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The use of context-sensitive insurance telematics data in auto insurance rate making

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  • Ma, Yu-Luen
  • Zhu, Xiaoyu
  • Hu, Xianbiao
  • Chiu, Yi-Chang

Abstract

Historically, auto insurers use various socio-demographic underwriting factors to differentiate driver risks. With the invention of GPS devices, information such as mileage, traffic condition and driving habits can be incorporated into auto insurance premium calculation. Several major auto insurance companies have offered usage based insurance (UBI) programs where auto insurance premiums are sensitive to actual GPS readings combined with driver’s driving behavior. However, given that telematics data are proprietary to the insurance companies that collect such data, the accessibility of UBI is extremely limited.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transa:v:113:y:2018:i:c:p:243-258
    DOI: 10.1016/j.tra.2018.04.013
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. Jiamin Yu, 2022. "Will claim history become a deprecated rating factor? An optimal design method for the real-time road risk model," Papers 2204.11585, arXiv.org.
    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. Alfiero, Simona & Battisti, Enrico & Ηadjielias, Elias, 2022. "Black box technology, usage-based insurance, and prediction of purchase behavior: Evidence from the auto insurance sector," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    6. Wadee Alhalabi & Miltiadis Lytras & Nada Aljohani, 2021. "Crowdsourcing Research for Social Insights into Smart Cities Applications and Services," Sustainability, MDPI, vol. 13(14), pages 1-27, July.
    7. Shengkun Xie & Anna T. Lawniczak, 2018. "Estimating Major Risk Factor Relativities in Rate Filings Using Generalized Linear Models," IJFS, MDPI, vol. 6(4), pages 1-14, October.
    8. Donatella Porrini & Giulio Fusco & Cosimo Magazzino, 2020. "Black boxes and market efficiency: the effect on premiums in the Italian motor-vehicle insurance market," European Journal of Law and Economics, Springer, vol. 49(3), pages 455-472, June.
    9. Shengkun Xie & Kun Shi, 2023. "Generalised Additive Modelling of Auto Insurance Data with Territory Design: A Rate Regulation Perspective," Mathematics, MDPI, vol. 11(2), pages 1-24, January.
    10. Omid Ghaffarpasand & Mark Burke & Louisa K. Osei & Helen Ursell & Sam Chapman & Francis D. Pope, 2022. "Vehicle Telematics for Safer, Cleaner and More Sustainable Urban Transport: A Review," Sustainability, MDPI, vol. 14(24), pages 1-20, December.

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