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With the improvement of people's quality of life, the number of cars in China is gradually increasing, and the auto insurance industry is developing rapidly and is an important part of property insurance. However, China's auto insurance market is still far from the international level in terms of accuracy rating determination: the international mature insurance market generally adopts the car-based pricing model, and the development of this model in China is relatively backward. Generalized Linear Models (GLMs) are commonly used to predict the intensity of auto insurance, but they require high data requirements and insufficient samples, which may lead to unreliable parameter estimates. In this paper, the Generalized Additive Models for Location, Scale and Shape (GAMLSS) can improve the limitations of GLM and perform better on various distribution parameters. In addition, China currently focuses on the purchase price of new cars as the main rating determination factor, which fails to match the differences of different claims caused by the crashworthiness and maintenance economy of the car itself with the motor vehicle premiums. In order to fill the gap in this area in China, this paper will introduce the results of crashworthiness and economic maintenance of China Insurance Automotive Safety Index (C-IASI). And machine learning-random forest and PAM are used to reduce the dimensionality of the independent variables to obtain the composite variables, after which the fitting effects of GLM and GAMLSS with the addition of machine learning factors are compared. In this paper, we use measurement results of C-IASI, multi-parameter GAMLSS, random forest and PAM to increase the accuracy of auto insurance pricing.
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