Insurance Fraud Prediction Model Based on eXtreme Gradient Boosting
DOI:
https://doi.org/10.54097/rd1m7n64Keywords:
Auto Insurance, Fraud Prediction, XGBoostAbstract
As the volume of insurance transactions continues to increase, combating insurance fraud has become increasingly important. One of the key challenges is how to predict fraud based on high-dimensional data. In the current research landscape, deep learning methods that perform well often require large amounts of data, but their training and deployment also pose significant computational challenges. This paper, based on the Alibaba Tianchi dataset, explores the feasibility of constructing a low-cost prediction system through feature engineering and parameter tuning. Additionally, this paper verifies the robustness of the eXtreme Gradient Boosting (XGBoost) model in handling high-dimensional sparse features and imbalanced data.
References
[1] Yu, W., Feng, G., Zhang, W. (2017) A Research on Fraud Detection System and Gang Identification of Vehicle Insurance. Insurance Studies, 2: 63–73
[2] Artís, M., Ayuso, M., Guillén, M. (1999) Modelling different types of automobile insurance fraud behaviour in the Spanish market. INSURANCE MATHEMATICS & ECONOMICS, 24: 67-81
[3] Caudill, SB., Ayuso, M., Guillén, M. (2005) Fraud detection using a multinomial logit model with missing information. JOURNAL OF RISK AND INSURANCE, 72: 539-550
[4] Ye, MH. (2011) Insurance frauds identification research based on the BP neurological network--with China motor insurance claim as an example. Insurance Studies, 3: 79-86
[5] Zhang, B. (2018) Master of Anti-fraud research on auto insurance claims based on data mining technology (academic dissertation, University of International Business and Economics). master.
[6] Yang, JX., Chen, K., Ding, K., Na, CN., Wang, M. (2023) Auto Insurance Fraud Detection with Multimodal Learning. DATA INTELLIGENCE, 5: 388-412
Downloads
Published
Issue
Section
License

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