关键词: 神经网络, 广义线性模型, 回归树, 索赔频率

Abstract: Auto insurance is widely concerned by the public and plays a very important role in property insurance companies, so claim frequency models of auto insurance have been a focus of theoretical and practical research in non-life actuarial science. Currently generalized linear models (GLM) are very popular in claim frequency modeling, such as Poisson regression, negative binomial regression and Poisson-inverse Gaussian regression. Using a real dataset of auto insurance, the paper compares various GLMs with neural networks and regression tree. The result shows that neural networks are superior to GLMs in fitting the data and Poisson regression is better than negative binomial regression and Poisson-inverse Gaussian regression. Linear regression is the worst one in the models considered in the paper and regression tree is a little better than linear regression model.

Key words: Neural Network, GLM, Regression Tree, Claim Frequency