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


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
Number of pages29
JournalASTIN Bulletin
Publication statusE-pub ahead of print - 27 Dec 2021


  • Keywords:
  • 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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