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Home and Motor insurance joined at a household level using multivariate credibility

Published online by Cambridge University Press:  06 July 2020

Florian Pechon*
Affiliation:
Institute of Statistics, Biostatistics and Actuarial Science Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium
Michel Denuit
Affiliation:
Institute of Statistics, Biostatistics and Actuarial Science Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium
Julien Trufin
Affiliation:
Department of Mathematics, Université Libre de Bruxelles (ULB), Bruxelles, Belgium
*
*Corresponding author. E-mail: [email protected]

Abstract

Actuarial ratemaking is usually performed at product and guarantee level, meaning that each product and guarantee is considered in isolation. Moreover, independence between policyholders is generally assumed. In this paper, we propose a multivariate Poisson mixture, with random effects correlated using a hierarchical structure, to accommodate for the dependence that may exist between unobserved risk factors across Home and Motor insurance and between policyholders from the same household. The hierarchical structure accounts for the fact that Home insurance covers the whole household, whereas Motor insurance policies are subscribed by specific policyholders within the household. The model allows to periodically correct the a priori expected claim frequencies using the reported number of claims in any of the considered products. Applications show that the impact of the number of claims reported in Motor insurance on the number of claims expected in Home insurance is larger than the other way around. Moreover, an out-of-sample analysis validates an improved predictive power. Also, the model allows to identify more rapidly the riskiest households.

