigator-grade narratives the moment they fire — with the evidence lineage documented so the control holds up under model risk review and examiner scrutiny.
Investigator time per alert
Detection of material model drift
Evidence lineage on every generated narrative
The constraints this work removes
False positives dominate the queue
AML and fraud alert queues run overwhelmingly false positive, and investigator time goes to sorting rather than investigating.
Real-time rails outrun batch-era controls
Instant payment rails move money in seconds, but many fraud and AML controls were designed around batch windows that gave analysts hours to review before funds cleared.
Narrative drafting is the bottleneck, not judgment
Investigators spend a large share of alert-handling time assembling evidence and writing the narrative, not deciding whether the pattern is suspicious.
Capabilities
Alert enrichment and scoring
Alerts scored against current fraud typologies and enriched with transaction and account context before an investigator opens the case.
Investigator-grade narrative drafting
Draft narratives generated with cited evidence, ready for investigator review rather than assembled from scratch.
Evidence lineage on every output
What data the system saw and what triggered the output is logged for every alert, to the standard model risk management already applies to other decision systems.
Model and threshold drift monitoring
Alert disposition patterns tracked against outcomes on a rolling basis so drift is caught before it shows up as a loss.
Human sign-off on consequential decisions
SAR filings and account actions retain a human decision-maker; agents draft and prioritize, never auto-dispose consequential outcomes.
See it against your own numbers
We start with a diagnostic on your current workflow, then run a parallel-control pilot so the lift is measured, not asserted.
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