Academic Journal of Computing & Information Science, 2023, 6(8); doi: 10.25236/AJCIS.2023.060808.
Haoran Zheng1, Fan Peng2, Yawen Tian3, Zizhou Zhang4, Wenting Zhang5
1Software Engineering, Shandong University of Technology, Zibo, Shandong, China
2Internet of Things Project, Hebei University of Technology, Tianjin, China
3McMaster University, Toronto, Ontario, Canada
4Nttingham University, Ningbo, Zhejiang, China
5Qihua Academy Nanchang, Nanchang, Jaingxi, China
This research conducted a comprehensive study on predicting customer car insurance claims using Gradient Boosting Decision Tree (GBDT) and XGBoost models. The process included data exploration, feature engineering, model evaluation, and parameter tuning. The dataset was explored based on variable types and missing values, and further processed through mean encoding and outlier removal. Date features were also manipulated to create more meaningful features. Two models, GBDT and XGBoost, were trained and evaluated based on their AUC (Area Under the Curve) values. Both models demonstrated good predictive power, with GBDT slightly outperforming XGBoost. The results of this study provide valuable insights for predicting insurance claims, offering significant implications for further research and practical applications.
GBDT, XGBoost, Machine Learning, Car Insurance Fraud Detection
Haoran Zheng, Fan Peng, Yawen Tian, Zizhou Zhang, Wenting Zhang. Insurance Fraud Detection Based on XGBoost. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 8: 68-74. https://doi.org/10.25236/AJCIS.2023.060808.
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