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Improving Automobile Insurance Claims Frequency Prediction With Telematics Car Driving Data

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

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.

Suggested Citation

  • Meng, Shengwang & Wang, He & Shi, Yanlin & Gao, Guangyuan, 2022. "Improving Automobile Insurance Claims Frequency Prediction With Telematics Car Driving Data," ASTIN Bulletin, Cambridge University Press, vol. 52(2), pages 363-391, May.
    • Handle: RePEc:cup:astinb:v:52:y:2022:i:2:p:363-391_1
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    Cited by:

    1. Freek Holvoet & Katrien Antonio & Roel Henckaerts, 2023. "Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff," Papers 2310.12671, arXiv.org, revised Aug 2024.
    2. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.

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