Improving fraud detection in insurance claims.
A global insurance company wanted to improve fraud detection in its insurance claims, while also reducing time and resources spent on false alarms.
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Situation: Actual frauds going unnoticed, large number of false alarms.
The client had an existing fraud detection model in place, which was not proving very effective in identifying suspicious patterns and inflated claims. While a large number of actual frauds were going unnoticed, there were a lot of false alarms which were taking up time and resources. The client wanted a robust system for fraud detection to reduce its losses.
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Logistical regression, fraud analytics and first notice of loss analytics to create multiple level filtering system.
We collected historical transaction data from the entire claims lifecycle, from the processes and systems involved. We used claims origination analytics, claims history analytics, to identify patterns which predict future suspicious fraudulent activity. We also used first notice of loss analytics to flag the most likely frauds. Further we ranked all possible indicators of anomalies and risks, and built a logistics regression model to predict the fraud propensity.
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Caught 25% more fraudulent claims, added USD 25 million in saving.
The client used the new analytics model for multiple level filtering of claims, which helped in better fraud management. The client was able to identify 25% more fraud cases while reducing false alarms by 50% over the next year, and add USD 25 million in savings.