Automated Insurance Fraud Detection Cuts False Positives by 60%
Rule-Based Fraud Detection Missed Complex Patterns and Drowned Analysts in False Alarms
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.
Anomaly Detection Framework Learns Complex Patterns from Historical Claims Data
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.
60% Fewer False Positives and a Fully Automated Detection Process
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.
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ReeDee Automates Over 50% of Zonky's User Registration Process
Zonky's back-office team was manually transcribing personal ID documents for every new loan applicant. Blindspot's ReeDee platform automated the process, eliminating the equivalent of 10 full-time employees' workload and processing documents in seconds.
Major Banking Institution
Continuous Portfolio Monitoring Lets the Bank "See Around Corners"
A major retail and SME bank needed to manage portfolio quality without exhausting resources on blanket surveillance. Blindspot's ADF platform surfaces anomalies in minutes — not days — providing early warnings that protect P&L from unexpected surprises.
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