UBI Rating Model Based On Driving Behavior Classification

China has a huge auto insurance market,but the profit of the industry is not satisfactory.The main reason for this situation is that China's auto insurance pricing only considered the car factor,ignoring the driver's driving behavior differences.UBI(Usage Based Insurance)is an emerging auto insurance model of Eur opean and American countries,which determine the premium according to the driver's driving behavior safety level to restrict the driver unsafe driving behavior,reduce the accident rate and claims.Under environment of the rating policy reform of our country,it is of great theoretical and practical significance to study the theory and method of UBI rating based on China's new rating policy.Therefore,based on the present situation of UBI research and application,th is paper puts forward the UBI rating model based on driving behavior classification.Firstly,the classification algorithm is used to establish the relationship between driving behavior and accident risk,and then the UBI coefficient is determined according to the classification result.Next,the UBI coefficient is introduced into the premium calculation formula underwriting new rating policy.A UBI rating model based on driving behavior classification for our country is established.The main contents of the paper are as follows:(1)Established UBI rating model based on the driving behavior classification.Firstly,the paper introduces the theoretical basis of the auto insurance rating,consist of the rating factor and the rating method.The follow analysis points out the shortcomings of the existing UBI rating theory,that is,the objective evaluation of driving behavior can't be carried out.So the idea of using the classificati on technology of data mining replace driving behavior scoring system which relying on the subjective weighting method to evaluate the driving behavior was pro posed.Then the UBI rating model based on the driving behavior classification u nder the new rating reform policy was established,and the premium calculation formula was determined.Finally,an example shows that the driving behavior classification model is superior to the driving behavior score model for the identification of accident risk,which can make UBI rate more scientific and reasonable.(2)Determined the driving behavior classification model according to the telematics data.The actual driving behavior data and the risk data of 400 drivers are used as experimental data.Firstly,the feature selection is performed using the ExtraTrees algorithm,and then trained the Decision tree,naive Bayes,k-NN,neural network,SVM classifier use selected features.According to the accuracy of the training set and the test set and the confusion matrix and the ROC on the test set,the driving behavior classification model based on SVM is determined.Then the model parameters was optimized by PSO algorithm and GA algorithm.