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Int. J. Financial Stud. 2018, 6(1), 18; doi:10.3390/ijfs6010018

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

1
College of Mathematics and Statistics, Chongqing University, Chongqing 401331, China
2
College of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710000, China
3
Lingnan College of Sun Yat-sen University, Guangzhou 510000, China
*
Author to whom correspondence should be addressed.
Received: 13 November 2017 / Revised: 27 January 2018 / Accepted: 1 February 2018 / Published: 7 February 2018
(This article belongs to the Special Issue Finance, Financial Risk Management and their Applications)
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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. View Full-Text
Keywords: auto insurance; claim frequency; logistic regression model auto insurance; claim frequency; logistic regression model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Duan, Z.; Chang, Y.; Wang, Q.; Chen, T.; Zhao, Q. A Logistic Regression Based Auto Insurance Rate-Making Model Designed for the Insurance Rate Reform. Int. J. Financial Stud. 2018, 6, 18.

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