Insurance
ML models trained on historical claims data that detect staged accidents, overstated losses, and provider billing fraud before payouts are made.
0h
Response time
0+
Projects delivered
0+
Years in production
Industry overview
Machine learning systems trained on claims history and external data sources to identify fraudulent patterns — from staged accidents and inflated losses to provider network billing schemes — before claims are paid.
At a glance
Insurance fraud accounts for 10–20% of all claims paid, yet most detection systems still rely on keyword rules and manual investigation referrals. By the time fraud is identified, payments have already been made. ArrayMatic builds ML fraud detection that scores every claim at intake — catching schemes that rule systems never see.
We combine anomaly detection on claims data with graph network analysis across claimant, provider, and repair shop relationships — exposing fraud rings that individual claim analysis misses. Image forensics models flag staged damage in submitted photos. Scoring happens in real time so fraudulent claims are flagged for investigation before any payment is authorised.
Key capabilities
Engagements are scoped to your business context — these are the core capabilities we bring to insurance clients.
Multi-source anomaly detection across claims and policy data
Social network analysis for fraud ring identification
Image forensics for staged damage and document tampering
Provider billing pattern analysis for healthcare and auto
Real-time claim scoring before payment authorisation
Investigation workflow integration with SIU case management
Built with
Work with us
Share what you're building — we'll respond within one business day with questions or a proposal outline.
