Multiline insurer · Financial Services & Insurance
Insurer Cuts False Positives in Fraud Alerts by Half
An insurer replaced static rules with multi-signal risk scoring that prioritizes genuinely suspicious claims—preserving analyst decision authority end to end.
About the client
A multiline insurer—property, casualty, and specialty lines—screening high daily claim volume through legacy rules engines tuned years ago and tuned rarely.
The challenge
Rules-based fraud screening flooded analysts with false positives while adaptive fraud patterns slipped through. Senior investigators spent their days dismissing noise instead of working cases. Alert quality varied with rule maintenance backlogs, genuinely suspicious claims waited in the same queue as noise, and every audit asked for evidence the rules could not produce.
The engagement
Global AI Nexus combined behavioral, claim, network, and historical signals into a risk score with explainability attached—every flagged case shows why it was flagged.
The system recommends priority; analysts decide outcomes. Model suggestions never auto-close or auto-deny a claim.
Confirmed outcomes feed back monthly, so the detection adapts as fraud patterns shift—replacing the rule-maintenance backlog with a retraining pipeline that runs whether or not anyone schedules it.
In their words
“The best thing the system does is know when to be quiet. Analysts open a queue now and it’s cases, not noise. Their expertise finally goes to the claims that deserve it.”
SIU Director, Multiline insurer
The results
- 01False positive alerts fell 52% at equal detection sensitivity.
- 02Analysts surface 2.4x more genuine cases per hour.
- 03Every alert carries an evidence trail supporting regulatory review.
- 04Fraud pattern libraries update monthly from analyst outcomes.
- 05Genuinely suspicious claims now reach senior investigators while the trail is still warm.
GAINX systems deployed
The solution systems behind this outcome.
Start with the business objective