Flag the claims worth investigating — before the payout leaves.
Insurance fraud drains billions each year, and it follows rule-like patterns — a brand-new policy, a late-night incident, a suspiciously large claim. InsuranceFraudAi learns those rule chains with a cost-sensitive decision tree and raises a Flag for Review on the riskiest files, with a transparent reason for every call.
Score a claim in real time
Adjust the claim variables — the model re-scores instantly, entirely in your browser, and shows the exact decision path it took. Move the operating point to trade precision against recall.
Claim details
Load a scenario, or adjust any field to see the model re-score live.
Operating point
threshold 0.35Lower the threshold to catch more fraud (higher recall); raise it to reduce false alarms (higher precision).
Why this decision
The exact rule chain the claim followed through the tree — every prediction is fully auditable.
- Police Report Filed: “No”
- CCTV / MOBESE Footage: “Yes”
- Incident Hour 22 h (> 7 h)
- Adjuster Suspicion Score 61 /100 (≤ 66 /100)
- Claim Amount ₺46,000 (> ₺37,879)
Policy Renewal Reminders
Automatically notify customers before their policies expire. Choose notification channels and set up automated reminders to improve retention.
Notification Method
Message Preview
Dear Ayşe Demir, your policy POL-4471 expires on August 19, 2026. Please renew to keep your coverage active without interruption.
Incident Report Analyzer
Upload the mutual incident statements to automatically run a situation assessment, estimate each party's share, and generate an expert situation report.
Upload Both Party Statements
Drag & drop files here, or click to browse
Upload Both Party Statements
Precision, recall & the cost trade-off
Fraud is rare and false negatives are expensive, so accuracy is the wrong yardstick. These are the numbers that matter, measured on a held-out test set the model never saw during training.
Precision / recall vs decision threshold
Every threshold is a different business choice. There is no free lunch — catching more fraud always costs precision.
Confusion matrix at the balanced operating point
Where the model is right, and — just as important — how it is wrong.
What drives the model
Feature importance from total impurity reduction. The adjuster suspicion score, policy age and claim size dominate — matching how a human investigator triages.
Share of total Gini impurity reduction attributed to each feature across the tree. Ranks (not scaled input values) — robust to feature units.
How InsuranceFraudAi works
A deliberately simple, auditable pipeline. No black boxes — every prediction can be traced to a rule chain and defended to a regulator.
CART decision tree
A classification tree learns axis-aligned rule chains that mirror how fraud actually unfolds — exactly the structure investigators reason about.
Cost-sensitive learning
Class weights during training plus a cost-optimal threshold at inference encode that a missed fraud hurts far more than a false alarm.
Tunable operating point
Slide the threshold to move along the precision/recall curve and hit whatever risk appetite the fraud team is working to.
Explainable by design
Every score ships with the decision path and the top risk factors, so an analyst can justify opening — or closing — an investigation.