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On the Validation of Claims with Excess Zeros in Liability Insurance: A Comparative Study

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  • Marjan Qazvini

    (Department of Actuarial Mathematics and Statistics, School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, 62200 Putrajaya, Wilayah Persekutuan Putrajaya, Malaysia)

Abstract

In this study, we consider the problem of zero claims in a liability insurance portfolio and compare the predictability of three models. We use French motor third party liability (MTPL) insurance data, which has been used for a pricing game, and show that how the type of coverage and policyholders’ willingness to subscribe to insurance pricing, based on telematics data, affects their driving behaviour and hence their claims. Using our validation set, we then predict the number of zero claims. Our results show that although a zero-inflated Poisson (ZIP) model performs better than a Poisson regression, it can even be outperformed by logistic regression.

Suggested Citation

  • Marjan Qazvini, 2019. "On the Validation of Claims with Excess Zeros in Liability Insurance: A Comparative Study," Risks, MDPI, vol. 7(3), pages 1-17, June.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:3:p:71-:d:244533
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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.
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    5. 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.
    6. Jean-Philippe Boucher & Michel Denuit & Montserrat Guillén, 2007. "Risk Classification for Claim Counts," North American Actuarial Journal, Taylor & Francis Journals, vol. 11(4), pages 110-131.
    7. Shrabanti Chowdhury & Saptarshi Chatterjee & Himel Mallick & Prithish Banerjee & Broti Garai, 2019. "Group regularization for zero-inflated poisson regression models with an application to insurance ratemaking," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(9), pages 1567-1581, July.
    8. Jean‐Philippe Boucher & Michel Denuit & Montserrat Guillen, 2009. "Number of Accidents or Number of Claims? An Approach with Zero‐Inflated Poisson Models for Panel Data," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(4), pages 821-846, December.
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    Cited by:

    1. Vali Asimit & Ioannis Kyriakou & Jens Perch Nielsen, 2020. "Special Issue “Machine Learning in Insurance”," Risks, MDPI, vol. 8(2), pages 1-2, May.
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    3. 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.
    4. Thomas Poufinas & Periklis Gogas & Theophilos Papadimitriou & Emmanouil Zaganidis, 2023. "Machine Learning in Forecasting Motor Insurance Claims," Risks, MDPI, vol. 11(9), pages 1-19, September.

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