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Discriminant Analysis in Classification Rate Making on Automobile

Author: ChenXueLian
Tutor: LiuLePing
School: Tianjin University of Finance and Economics
Course: Statistics
Keywords: Fisher Discriminant Analysis Bayes Discriminant Analysis Classification Rate Making
CLC: F842.6
Type: Master's thesis
Year: 2009
Downloads: 235
Quote: 0
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Abstract


With the development of China’s insurance system’s reform and the comprehensive entry of foreign insurance companies, our own insurance industry has to face the fierce competition increasingly. Those who can analyze the information that hide in the insurance companies’customers data correctly, will be able to control the claims risk better, and can also provide customers with better insurance products and services. Finally, they will win in the fierce competition.Discriminant analysis is a statistical method that can be widely applied in the multivariate statistical analysis, and it is based on the variables’value of characteristics of things and their own class to get the discriminant function. It is a kind of analysis method that we make classification on unknown things based on discriminant function. There are Fisher discriminant and Bayes discriminant in Discriminant analysis methods, which are often used. Bayes discriminant is a discriminant analysis which is based on probability. In the beginning of analysis we need get distribution density function of every class, and also need to know the prior probability that various types of sample points belong to every class. At the same time, we sum up the regularity of objective things classification to establish discriminant function; at the end of the analysis, we calculate the largest probability and the smallest misjudgement loss that each sample point belongs to some class and determine which predicting class every sample point belongs to.Fisher discriminant is another method of discriminant analysis, based on the principle of analysis of variance. The basic idea of Fisher discriminant is to project and make the same types of points of transformed data points "as near as possible", and different types of points "as far as possible separation" to achieve the purpose of classification. Discriminant analysis has been applied in many fields, such as: in companies’ financial analysis, to give analysis and evaluation of the financial health status and make early warning of upcoming financial crisis, based on the financial indicators of listed companies; in the market prediction, to determine whether or not the products is ready sell in the next quarter based on previous survey data.We apply discriminant analysis to the system of rate of automobile premium, and focus on how to improve the accuracy of rate-making of vehicles risk in the application of discriminant analysis. We use Fisher and Bayes discriminant analysis methods respectively to do empirical study on insurance data, and do some comparison job between discriminant results of the two discriminant analysis methods, combined with of practical experience and the insurance theory. The study illustrates it is feasible in theory to do the discriminant analysis in the rate redefinition of automobile premium. Discriminant analysis can test whether the classification of insurance companies is accurate and rates are reasonably charged. After taking into account the various risk factors, the application of discriminant analysis can more fundamentally mine the available information, which will lower the probability of erroneous judgment and make rate redefinition of automobile premium more accurate.

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CLC: > Economic > Fiscal, monetary > Insurance > China's insurance industry > Various types of insurance
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