Type
Paper
Copyright
© Institute and Faculty of Actuaries 2020

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References

Agresti, A. (2003). Categorical Data Analysis, vol. 482. John Wiley & Sons.Google Scholar
Antonio, K. & Zhang, Y. (2014). Nonlinear mixed models. In E.W. Frees, R.A. Derrig & G. Meyers (Eds.), Predictive Modeling Applications in Actuarial Science (pp. 398–424). International Series on Actuarial Science, vol. 1. Cambridge University Press.CrossRefGoogle Scholar
Antonio, K., Guillén, M., Pérez Martn, A.M., et al. (2010a). Multidimensional credibility: a Bayesian analysis of policyholders holding multiple policies, technical report, Amsterdam School of Economics Research Institute.Google Scholar
Antonio, K., Frees, E.W. & Valdez, E.A. (2010b). A multilevel analysis of intercompany claim counts. ASTIN Bulletin: The Journal of the IAA, 40(1), 151177.CrossRefGoogle Scholar
Bermúdez, L. (2009). A priori ratemaking using bivariate Poisson regression models. Insurance: Mathematics and Economics, 44(1), 135141.Google Scholar
Bermúdez, L., Guillén, M. & Karlis, D. (2018). Allowing for time and cross dependence assumptions between claim counts in ratemaking models. Insurance: Mathematics and Economics, 83, 161169.Google Scholar
Bermúdez, L. & Karlis, D. (2011). Bayesian multivariate Poisson models for insurance ratemaking. Insurance: Mathematics and Economics, 48(2), 226236.Google Scholar
Bermúdez, L. & Karlis, D. (2017). A posteriori ratemaking using bivariate Poisson models. Scandinavian Actuarial Journal, 2017(2), 148158.CrossRefGoogle Scholar
Boucher, J.-P. & Inoussa, R. (2014). A posteriori ratemaking with panel data. ASTIN Bulletin, 44(3), 587612.CrossRefGoogle Scholar
Brockett, P.L., Golden, L.L., Guillen, M., Nielsen, J.P., Parner, J. & Perez-Marin, A.M. (2008). Survival analysis of a household portfolio of insurance policies: how much time do you have to stop total customer defection? Journal of Risk and Insurance, 75(3), 713737.CrossRefGoogle Scholar
Denuit, M., Maréchal, X., Pitrebois, S. & Walhin, J.-F. (2007). Actuarial Modelling of Claim Counts: Risk Classification, Credibility and Bonus-Malus Systems. John Wiley & Sons.CrossRefGoogle Scholar
Eddelbuettel, D. & François, R. (2011). Rcpp: Seamless R and C++ integration. Journal of Statistical Software, 40(8), 118.CrossRefGoogle Scholar
Frees, E.W. (2003). Multivariate credibility for aggregate loss models. North American Actuarial Journal, 7(1), 1337.CrossRefGoogle Scholar
Frees, E.W. & Valdez, E.A. (2008). Hierarchical insurance claims modeling. Journal of the American Statistical Association, 103(484), 14571469.CrossRefGoogle Scholar
Frees, E.W. & Wang, P. (2005). Credibility using copulas. North American Actuarial Journal, 9(2), 3148.CrossRefGoogle Scholar
Frees, E.W. & Wang, P. (2006). Copula credibility for aggregate loss models. Insurance: Mathematics and Economics, 38(2), 360373.Google Scholar
Frees, E.W., Shi, P. & Valdez, E.A. (2009). Actuarial applications of a hierarchical insurance claims model. ASTIN Bulletin, 39(1), 165197.CrossRefGoogle Scholar
Frees, E.W., Bolancé, C., Guillen, M. & Valdez, E. (2018). Copula modeling of multivariate longitudinal data with dropout. arXiv preprint arXiv:1810.04567.Google Scholar
Guillen, M., Nielsen, J.P. & Pérez-Marín, A.M. (2008). The need to monitor customer loyalty and business risk in the European insurance industry. The Geneva Papers on Risk and Insurance – Issues and Practice, 33(2), 207218.CrossRefGoogle Scholar
Henckaerts, R., Côté, M.-P., Antonio, K. & Verbelen, R. (2019). Boosting insights in insurance tariff plans with tree-based machine learning. arXiv preprint arXiv:1904.10890.Google Scholar
Jewell, W.S. (1974). Exact multidimensional credibility. Bulletin of Swiss Association of Actuaries, 74, 193214.Google Scholar
Karlis, D. & Pedeli, X. (2013). Flexible bivariate INAR (1) processes using copulas. Communications in Statistics-Theory and Methods, 42(4), 723740.CrossRefGoogle Scholar
Kroeze, K. (2016). Multighquad: Multidimensional Gauss-Hermite Quadrature. R package version 1.2.0.Google Scholar
Meng, X.-L. & Rubin, D.B. (1993). Maximum likelihood estimation via the ECM algorithm: a general framework. Biometrika, 80(2), 267278.CrossRefGoogle Scholar
Pechon, F., Trufin, J. & Denuit, M. (2018). Multivariate modelling of household claim frequencies in motor third-party liability insurance. ASTIN Bulletin, 48(3), 969993.CrossRefGoogle Scholar
Pechon, F., Denuit, M. & Trufin, J. (2019). Multivariate modelling of multiple guarantees in motor insurance of a household. European Actuarial Journal, 9(2), 575602.CrossRefGoogle Scholar
Pinquet, J. (1998). Designing optimal bonus-malus systems from different types of claims. ASTIN Bulletin, 28(2), 205–220.CrossRefGoogle Scholar
Purcaru, O. & Denuit, M. (2003). Dependence in dynamic claim frequency credibility models. ASTIN Bulletin, 33(1), 23–40.CrossRefGoogle Scholar
Self, S.G. & Liang, K.-Y. (1987). Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. Journal of the American Statistical Association, 82(398), 605610.CrossRefGoogle Scholar
Shi, Peng, & Valdez, Emiliano A. (2014). Multivariate negative binomial models for insurance claim counts. Insurance: Mathematics and Economics, 55(1), 1829.Google Scholar
Shi, P. & Yang, L. (2018). Pair copula constructions for insurance experience rating. Journal of the American Statistical Association, 113(521), 122133.CrossRefGoogle Scholar
Shi, P., Feng, X. & Boucher, J.-P. (2016). Multilevel modeling of insurance claims using copulas. The Annals of Applied Statistics, 10(2), 834863.CrossRefGoogle Scholar
Tuerlinckx, F., Rijmen, F., Verbeke, G. & De Boeck, P. (2006). Statistical inference in generalized linear mixed models: a review. British Journal of Mathematical and Statistical Psychology, 59(2), 225255.CrossRefGoogle ScholarPubMed
Wood, S.N. (2017). Generalized Additive Models: An Introduction with R, 2nd edition. Chapman and Hall/CRC.CrossRefGoogle Scholar
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