Why fixed rules over-alert
Rule-based thresholds are blunt. They fire on benign behaviour and still miss novel laundering patterns, so analysts spend their days clearing noise while the genuine case waits in the same queue.
Score behaviour in context
The measure is the false-positive rate, false alerts over total alerts. Scoring behaviour in context rather than on a fixed threshold cuts it sharply while lifting real detection. The FFIEC manual sets what examiners expect: tuned thresholds, model validation, and explainability.
Where the ERP closes the loop
On Hudace, transaction, customer, and case data share one platform, so Xenon AI risk-scores and prioritises alerts, suppresses repetitive benign patterns, and surfaces network anomalies for review.
An analyst dispositions every alert and makes the reporting decision. AI is a triage layer under human and regulatory control; it never files.
The numbers to watch
Cut the noise without lowering real detection.
False-positive rate
False alerts / total alerts. Often above 90 percent on rules alone.
SAR conversion rate
Alerts that become filed reports. The signal that real risk is being caught.
Alert backlog
Unworked alerts. Falls as triage prioritises the ones that matter.
Time to disposition
Time to clear or escalate an alert. Shorter without cutting corners is the goal.
See smarter AML triage on Hudace
Talk to our team about scoring alerts in context, with the audit trail intact.