Using a New Data Mining Method for Automobile Insurance Fraud Detection: A Case Study by a Real Data from an Iranian Insurance Company
محورهای موضوعی : فصلنامه ریاضی
1 - Insurance Research CenterTehran, Iran
کلید واژه: Fraud detection, Imbalanced data, XGBoost algorithm, Random forest algorithm,
چکیده مقاله :
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.
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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