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Driving to a future without accidents? Connected automated vehicles’ impact on accident frequency and motor insurance risk

Author

Listed:
  • Fabian Pütz

    (University of Limerick
    TH Köln University of Applied Sciences)

  • Finbarr Murphy

    (University of Limerick)

  • Martin Mullins

    (University of Limerick)

Abstract

Road traffic accidents are largely driven by human error; therefore, the development of connected automated vehicles (CAV) is expected to significantly reduce accident risk. However, these changes are by no means proven and linear as different levels of automation show risk-related idiosyncrasies. A lack of empirical data aggravates the transparent evaluation of risk arising from CAVs with higher levels of automation capability. Nevertheless, it is likely that the risks associated with CAV will profoundly reshape the risk profile of the global motor insurance industry. This paper conducts a deep qualitative analysis of the impact of progressive vehicle automation and interconnectedness on the risks covered under motor third-party and comprehensive insurance policies. This analysis is enhanced by an assessment of potential emerging risks such as the risk of cyber-attacks. We find that, in particular, primary insurers focusing on private retail motor insurance face significant strategic risks to their business model. The results of this analysis are not only relevant for insurance but also from a regulatory perspective as we find a symbiotic relationship between an insurance-related assessment and a comprehensive evaluation of CAV’s inherent societal costs.

Suggested Citation

  • Fabian Pütz & Finbarr Murphy & Martin Mullins, 2019. "Driving to a future without accidents? Connected automated vehicles’ impact on accident frequency and motor insurance risk," Environment Systems and Decisions, Springer, vol. 39(4), pages 383-395, December.
    • Handle: RePEc:spr:envsyd:v:39:y:2019:i:4:d:10.1007_s10669-019-09739-x
    DOI: 10.1007/s10669-019-09739-x
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    References listed on IDEAS

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    Cited by:

    1. Darren Shannon & Grigorios Fountas, 2021. "Extending the Heston Model to Forecast Motor Vehicle Collision Rates," Papers 2104.11461, arXiv.org, revised May 2021.
    2. Wang, Fei & Zhang, Zhentai & Lin, Shoufu, 2023. "Purchase intention of Autonomous vehicles and industrial Policies: Evidence from a national survey in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    3. Zachary A. Collier & James H. Lambert & Igor Linkov, 2019. "Innovation of risk analytics for technology and society," Environment Systems and Decisions, Springer, vol. 39(4), pages 369-370, December.
    4. 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.

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