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JOINT MODELING OF CLAIM FREQUENCIES AND BEHAVIORAL SIGNALS IN MOTOR INSURANCE

Published online by Cambridge University Press:  07 October 2021

Alexandre Corradin ,
Michel Denuit ,
Marcin Detyniecki ,
Vincent Grari ,
Matteo Sammarco  and
Julien Trufin Open the ORCID record for Julien Trufin [Opens in a new window]
Alexandre Corradin
Affiliation:
AXA/GO/REV, Paris, France
Michel Denuit
Affiliation:
Institute of Statistics, Biostatistics and Actuarial Science (ISBA/LIDAM), UCLouvain, Louvain-la-Neuve, Belgium
Marcin Detyniecki
Affiliation:
AXA/GO/REV, Paris, France
Vincent Grari
Affiliation:
AXA/GO/REV, Paris, France
Matteo Sammarco
Affiliation:
AXA/GO/REV, Paris, France
Julien Trufin*
Affiliation:
Department of Mathematics, Université Libre de Bruxelles (ULB), Bruxelles, Belgium E-Mail: julien.trufin@ulb.ac.be
*
E-Mail: julien.trufin@ulb.ac.be
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Abstract

Telematicsdevices installed in insured vehicles provide actuaries with new risk factors, such as the time of the day, average speeds, and other driving habits. This paper extends the multivariate mixed model describing the joint dynamics of telematics data and claim frequencies proposed by Denuit et al. (2019a) by allowing for signals with various formats, not necessarily integer-valued, and by replacing the estimation procedure with the Expected Conditional Maximization algorithm. A numerical study performed on a database related to Pay-How-You-Drive, or PHYD motor insurance illustrates the relevance of the proposed approach for practice.

Keywords

Risk classificationpremium calculationdriving behaviormixed modelsmultivariate credibilitypanel dataC13C33C38
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 52 , Issue 1 , January 2022 , pp. 33 - 54
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The International Actuarial Association

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References

Ayuso, M., Guillen, M., Perez-Marin, A. M. (2016). Telematics and gender discrimination: Some usage-based evidence on whether men’s risk of accidents differs from women’s. Risks 4, 1–10.CrossRefGoogle Scholar
Ayuso, M., Guillen, M., Nielsen, J.P. (2019). Improving automobile insurance ratemaking using telematics: Incorporating mileage and driver behaviour data. Transportation 46, 735–752.CrossRefGoogle Scholar
Boucher, J.-P., Denuit, M., Guillen, M. (2007). Risk classification for claim counts: A comparative analysis of various zero-inflated Mixed Poisson and Hurdle models. North American Actuarial Journal 11, 110–131.CrossRefGoogle Scholar
Boucher, J.-Ph., Denuit, M.(2008). Credibility premiums for the zero-inflated Poisson model and new hunger for bonus interpretation. Insurance: Mathematics and Economics 42, 727–735.Google Scholar
Boucher, J. P., Perez-Marin, A. M., Santolino, M. (2013). Pay-as-you-drive insurance: The effect of the kilometers on the risk of accident. Anales del Instituto de Actuarios EspaÑoles 19, 135–154.Google Scholar
Dempster, A. P., Laird, N. M., Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society – Series B: Methodological 39, 1–22.Google Scholar
Denuit, M., Guillen, M., Trufin, J. (2019a). Multivariate credibility modeling for usage-based motor insurance pricing with behavioral data. Annals of Actuarial Science 13, 378–399.CrossRefGoogle Scholar
Denuit, M., Marechal, X., Pitrebois, S., Walhin, J.-F. (2007). Actuarial Modelling of Claim Counts: Risk Classification, Credibility and Bonus-Malus Systems. Wiley, New York.CrossRefGoogle Scholar
Denuit, M., Hainaut, D., Trufin, J. (2019b). Effective Statistical Learning Methods for Actuaries Volume 1: GLM and Extensions Springer Actuarial Lecture Notes Series.CrossRefGoogle Scholar
Fung, T.C., Badescu, A.L., Lin, X.S. (2019). A class of mixture of experts models for general insurance: Application to correlated claim frequencies. ASTIN Bulletin 49, 647–688.CrossRefGoogle Scholar
Fung, T.C., Badescu, A.L., Lin, X.S. (2020). A new class of severity regression models with an application to IBNR prediction. North American Actuarial Journal, in press.Google Scholar
Gao, G., Wang, H., Wüthrich, M.V. (2021). Boosting Poisson regression models with telematics car driving data. Machine Learning, 1–30.CrossRefGoogle Scholar
Guillen, M., Nielsen, J.P., Ayuso, M., Perez-Marin, A.M. (2019). The use of telematics devices to improve automobile insurance rates. Risk Analysis 39, 662–672.CrossRefGoogle ScholarPubMed
Grari, V., Lamprier, S., Detyniecki, M. (2020). Fairness-Aware Neural RÉnyi Minimization for Continuous Features. Proceedings of the 29th International Joint Conference on Artificial Intelligence IJCAI-20, 2262–2268.CrossRefGoogle Scholar
Meng, X.L., Rubin, D.B. (1993). Maximum likelihood estimation via the ECM algorithm: A general framework. Biometrika 80, 267–278.CrossRefGoogle Scholar
Ortiz, F.M., Sammarco, M., Costa, L.H.M.K., Detyniecki, M. (2020). Vehicle telematics via exteroceptive sensors: A survey. Available from https://arxiv.org/abs/2008.12632 Google Scholar
Pechon, F., Trufin, J., Denuit, M. (2018). Multivariate modelling of household claim frequencies in motor third-party liability insurance. ASTIN Bulletin 48, 969–993.CrossRefGoogle Scholar
Pechon, F., Denuit, M., Trufin, J. (2019). Multivariate modelling of multiple guarantees in motor insurance of a household. European Actuarial Journal 9, 575–602.CrossRefGoogle Scholar
Pechon, F., Denuit, M., Trufin, J. (2021). Home and Motor insurance joined at a household level using multivariate credibility. Annals of Actuarial Science 15, 82–114.CrossRefGoogle Scholar
Tselentis, D. I., Yannis, G., Vlahogianni, E. I. (2016). Innovative insurance schemes: Pay as/how you drive. Transportation Research Procedia 14, 362–371.CrossRefGoogle Scholar
Varadhan, R., Roland, C. (2008). Simple and globally convergent methods for accelerating the convergence of any EM algorithm. Scandinavian Journal of Statistics 35, 335–353.CrossRefGoogle Scholar
Wahlstrom, J., Skog, I., Handel, P. (2015). Driving behavior analysis for smartphone-based insurance telematics. Proceedings of the 2nd Workshop on Physical Analytics WPA ’15, 19–24.CrossRefGoogle Scholar