Fraud Detection Sensitivity vs False Positives

Business Context

Increasing the sensitivity of fraud models and detection rules can identify more suspicious claims, but it also increases the number of legitimate claims incorrectly flagged for investigation. Excessive false positives consume specialist capacity, delay genuine customers, and reduce confidence in detection tools. Raising thresholds reduces unnecessary referrals but can allow sophisticated fraud to pass undetected.

Insurance TRIZ Resolution

Fraud detection can use multiple levels of intervention rather than a single referral threshold. Moderate signals can trigger background validation or additional automated checks, while combinations of stronger indicators initiate specialist investigation. Outcomes from investigations should continuously recalibrate detection thresholds, variables, and combinations according to their actual predictive value.

Applicable TRIZ Principles

Principle 1 – Segmentation separates fraud signals according to strength and required response.

Principle 23 – Feedback uses investigation outcomes to continuously refine detection logic.

Principle 15 – Dynamics adjusts intervention intensity according to accumulated evidence.

Expected Outcome

Higher fraud detection precision

Fewer legitimate claims delayed

Better use of investigation resources

Reduced fraud leakage

Decision Indicators

Early indicators that this contradiction is limiting claims performance include:

Fraud referrals increase without corresponding growth in confirmed fraud.

Investigators spend substantial time clearing low-risk claims.

Claims teams routinely ignore alerts because false positives are excessive.

Detection thresholds are increased simply to manage investigation workload.

Legitimate customers experience significant delays after automated flags.

Monitoring these indicators helps insurers increase fraud detection capability without overwhelming the claims system with weak signals.