Insurance Fraud Detection: From Red Flags to Referral
Detection is triage, not proof
Fraud detection is the front end of the anti-fraud process: spotting patterns that justify a closer look. A candidate must know the difference between a red flag and evidence. Common indicators include loss dates close to policy inception, premium payment by unusual means, inconsistent descriptions across the claim form and recorded statement, delayed reporting without a credible reason, prior claim frequency, staged-accident fact patterns, inflated or duplicate medical billing, and vendor relationships that suggest collusion. Analytics extend the same logic: compare the claim against peer and historical norms, but treat an outlier as a question, not an answer.
Traps in exam questions are usually about overclaiming. One red flag rarely proves fraud; a cluster of independent indicators with a material amount at stake is stronger. Protected characteristics and mere lifestyle facts are not legitimate indicators. Detection staff should not cross into investigation techniques that require authorization, such as pretext interviews or pulling non-public data.
Rule of thumb: escalate when you have a cluster, materiality, and an unexplained inconsistency. Worked example: a new auto policy, a luxury vehicle reported stolen within two weeks, no police report at first notice, and a claimant who cannot describe the parking location. Each fact has an innocent explanation. Together they justify a referral, not an accusation; the referral should state facts and open questions.