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Application of Hierarchical Model in Non-Life Insurance Actuarial Science

DOI: 10.4236/me.2018.93025, PP. 393-399

Keywords: Hierarchical Model, Actuaries, Non-Life Insurance, Random Effect

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Loss data structures in non-life insurance businesses are increasingly complex, and the tendency of correlation and heterogeneity is gradually presented. Hierarchical model can breakthrough limitation that the traditional rate determination method only analyzes the loss data of the same insurance policy; meanwhile, the accuracy of complex structure data prediction is improved. This paper, using a hierarchical generalized linear model, studies the non-life rate determination of multi-year loss data and takes auto insurance data for empirical analysis. The research results show that GLMM’s fitting degree is greatly improved compared with GLM, considering the random effects. It can more effectively reflect different risk individual differences and also reveal the heterogeneity and correlation of risk individual loss during multiple insurance periods.


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