Catching claims fraud early with predictive analytics

Fraud is one of the largest hidden costs in insurance. Every fraudulent payout increases premiums for honest policyholders, yet heavy manual checks slow down claims and frustrate genuine customers. Predictive analytics helps insurers do both: investigate less, and catch more.

From rules to risk scores

Traditional fraud controls rely on fixed rules, such as a claim filed soon after a policy starts or a repair above a set amount. Rules are useful, but fraudsters learn them quickly and they produce many false alarms.

A predictive model looks at hundreds of signals together and learns from past outcomes. Each new claim receives a risk score at first notice of loss, so investigators focus on the small share of claims that really need attention.

Signals that make a difference

• Timing patterns, such as claims close to policy start, renewal or cancellation
• Links between claimants, repairers, witnesses and addresses across different claims
• Claim values that fall outside the norm for the vehicle, property or treatment involved
• Changes in customer behaviour, such as sudden cover increases before a loss

The aim is not to accuse, but to prioritise: fast-track the claims that look genuine, and look closer at the few that don't.

Start with the data you have

Most insurers already hold what they need in their policy administration, claims and CRM systems. Bringing these together in a secure cloud data warehouse is the first step; dashboards for claims frequency, severity and loss ratio follow naturally, and fraud scoring builds on the same foundation.

Explore how our Insurance Analytics brings claims, risk and customer data into one view.