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IMPROVING AUTOMOBILE INSURANCE CLAIMS FREQUENCY PREDICTION WITH TELEMATICS CAR DRIVING DATA

Published online by Cambridge University Press:  27 December 2021

Shengwang Meng
Affiliation:
Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China, 100872, E-Mail: mengshw@ruc.edu.cn
He Wang
Affiliation:
Department of Finance, Shenzhen Humanities & Social Sciences Key Research Bases and Ying Shang Nan Ke Actuarial Science Center, Southern University of Science and Techology, Shenzhen, China, 518055, E-Mail: wangh9@sustc.edu.cn
Yanlin Shi
Affiliation:
Department of Actuarial Studies and Business Analytics, Macquarie University, North Ryde2109, Australia, E-Mail: yanlin.shi@mq.edu.au
Guangyuan Gao*
Affiliation:
Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China, 100872 E-Mail: guangyuan.gao@ruc.edu.cn

Abstract

Novel navigation applications provide a driving behavior score for each finished trip to promote safe driving, which is mainly based on experts’ domain knowledge. In this paper, with automobile insurance claims data and associated telematics car driving data, we propose a supervised driving risk scoring neural network model. This one-dimensional convolutional neural network takes time series of individual car driving trips as input and returns a risk score in the unit range of (0,1). By incorporating credibility average risk score of each driver, the classical Poisson generalized linear model for automobile insurance claims frequency prediction can be improved significantly. Hence, compared with non-telematics-based insurers, telematics-based insurers can discover more heterogeneity in their portfolio and attract safer drivers with premiums discounts.

Type
Research Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The International Actuarial Association

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