Machine Learning Approaches for Auto Insurance Big Data

被引:45
作者
Hanafy, Mohamed [1 ,2 ]
Ming, Ruixing [1 ]
机构
[1] Zhejiang Gongshang Univ, Sch Stat & Math, Hangzhou 310018, Peoples R China
[2] Assiut Univ, Fac Commerce, Dept Stat Math & Insurance, Asyut 71515, Egypt
关键词
big data; insurance; machine learning; a confusion matrix; classification analysis; CUSTOMER RETENTION; CLASSIFICATION;
D O I
10.3390/risks9020042
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
The growing trend in the number and severity of auto insurance claims creates a need for new methods to efficiently handle these claims. Machine learning (ML) is one of the methods that solves this problem. As car insurers aim to improve their customer service, these companies have started adopting and applying ML to enhance the interpretation and comprehension of their data for efficiency, thus improving their customer service through a better understanding of their needs. This study considers how automotive insurance providers incorporate machinery learning in their company, and explores how ML models can apply to insurance big data. We utilize various ML methods, such as logistic regression, XGBoost, random forest, decision trees, naive Bayes, and K-NN, to predict claim occurrence. Furthermore, we evaluate and compare these models' performances. The results showed that RF is better than other methods with the accuracy, kappa, and AUC values of 0.8677, 0.7117, and 0.840, respectively.
引用
收藏
页码:1 / 23
页数:23
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