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Near‐miss telematics in motor insurance

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  • Montserrat Guillen
  • Jens Perch Nielsen
  • Ana M. Pérez‐Marín

Abstract

We present a method to integrate telematics data in a pay‐how‐you‐drive insurance pricing scheme that penalizes some near‐miss events. We illustrate our method with a sample of drivers for whom information on near‐miss events and claims frequency records are available. We discuss the implications for motor insurance ratemaking. Our pricing principle is to combine a baseline insurance premium with added extra charges for near‐miss events indicating risky driving (or discounts) that can be updated on a weekly basis. This procedure provides an incentive for safe driving. In our real‐case study illustration, hard‐braking and acceleration events as well as smartphone use while driving increase the cost of insurance.

Suggested Citation

  • Montserrat Guillen & Jens Perch Nielsen & Ana M. Pérez‐Marín, 2021. "Near‐miss telematics in motor insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 569-589, September.
    • Handle: RePEc:bla:jrinsu:v:88:y:2021:i:3:p:569-589
    DOI: 10.1111/jori.12340
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    References listed on IDEAS

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    1. Guangyuan Gao & Mario V. Wüthrich, 2019. "Convolutional Neural Network Classification of Telematics Car Driving Data," Risks, MDPI, vol. 7(1), pages 1-18, January.
    2. Chiang Ku Fan & Wei-Yuan Wang, 2017. "A Comparison of Underwriting Decision Making Between Telematics-Enabled UBI and Traditional Auto Insurance," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 7(1), pages 1-2.
    3. Martin Eling & Mirko Kraft, 2020. "The impact of telematics on the insurability of risks," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 21(2), pages 77-109, April.
    4. Jean-Philippe Boucher & Roxane Turcotte, 2020. "A Longitudinal Analysis of the Impact of Distance Driven on the Probability of Car Accidents," Risks, MDPI, vol. 8(3), pages 1-19, September.
    5. Denuit, Michel & Guillen, Montserrat & Trufin, Julien, 2019. "Multivariate credibility modelling for usage-based motor insurance pricing with behavioural data," Annals of Actuarial Science, Cambridge University Press, vol. 13(2), pages 378-399, September.
    6. Denuit, Michel & Guillen, Montserrat & Trufin, Julien, 2019. "Multivariate credibility modelling for usage-based motor insurance pricing with behavioural data," LIDAM Reprints ISBA 2019039, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Alois Geyer & Daniela Kremslehner & Alexander Muermann, 2020. "Asymmetric Information in Automobile Insurance: Evidence From Driving Behavior," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 87(4), pages 969-995, December.
    8. Lemaire, Jean & Park, Sojung Carol & Wang, Kili C., 2016. "The Use Of Annual Mileage As A Rating Variable," ASTIN Bulletin, Cambridge University Press, vol. 46(1), pages 39-69, January.
    9. Montserrat Guillen & Jens Perch Nielsen & Ana M. Pérez-Marín & Valandis Elpidorou, 2020. "Can Automobile Insurance Telematics Predict the Risk of Near-Miss Events?," North American Actuarial Journal, Taylor & Francis Journals, vol. 24(1), pages 141-152, January.
    10. Montserrat Guillen & Jens Perch Nielsen & Mercedes Ayuso & Ana M. Pérez‐Marín, 2019. "The Use of Telematics Devices to Improve Automobile Insurance Rates," Risk Analysis, John Wiley & Sons, vol. 39(3), pages 662-672, March.
    11. Ma, Yu-Luen & Zhu, Xiaoyu & Hu, Xianbiao & Chiu, Yi-Chang, 2018. "The use of context-sensitive insurance telematics data in auto insurance rate making," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 243-258.
    12. Jean-Philippe Boucher & Steven Côté & Montserrat Guillen, 2017. "Exposure as Duration and Distance in Telematics Motor Insurance Using Generalized Additive Models," Risks, MDPI, vol. 5(4), pages 1-23, September.
    13. Gao, Guangyuan & Wüthrich, Mario V. & Yang, Hanfang, 2019. "Evaluation of driving risk at different speeds," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 108-119.
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    Cited by:

    1. 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).
    2. 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.
    3. Daniel Bauer & James Tyler Leverty & Joan Schmit & Justin Sydnor, 2021. "Symposium on insure‐tech, digitalization, and big‐data techniques in risk management and insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 525-528, September.
    4. Zhiyu Quan & Changyue Hu & Panyi Dong & Emiliano A. Valdez, 2024. "Improving Business Insurance Loss Models by Leveraging InsurTech Innovation," Papers 2401.16723, arXiv.org.

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