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Black boxes and market efficiency: the effect on premiums in the Italian motor-vehicle insurance market

Author

Listed:
  • Donatella Porrini

    (Università del Salento)

  • Giulio Fusco

    (Università del Salento)

  • Cosimo Magazzino

    (Università Roma Tre)

Abstract

The paper addresses the research question of whether black boxes affect the market efficiency, particularly by reducing the level of premiums. The case analyzed is the Italian motor-vehicle insurance market, characterized by the greatest amount of black boxes in the world as a consequence of regulatory interventions that fostered the spread of these kinds of devices. Particularly, using the data provided by the Italian Insurance Authority (IVASS), we show a specific relation between the increasing number of these devices and the decreasing trend in average premium. Conclusive remarks outline that in the near future this efficiency effect may increase because of the specific use of information derived from the black box that reveals the behaviors of the drivers and allows for innovative ways of individually profiling the insurance policies.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:ejlwec:v:49:y:2020:i:3:d:10.1007_s10657-020-09657-3
    DOI: 10.1007/s10657-020-09657-3
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    References listed on IDEAS

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    1. Roel Verbelen & Katrien Antonio & Gerda Claeskens, 2018. "Unravelling the predictive power of telematics data in car insurance pricing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1275-1304, November.
    2. Mercedes Ayuso & Montserrat Guillen & Jens Perch Nielsen, 2019. "Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data," Transportation, Springer, vol. 46(3), pages 735-752, June.
    3. 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.
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    5. Donatella Porrini, 2015. "Risk Classification Efficiency and the Insurance Market Regulation," Risks, MDPI, vol. 3(4), pages 1-10, September.
    6. Hultkrantz, Lars & Nilsson, Jan-Eric & Arvidsson, Sara, 2012. "Voluntary internalization of speeding externalities with vehicle insurance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(6), pages 926-937.
    7. Paefgen, Johannes & Staake, Thorsten & Fleisch, Elgar, 2014. "Multivariate exposure modeling of accident risk: Insights from Pay-as-you-drive insurance data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 27-40.
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    Citations

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    Cited by:

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    3. 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).
    4. Martin Eling & Ruo Jia & Jieyu Lin & Casey Rothschild, 2022. "Technology heterogeneity and market structure," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(2), pages 427-448, June.

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    More about this item

    Keywords

    Black box; Insurance market; Risk classification;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • K20 - Law and Economics - - Regulation and Business Law - - - General
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation

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