Automated Insurance Fraud Detection Cuts False Positives by 60%

ČSOB, the fifth-largest insurer in the Czech Republic, possessed existing fraud detection processes and dedicated staff but sought to enhance detection efficiency, reduce false positives, and address evolving fraud threats that their rule-based systems could not adapt to quickly enough.

Blindspot applied their Anomaly Detection Framework (ADF) to historical claim data. The system processes time-series data and clusters it using statistical and machine learning algorithms to identify significant outliers. It enables analyst feedback for continuous calibration and pattern recognition refinement.

The AI models detected complex patterns from claims, policy, and other data, enabling reliable and automated fraud detection. False positives were reduced by 60%, operational efficiency improved, and customer experience was enhanced. Analysts can now concentrate on genuinely suspicious cases rather than sifting through noise.