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What can we learn from telematics car driving data: A survey

Author

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  • Gao, Guangyuan
  • Meng, Shengwang
  • Wüthrich, Mario V.

Abstract

We give a survey on the field of telematics car driving data research in actuarial science. We describe and discuss telematics car driving data, we illustrate the difficulties of telematics data cleaning, and we highlight the transparency issue of telematics car driving data resulting in associated privacy concerns. Transparency of telematics data is demonstrated by aiming at correctly allocating different car driving trips to the right drivers. This is achieved rather successfully by a convolutional neural network that manages to discriminate different car drivers by their driving styles. In a last step, we describe two approaches of using telematics data for improving claims frequency prediction, one is based on telematics heatmaps and the other one on time series of individual trips, respectively.

Suggested Citation

  • Gao, Guangyuan & Meng, Shengwang & Wüthrich, Mario V., 2022. "What can we learn from telematics car driving data: A survey," Insurance: Mathematics and Economics, Elsevier, vol. 104(C), pages 185-199.
    • Handle: RePEc:eee:insuma:v:104:y:2022:i:c:p:185-199
    DOI: 10.1016/j.insmatheco.2022.02.004
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    Citations

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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. Ghaffarpasand, Omid & Pope, Francis D., 2024. "Telematics data for geospatial and temporal mapping of urban mobility: New insights into travel characteristics and vehicle specific power," Journal of Transport Geography, Elsevier, vol. 115(C).
    3. Simon, Pierre-Alexandre & Trufin, Julien & Denuit, Michel, 2023. "Bivariate Poisson credibility model and bonus-malus scale for claim and near-claim events," LIDAM Discussion Papers ISBA 2023014, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. 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.

    More about this item

    Keywords

    Telematics car driving data; Heatmaps; Poisson regression models; Convolutional neural networks; Limited fluctuation credibility model;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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