Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm

With the support of vehicular networking technology, the rating of UBI auto insurance rate has a certain guiding significance for accurate pricing of the rate and personalized demand. Based on CNN-Softmax algorithm, a rating model of UBI auto insurance rate is proposed in this paper. The model first performs a series of operations such as convolution, pooling, and non-linear activation function mapping on CNN algorithm to enable it to extract features based on UBI customers' driving behavior data, and then uses Softmax algorithm to classify customers according to their driving behaviors, thereby obtain UBI customer's auto insurance rate rating. The empirical results show that compared with, CNN algorith, BP neural network algorithm and SVM algorithm, CNN-Softmax algorithm has a higher discrimination accuracy in the risk assessment of UBI customers' driving behavior. And it is easy to implement in establishing rating model of UBI auto insurance rate. What's more, it also has a good robustness, which can achieve better grade evaluations.