Academic Journal of Computing & Information Science, 2023, 6(9); doi: 10.25236/AJCIS.2023.060914.
Yaqi Chen1, Qianshuo Feng2, Jiexin Zhang3, Zheng Zhao4
1School of Computer and Network Security, Chengdu University of Technology, Chengdu, Sichuan, China
2SJZ NO. 24 High School, Shijiazhuang, Hebei, China
3Department of Calligraphy, Taiyuan Normal University, Taiyuan, Shanxi, China
4Computer Science and Technology, Xiamen University Malaysia, Malaysia
In the insurance sector, spotting insurance fraud is crucial. Insurance is vital for finance and societal security. Frequent fraud causes losses to insurers and the financial system, impacting insurance companies' functioning and trust. Insurance fraud involves policyholders giving false information or creating incidents to claim compensation. This harms insurers and raises premiums for honest policyholders. To combat frauds, insurers must use methods to detect and prevent them. This study assesses popular ML algorithms like Gradient Boosting Decision Trees and XGBoost for fraud detection efficiency and verifiability. Metrics such as efficiency, recall rate, precision F1 score, and AUC score are calculated using these methods.
Insurance fraud, Machine Learning, XGBoost
Yaqi Chen, Qianshuo Feng, Jiexin Zhang, Zheng Zhao. Algorithmic Implementation for Insurance Fraud Detection. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 9: 93-101. https://doi.org/10.25236/AJCIS.2023.060914.
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