文章基本信息

标题：The Accuracy of XGBoost for Insurance Claim Prediction
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作者：Muhammad Arief Fauzan ; Hendri Murfi
期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2018
卷号：10
期号：2
出版社：International Center for Scientific Research and Studies
摘要：The increasing trend of claim frequency and claim severity for auto-insurance result in need of methods to quickly file claims while maintaining accuracy. One of them is machine learning that treats the problem as supervised learning. The volume of the historical claim data is usually large. Moreover, there are many missing values for many features of the data. Therefore, we need machine learning models that can handle both data characteristics. XGBoost is a new ensemble learning that should be very suitable for both data characteristics. In this paper, we apply and analyze the accuracy of XGBoost for the problem of claim prediction. We also compare the performance of XGBoost with that of another ensemble learning, i.e., AdaBoost, Stochastic GB, Random Forest, and online learning- based method, i.e., Neural Network. Our simulations show that XGBoost gives better accuracies in term of normalized Gini than other methods.
关键词：claim prediction; large volume; missing values; ensemble learning; ; XGBoost