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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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    4. 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.
    5. 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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    8. Yip, Karen C.H. & Yau, Kelvin K.W., 2005. "On modeling claim frequency data in general insurance with extra zeros," Insurance: Mathematics and Economics, Elsevier, vol. 36(2), pages 153-163, April.
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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. Christopher Grumiau & Mina Mostoufi & Solon Pavlioglou & Tim Verdonck, 2020. "Address Identification Using Telematics: An Algorithm to Identify Dwell Locations," Risks, MDPI, vol. 8(3), pages 1-12, September.
    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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