10.71932/IJM.2024.1126834

Using a New Data Mining Method for Automobile Insurance Fraud Detection: A Case Study by a Real Data from an Iranian Insurance Company

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  • Maryam Esna-Ashari*1,
  1. Insurance Research CenterTehran, Iran

Received: 15-01-2024

Accepted: 25-03-2024

Published in Issue 30-03-2024

Copyright (c) 2024 International Journal of Mathematical Modeling & Computations

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Esna-Ashari, M. (2024). Using a New Data Mining Method for Automobile Insurance Fraud Detection: A Case Study by a Real Data from an Iranian Insurance Company. International Journal of Mathematical Modelling & Computations, 14(1), 15-20. https://doi.org/10.71932/IJM.2024.1126834
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Abstract

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.

Keywords

  • Fraud detection,
  • Imbalanced data,
  • XGBoost algorithm,
  • Random forest algorithm
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