Abstract
With the support of Internet of Vehicles technology, UBI (Usage Based Insurance) car insurance rate determination has certain guiding significance for achieving accurate pricing of car insurance rates and satisfying the personalized needs of users. Based on the CNN (Convolutional Neural Networks) algorithm and SVM (Support Vector Machine) algorithm, this paper establishes a rating model for UBI car insurance rates. The model first performs a series of operations such as convolutions, pooling and nonlinear activation function mapping using the CNN algorithm so that it can extract the features from the driving behavior data of UBI customers. Then, it introduces the Hull Vector to optimize the operating efficiency of the SVM algorithm. The HVSVM (Hull Vector Support Vector Machine) algorithm classifies customers according to their driving behavior, and thus obtains UBI customer car insurance rate grades. Therefore, this paper proposes a UBI car insurance rate grade determination model based on the CNN-HVSVM algorithm. The empirical results of the model show that the CNN-HVSVM algorithm has higher discrimination accuracy in the risk rating process of UBI customer driving behavior than the CNN algorithm, BP neural network algorithm and SVM algorithm; and when dealing with large training sets, it has a speed advantage over the CNN-SVM algorithm. Furthermore, it is easy to realize in the process of establishing the UBI car insurance rate determination model and it has good robustness, which can adapt to diverse data sets, thus achieving better results in the car insurance rate determination process. Therefore, the CNN-HVSVM model can predict the grade of UBI car insurance users more accurately and efficiently, and the prediction results are more consistent with the actual situation, which has strong applicability and flexibility. The UBI car insurance premium rate determination model based on the CNN-HVSVM algorithm can determine driver behavior more fairly and reasonably, and has certain practical significance for promoting car insurance rate market reform, which can better promote future UBI research work.
Highlights
INTRODUCTIONWith the progress of the automobile industry, China’s total car volume is expected to become the first in the world by 2020, which will form a large car insurance and related
With the progress of the automobile industry, China’s total car volume is expected to become the first in the world by 2020, which will form a large car insurance and relatedThe associate editor coordinating the review of this manuscript and approving it for publication was Aysegul Ucar .industry market [1]
Yan et al.: Research on the UBI Car Insurance Rate Determination Model Based on the convolutional neural network (CNN)-HVSVM Algorithm insurance is car-based, which mainly calculates the basic premium price of the vehicle through attributes such as vehicle age, vehicle use, purchase price, etc., and gives the customer a corresponding discount according to the number of vehicles insured
Summary
With the progress of the automobile industry, China’s total car volume is expected to become the first in the world by 2020, which will form a large car insurance and related. Chun Yan (2019) established a UBI car insurance rate determination model based on the K-S algorithm by combining the KNN algorithm and the SVM algorithm This model constructs the corresponding relationship between a driver’s behavior grade and vehicle insurance rate, and obtains the corresponding adjustment coefficient of their insurance rate according to the driving behavior grade. This paper uses the CNN algorithm for feature extraction, introduces the Hull Vector to optimize the operating efficiency of the SVM algorithm and combines the two algorithms to establish the CNN-HVSVM algorithm, which can be used to solve the problem of determining the driving behavior risk level of UBI customers. The BP neural network algorithm, CNN algorithm, SVM algorithm and CNN-SVM algorithm are introduced in this paper and are compared with the CNN-HVSVM algorithm, highlighting the superiority of the CNN-HVSVM algorithm in UBI vehicle insurance rate level determination, and making the determination result of the model more convincing
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