A Logistic Regression Based Auto Insurance Rate-Making Model Designed for the Insurance Rate Reform

Zhengmin Duan, Yonglian Chang, Qi Wang, Tianyao Chen and Qing Zhao
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Zhengmin Duan: College of Mathematics and Statistics, Chongqing University, Chongqing 401331, China
Yonglian Chang: College of Mathematics and Statistics, Chongqing University, Chongqing 401331, China
Qi Wang: College of Mathematics and Statistics, Chongqing University, Chongqing 401331, China
Tianyao Chen: College of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710000, China
Qing Zhao: Lingnan College of Sun Yat-sen University, Guangzhou 510000, China

IJFS, 2018, vol. 6, issue 1, 1-16

Abstract: Using a generalized linear model to determine the claim frequency of auto insurance is a key ingredient in non-life insurance research. Among auto insurance rate-making models, there are very few considering auto types. Therefore, in this paper we are proposing a model that takes auto types into account by making an innovative use of the auto burden index. Based on this model and data from a Chinese insurance company, we built a clustering model that classifies auto insurance rates into three risk levels. The claim frequency and the claim costs are fitted to select a better loss distribution. Then the Logistic Regression model is employed to fit the claim frequency, with the auto burden index considered. Three key findings can be concluded from our study. First, more than 80% of the autos with an auto burden index of 20 or higher belong to the highest risk level. Secondly, the claim frequency is better fitted using the Poisson distribution, however the claim cost is better fitted using the Gamma distribution. Lastly, based on the AIC criterion, the claim frequency is more adequately represented by models that consider the auto burden index than those do not. It is believed that insurance policy recommendations that are based on Generalized linear models (GLM) can benefit from our findings.

Keywords: auto insurance; claim frequency; logistic regression model (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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