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

被引：3
作者：
Kanta Reddy T.M. [1 ,2 ]
Premamayudu B. [2 ]
机构：
[1] Department of IT, VFSTR Deemed to be University, Vadlamudi, Guntur, 522017, Andhra Pradesh
[2] VFSTR Deemed to be University, Vadlamudi, Guntur, 522017, Andhra Pradesh
来源：
Ing. Syst. Inf. | 2019年 / 5卷 / 507-512期
关键词：
Block chain; Drivers driving conduct; Machine learning approach; Motor insurance; Premium calculation;
D O I：
10.18280/isi.240507
中图分类号：
学科分类号：
摘要：
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. © 2019 International Information and Engineering Technology Association. All rights reserved.
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页码：507 / 512
页数：5
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