FNOL Triage & Severity
Classifies, scores severity, routes to fast-track or handler pod. Confidence < 0.75 auto-routes to human. Model card + fairness testing on every release.
See claims solutions →You're measured on cycle time, indemnity leakage, adjuster productivity, and NPS — usually all four at once. FPTInsure ships claims AI that reads your files, triages FNOL, spots fraud, and hands the hard cases to your best adjusters faster.
The problems we hear from every COO / Head of Claims we talk to. If two of these match your week, keep reading.
Peaks blow up cycle-time SLAs. Overtime spend rises, quality drops, and complaints follow.
Investigators drown in low-signal referrals. Real fraud pays out before SIU gets there.
60% of adjuster time is on documents and coordination, not the customer. Attrition follows.
Every vendor promises AI. Very few can show you an EU AI Act technical file or a bias report your regulator will accept.
The four moves that shift your metrics fastest — and where in our stack they live.
Classifies, scores severity, routes to fast-track or handler pod. Confidence < 0.75 auto-routes to human. Model card + fairness testing on every release.
See claims solutions →Explainable referral scores with SHAP-style contributing signals. The model never denies a claim — it prioritises where SIU spends time.
See Fraud solution →Document extraction, summary generation, coverage-check drafting. Grounded in your policy library, every claim shows sources.
See AI Factory →Inbound voice agent handles status checks, endorsements, callbacks. Warm-transfer on complaint keywords or vulnerable-customer signals.
See FPT.AI →Three links that get the rest of your organisation onside.
The questions your peers, procurement, and board will fire back at you. Our answers.
No. Every coverage decision, decline, and cancellation is a Gate pattern — human decides. AI proposes and cites sources; the licensed adjuster owns the call.
Cycle time and leakage move first — quarters, not years. Loss ratio impact is programme-dependent; we scope it against your triangle data before we sign.
The adjuster is the user, not the target. Every model output shows its sources; every override is one click and audit-logged. Adjusters adopt tools that make them faster, not tools that grade them.
Quarterly fairness testing on stratified cohorts. Misses trigger a documented remediation workflow — not a silent override.
Pick the one that fits where you are.
Send us anonymised FNOL data — we'll benchmark cycle time and leakage vs. peers.
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