Vehicle insurance model using telematics system with improved machine learning techniques: A survey

Abstract

In the field of vehicle insurance, the current models for premium calculation and charging are not friendly to end users. Many important parameters are not considered in these models, such as mileage, driving conduct and road type. This paper designs a vehicle insurance model based on telematics system and machine learning. With telematics system as the foundation, the key of the model construction is to select a suitable algorithm to assess the driving style of the driver, which is an important influencing factor of the car crash likelihood. Deep learning techniques like artificial neural network (ANN), Bayesian network and fuzzy logic were examined, and combined with block chain technology to improve the premium calculation based on driving style. The effectiveness of the established model was fully analyzed, providing a novel angle to premium calculation of vehicle insurance.