Machine Learning Approaches for Auto Insurance Big Data

Mohamed Hanafy and Ruixing Ming
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Mohamed Hanafy: School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China
Ruixing Ming: School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China

Risks, 2021, vol. 9, issue 2, 1-23

Abstract: 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, naïve 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.

Keywords: big data; insurance; machine learning; a confusion matrix; classification analysis (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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