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

Department of Actuarial Mathematics and Statistics, School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, 62200 Putrajaya, Wilayah Persekutuan Putrajaya, Malaysia
Risks 2019, 7(3), 71; https://doi.org/10.3390/risks7030071
Received: 27 May 2019 / Revised: 17 June 2019 / Accepted: 19 June 2019 / Published: 30 June 2019
(This article belongs to the Special Issue Machine Learning in Insurance)
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. View Full-Text
Keywords: validation; generalised linear modelling; zero-inflated poisson model; telematics validation; generalised linear modelling; zero-inflated poisson model; telematics
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Qazvini, M. On the Validation of Claims with Excess Zeros in Liability Insurance: A Comparative Study. Risks 2019, 7, 71.

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