Using a New Data Mining Method for Automobile Insurance Fraud Detection: A Case Study by a Real Data from an Iranian Insurance Company
الموضوعات : فصلنامه ریاضی
1 - Insurance Research CenterTehran, Iran
الکلمات المفتاحية: Fraud detection, Imbalanced data, XGBoost algorithm, Random forest algorithm,
ملخص المقالة :
The issue of car insurance fraud is one of the most important issues for insurance companies because it can impose a lot of financial losses on the insurance company. Therefore, timely and early detection of a suspected case can greatly prevent this loss. In the last decade, a lot of studies has been done using data mining techniques in this regard. In this article, we first examine the challenge of imbalanced data, and then, after fixing it, use a very new algorithm introduced in the field of fraud discovery, called XGBoost, for a real data set. Finally, we compare this method with an older method Random Forest algorithm and we will see that the new method works well.
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