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Telematic driving profile classification in car insurance pricing

Author

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  • Weidner, Wiltrud
  • Transchel, Fabian W.G.
  • Weidner, Robert

Abstract

This paper presents pricing innovations to German car insurance. The purpose is to provide an effective approach to adapting actuarial pricing decision to incorporate telematic data, which differs substantially from established tariff criteria in complexity and volume. A vehicle mobility model and a real-world sample of driving profiles form the input into the analysis. We propose an allocation of the driving profiles based on velocity and acceleration parameters to specific driving styles for evaluating the driving behaviour to subsequently enable discounts or surcharges on the premiums to obtain usage-based insurance premiums. The result is highly relevant for actuaries, who calculate the tariffs, but also for managers, as they have to make a pricing decision.

Suggested Citation

  • Weidner, Wiltrud & Transchel, Fabian W.G. & Weidner, Robert, 2017. "Telematic driving profile classification in car insurance pricing," Annals of Actuarial Science, Cambridge University Press, vol. 11(2), pages 213-236, September.
    • Handle: RePEc:cup:anacsi:v:11:y:2017:i:02:p:213-236_00
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    Citations

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    Cited by:

    1. Christopher Blier-Wong & Hélène Cossette & Luc Lamontagne & Etienne Marceau, 2020. "Machine Learning in P&C Insurance: A Review for Pricing and Reserving," Risks, MDPI, vol. 9(1), pages 1-26, December.
    2. Etye Steinberg, 2022. "Run for Your Life: The Ethics of Behavioral Tracking in Insurance," Journal of Business Ethics, Springer, vol. 179(3), pages 665-682, September.
    3. Meng, Shengwang & Gao, Yaqian & Huang, Yifan, 2022. "Actuarial intelligence in auto insurance: Claim frequency modeling with driving behavior features and improved boosted trees," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 115-127.
    4. Aristodemos Pnevmatikakis & Stathis Kanavos & George Matikas & Konstantina Kostopoulou & Alfredo Cesario & Sofoklis Kyriazakos, 2021. "Risk Assessment for Personalized Health Insurance Based on Real-World Data," Risks, MDPI, vol. 9(3), pages 1-15, March.
    5. Zhiyu Quan & Changyue Hu & Panyi Dong & Emiliano A. Valdez, 2024. "Improving Business Insurance Loss Models by Leveraging InsurTech Innovation," Papers 2401.16723, arXiv.org.
    6. Angela Zeier Röschmann & Matthias Erny & Joël Wagner, 2022. "On the (future) role of on-demand insurance: market landscape, business model and customer perception," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(3), pages 603-642, July.
    7. Darren Shannon & Tim Jannusch & Florian David‐Spickermann & Martin Mullins & Martin Cunneen & Finbarr Murphy, 2021. "Connected and autonomous vehicle injury loss events: Potential risk and actuarial considerations for primary insurers," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 24(1), pages 5-35, March.
    8. Guangyuan Gao & Mario V. Wüthrich, 2019. "Convolutional Neural Network Classification of Telematics Car Driving Data," Risks, MDPI, vol. 7(1), pages 1-18, January.
    9. Denuit, Michel & Guillen, Montserrat & Trufin, Julien, 2018. "Multivariate credibility modeling for usage-based motor insurance pricing with behavioral data," LIDAM Discussion Papers ISBA 2018032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

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