In 1960, Bailey and Simon ( 1960) believed that the basis of classification rates was to group individuals of the same risk characteristics, determine the relative number of risk levels of each group and then calculate relative rates. Finger ( 2001) of this method has carried on the detailed narration, more scientifically expounds the classification rate set: the basic idea of the large number of individuals with homogeneous risk is divided into the same category, through the statistical method to determine the relative abundance of each group level and the corresponding parameters and then get the group rate.

Among them, classification rate is a kind of non-life insurance pricing method based on risk classification, which has a certain universality and is not lack of pertinence to specific groups. The classification rate, general rate and individual risk rate are the main non-life insurance pricing methods. Since the beginning of the 20th century, some scholars have studied non-life actuarial models. The risks in auto insurance were classified by cluster analysis in this paper. The two schemes differ in cost and accuracy. They believed that there are two options for insurance companies to classify the insured, one is that the insurance company is independent and the other is the risk assessment institution. Lereah ( 1983) and Cheng ( 2007) compared the effects of different risk classification subjects. Second, it is not easy to measure fairness and efficiency of resource costs according to classification and it may be effective to ban some sort of cost classification. First, any market equilibrium with no cost classification is better than that of without classification. Crocker and Snow ( 1986) made more detailed studies, they did not categorize the groups in the utility of boundary classification, otherwise, they came to the following conclusions. The first study of the risk classification is Hoy ( 1982), the R-S equilibrium, Wilson equilibrium and Miyazaki’s assumption are expected as the underwriter contracts in the cross-subsidy equilibrium model, the results showed that the causal relationship between risk classification and economic efficiency is not clear, which depends on the classification and form of equilibrium. The impact on equity and efficiency in the insurance market has always been the focus of debate. Risk classification plays a part to eliminate cross-subsidy between people with low and high risks, which contributes to promote the market efficiency, as well as the increase of social risk cost and the loss of fairness. It is believed that insurance policy recommendations that are based on Generalized linear models (GLM) can benefit from our findings. 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. Secondly, the claim frequency is better fitted using the Poisson distribution, however the claim cost is better fitted using the Gamma distribution.

First, more than 80% of the autos with an auto burden index of 20 or higher belong to the highest risk level. Three key findings can be concluded from our study. Then the Logistic Regression model is employed to fit the claim frequency, with the auto burden index considered. The claim frequency and the claim costs are fitted to select a better loss distribution. 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. 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. Among auto insurance rate-making models, there are very few considering auto types. Using a generalized linear model to determine the claim frequency of auto insurance is a key ingredient in non-life insurance research.

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