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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 ,
He Wang ,
Yanlin Shi  and
Guangyuan Gao
Shengwang Meng
Affiliation:
Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China, 100872, E-Mail: [email protected]
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: [email protected]
Yanlin Shi
Affiliation:
Department of Actuarial Studies and Business Analytics, Macquarie University, North Ryde2109, Australia, E-Mail: [email protected]
Guangyuan Gao*
Affiliation:
Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China, 100872 E-Mail: [email protected]
*
E-Mail: [email protected]
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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.

Keywords

Telematics car driving dataautomobile insurance pricingone-dimensional convolutional neural networklimited fluctuation credibility modelgeneralized linear modelclaims frequencyG22C45
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 52 , Issue 2 , May 2022 , pp. 363 - 391
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The International Actuarial Association

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