Improving automobile insurance claims frequency prediction with telematics car driving data

Shengwang Meng, He Wang, Yanlin Shi, Guangyuan Gao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)363-391
Number of pages29
JournalASTIN Bulletin
Issue number2
Early online date27 Dec 2021
Publication statusPublished - May 2022


  • Telematics car driving data
  • automobile insurance pricing
  • claims frequency
  • generalized linear model
  • limited fluctuation credibility model
  • one-dimensional convolutional neural network


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