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EXOGENOUS AND ENDOGENOUS RISK FACTORS MANAGEMENT TO PREDICT SURRENDER BEHAVIOURS

Published online by Cambridge University Press:  11 July 2013

Xavier Milhaud
Xavier Milhaud*
Affiliation:
Actuarial Department, ENSAE ParisTech and CREST (LFA). Timbre J101, 92245 Malakoff Cedex, Paris, France. Tel.: +33 (0)1 41 17 58 09, E-Mail: xavier.milhaud@ensae.fr
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Abstract

Insurers have been concerned about surrenders for a long time especially in saving business, where huge sums are at stake. The emergence of the European directive Solvency II, which promotes the development of internal risk models (among which a complete unit is dedicated to surrender risk management), strengthens the necessity to deeply study and understand this risk. In this paper, we investigate the topics of segmenting and modelling surrenders in order to better take into account the main risk factors impacting policyholders' decisions. We find that several complex aspects must be specifically dealt with to predict surrenders, in particular the heterogeneity of behaviour as well as the context faced by the insured. Combining them, we develop a new methodology that seems to provide good results on given business lines, and that moreover can be adapted for other products with little effort.

Keywords

Risk managementlife insurancesurrender riskmixture models
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 43 , Issue 3 , September 2013 , pp. 373 - 398
Copyright
Copyright © ASTIN Bulletin 2013 

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References

Bacinello, A.R. (2005) Endogenous model of surrender conditions in equity-linked life insurance. Insurance: Mathematics and Economics, 37, 270–296.Google Scholar
Biard, R., Lefèvre, C. and Loisel, S. (2008) Impact of correlation crises in risk theory: Asymptotics of finite-time ruin probabilities for heavy-tailed claim amounts when some independence and stationarity assumptions are relaxed. Insurance: Mathematics and Economics, 43 (3), 412–421.Google Scholar
Bluhm, W.F. (1982) Cumulative antiselection theory. Transactions of Society of actuaries, 34, 215–231.Google Scholar
Breiman, L. (2001) Random forests. Machine Learning, 45 (1), 5–32.CrossRefGoogle Scholar
Breiman, L., Friedman, J., Olshen, R.A. and Stone, C.J. (1984) Classification and Regression Trees. Boca Raton: Chapman and Hall.Google Scholar
Cox, D. (1972) Regression models and life tables (with discussion). Journal of the Royal Statistical Society: Series B, 34 (2), 187–220.Google Scholar
Frühwirth-Schnatter, S. (2006) Finite Mixture and Markov Switching Models. Springer Series in Statistics. New York: Springer-Verlag.Google Scholar
Hosmer, D.W. and Lemeshow, S. (2000) Applied Logistic Regression, 2nd ed. Wiley Series In Probability and Statistics. New York: Wiley.CrossRefGoogle Scholar
Kagraoka, Y. (2005) Modeling insurance surrenders by the negative binomial model. Working Paper 2005, Musashi University, Japan.Google Scholar
Kim, C. (2005) Modeling surrender and lapse rates with economic variables. North American Actuarial Journal, 9, 56–70.CrossRefGoogle Scholar
Loisel, S. and Milhaud, X. (2011) From deterministic to stochastic surrender risk models: Impact of correlation crises on economic capital. European Journal of Operational Research, 214 (2), 348–357.CrossRefGoogle Scholar
McCullagh, P. and Nelder, J.A. (1989) Generalized linear models, 2nd ed. Monographs on Statistics and Applied Probability. London: Chapman and Hall.CrossRefGoogle Scholar
McLachlan, G. and Peel, D. (2000) Finite Mixture Models. Wiley Series In Probability and Statistics. New York: Wiley.CrossRefGoogle Scholar
Milhaud, X. (2011) Segmentation et modélisation des comportements de rachat en assurance-vie. Master's thesis, ISFA. Actuary memoir.Google Scholar
Milhaud, X., Maume-Deschamps, V. and Loisel, S. (2011) Surrender triggers in life insurance: What main features affect the surrender behavior in a classical economic context? Bulletin Francais d'Actuariat, 22, 5–48.Google Scholar
Schwarz, G. (1978) Estimating the dimension of a model. Annals of Statistics, 6, 461–464.CrossRefGoogle Scholar
Siu, T.K. (2005) Fair valuation of participating policies with surrender options and regime switching. Insurance: Mathematics and Economics, 37, 533–552.Google Scholar
Tsai, C., Kuo, W. and Chen, W.-K. (2002) Early surrender and the distribution of policy reserves. Insurance: Mathematics and Economics, 31, 429–445.Google Scholar