Fraud detection in insurance and finance is a signal detection problem. Fraudulent transactions and claims are rare relative to legitimate ones, and they actively try to look legitimate. This guide covers the detection approaches that work in practice and how to connect them to effective human review workflows.

Common Fraud Signal Categories

Amount anomalies - Claims or transactions that are significantly above or below baseline for their type. A water damage claim for EUR 45,000 in a region where similar claims average EUR 8,000 is a signal. So is a claim for an unusually round number (fraudulent estimates are often fabricated with round numbers). Neither proves fraud - they warrant review.

Network signals - Fraudulent actors often reuse infrastructure across multiple claims or transactions: the same contractor appearing across unrelated claims, the same phone number linked to multiple claimants, the same IP address submitting multiple applications. Network analysis across the claim population finds these connections that are invisible when reviewing claims individually.

Timing patterns - Policy taken out very recently before a claim is a classic signal. Recurring claims at regular intervals. Claims filed immediately before coverage lapses.

Documentation signals - Inconsistencies between documents in the same claim: repair estimate line items that do not match the described damage, photos with metadata timestamps that contradict the reported incident date, handwriting analysis inconsistencies on paper forms.

Behavioral signals - Applicants who are unusually specific about coverage amounts during policy intake, customers who decline standard options that would benefit a legitimate claimant, inconsistencies between the claimed circumstances and the reported details.

Rule-Based Detection

Rule-based detection is explicit and auditable. Rules encode known fraud patterns as logical conditions:

  • Claim amount > 3x regional average for claim type
  • Policy age at claim date < 60 days
  • Same contractor ID appears in more than N claims in rolling 30-day window
  • Claimant has submitted more than N claims in rolling 12 months

Rules are fast, interpretable, and easy to explain to regulators. They work well for known patterns. Their limitation is that they cannot detect novel patterns and they can be gamed once the rules become known.

Start with rules. They provide a baseline and generate the labeled data you need to train statistical models.

ML-Based Anomaly Detection

Statistical models detect patterns too subtle or complex to express as rules. Common approaches for fraud:

Isolation forests - Identify claims or transactions that are anomalous relative to the population without requiring labeled fraud examples. Useful in cold-start situations where you have few confirmed fraud cases.

Gradient boosted classifiers - When you have labeled historical fraud data, supervised classifiers produce calibrated probability scores. These typically outperform rule-based systems on recall (finding more fraud) once sufficient training data exists.

Graph-based detection - Network analysis models identify fraud rings by looking at connection patterns across claims. An entity that appears in many separate claims is suspicious in ways that are not visible in per-claim features.

ML models are less interpretable than rules. When a model flags a claim, the reviewer needs to understand why. Use SHAP values or similar explainability tools to surface the top contributing features for each flagged case.

Human Review Workflow

Flagged cases need structured review. The human reviewer needs:

  • The specific signals that triggered the flag, with source references
  • The claim documents for context
  • The claimant’s and any third party’s prior history
  • A clear set of actions available: clear flag, request additional documentation, escalate to fraud specialist, deny claim

Review queues should be prioritized by signal strength and claim value. A high-signal flag on a EUR 50,000 claim should not sit behind a low-signal flag on a EUR 500 claim.

The reviewer’s decision should feed back into the detection system. Confirmed fraud cases update the training data for ML models. False-positive patterns inform rule threshold adjustments. Without this feedback loop, the detection system cannot improve.

Metrics That Matter

Detection rate (recall) - What fraction of actual fraud cases are flagged? This is the primary objective metric.

Precision - What fraction of flagged cases turn out to be fraud? Low precision means reviewers spend most of their time on false positives.

Review burden - What fraction of claims require human review? High review burden indicates the system is over-flagging and will create adjuster fatigue.

Target the precision-recall trade-off deliberately. In most insurance contexts, missing fraud is more expensive than reviewing false positives - so tuning toward higher recall at the cost of precision is correct.