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Predicting Probability of Customer Churn in Insurance

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Modeling and Simulation in Engineering, Economics and Management (MS 2016)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 254))

Abstract

We focus on a real case of the motor insurance sector. We propose four different methods to predict lapsing from a portfolio of policies. We present a comparative analysis between three different performance measures in order to assess the predictive power of each model. Our comparison analyses the outcomes of a logistic regression, a conditional tree, a neural network and a support vector machine. These are all considered basic approaches to data mining and knowledge discovery. The main contribution of this paper is to show that, depending on the type of analysis and the objective of the researcher, the optimal prediction method may differ.

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References

  1. Zeithaml, V.A., Berry, L.L., Parasuraman, A.: The behavioral consequences of service quality. J. Mark. 60, 31–46 (1996)

    Article  Google Scholar 

  2. Fornell, C., Wernerfelt, B.: Defensive marketing strategy by customer complaint management: a theoretical analysis. J. Mark. Res. 24, 337–346 (1987)

    Article  Google Scholar 

  3. Fornell, C., Wernerfelt, B.: A model for customer complaint management. Mark. Sci. 7(3), 287–298 (1988)

    Article  Google Scholar 

  4. Thuring, F., Nielsen, J.P., Guillen, M., Bolance, C.: Selecting prospects for cross-selling financial products using multivariate credibility. Expert Syst. Appl. 39(10), 8809–8816 (1988)

    Article  Google Scholar 

  5. Keaveney, S.M.: Customer switching behavior in service industries: an exploratory study. J. Mark. 59, 71–82 (1995)

    Article  Google Scholar 

  6. Brockett, P.L., Golden, L.L., Guillen, M., Nielsen, J.P., Parner, J., Pérez-Marín, A.M.: Survival analysis of a household portfolio of insurance policies: how much time do you have to stop total customer defection? J. Risk Insur. 75(3), 713–737 (2008)

    Article  Google Scholar 

  7. Fornell, C.: A national customer satisfaction barometer: the Swedish experience. J. Mark. 56, 6–21 (1992)

    Article  Google Scholar 

  8. Guillen, M., Nielsen, J.P., Scheike, T.H., Pérez-Marín, A.M.: Time-varying effects in the analysis of customer loyalty: a case study in insurance. Expert Syst. Appl. 39(3), 3551–3558 (2012)

    Article  Google Scholar 

  9. Guillen, M., Nielsen, J.P., Pérez-Marín, A.M.: The need to monitor customer loyalty and business risk in the European insurance industry. In: Geneva Papers on Risk and Insurance, Issues and Practice, pp. 207–218 (2008)

    Google Scholar 

  10. Guelman, L., Guillen, M.: A causal inference approach to measure price elasticity in automobile insurance. Expert Syst. Appl. 41(2), 387–396 (2014)

    Article  Google Scholar 

  11. Guelman, L., Guillen, M., Pérez-Marín, A.M.: A survey of personalized treatment models for pricing strategies in insurance. Insur. Math. Econ. 58, 68–76 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  12. Guelman, L., Guillen, M., Pérez-Marín, A.M.: A decision support framework to implement optimal personalized marketing interventions. Decis. Support Syst. 72, 24–32 (2015)

    Article  Google Scholar 

  13. Guelman, L., Guillen, M., Pérez-Marín, A.M.: Uplift random forests. Cybern. Syst. 46(3–4), 230–248 (2015)

    Article  Google Scholar 

  14. Guillen, M.: Regression with categorical dependent variables. In: Predictive Modeling Applications in Actuarial Science. Predictive Modeling Techniques, vol. 1, Cambridge University Press, Cambridge (2014)

    Google Scholar 

  15. Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27, 861–874 (2006)

    Article  Google Scholar 

  16. Gigliarano, C., Figini, S., Muliere, P.: Making classifier performance comparisons when ROC curves intersect. Comput. Stat. Data Anal. 77, 300–312 (2014)

    Article  MathSciNet  Google Scholar 

  17. Stripling, E., Vanden Broucke, S., Antonio, K., Baesens, B., Snoeck, M.: Profit maximizing logistic regression modeling for customer churn prediction. In: IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–10 (2015)

    Google Scholar 

  18. Liu, Y., Zhang, H.H., Wu, Y.: Hard or soft classification? Large-margin unified machines. J. Am. Stat. Assoc. 106(493), 166–177 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  19. Hothorn, T., Hornik, K., Zeileis, A.: Unbiased recursive partitioning: a conditional inference framework. J. Comput. Graph. Stat. 15(3), 651–674 (2006)

    Article  MathSciNet  Google Scholar 

  20. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)

    MATH  Google Scholar 

  21. Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)

    Book  MATH  Google Scholar 

  22. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM (1992)

    Google Scholar 

  23. Karatzoglou, A., Meyer, D., Hornik, K.: Support vector machines in R. J. Stat. Softw. 15(9), 1–28 (2006)

    Article  Google Scholar 

  24. Hsu, C.W., Chang, C.C., Lin, C.J.: A Practical Guide to Support Vector Classification. Department of Computer Science, National Taiwan University, Taiwan (2003)

    Google Scholar 

  25. Tao, D., Tang, X., Li, X., Wu, X.: Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 28(7), 1088–1099 (2006)

    Article  Google Scholar 

  26. Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F.: Package ‘‘e1071”. Misc. Functions of the Department of Statistics (e1071), TU Wien. The comprehensive R archive network (2012)

    Google Scholar 

  27. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)

    Google Scholar 

  28. Gelman, A., Hill, J.: Missing-data imputation. Data Analysis Using Regression and Multilevel/Hierarchical Models, 1st edn, pp. 529–544. Cambridge University Press, Cambridge (2006)

    Google Scholar 

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Acknowledgements

We thank the Spanish Ministry of Economy FEDER grant ECO2013-48326-C2-1-P, AGAUR, and ICREA Academia.

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Authors and Affiliations

  1. Department of Econometrics, Riskcenter-IREA, University of Barcelona, Av. Diagonal 690, 08034, Barcelona, Spain

    Catalina Bolancé, Montserrat Guillen & Alemar E. Padilla-Barreto

Authors
  1. Catalina Bolancé
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  2. Montserrat Guillen
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  3. Alemar E. Padilla-Barreto
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Corresponding author

Correspondence to Alemar E. Padilla-Barreto .

Editor information

Editors and Affiliations

  1. Dept of Accounting & Finance, University of Zaragoza, Zaragoza, Spain

    Raúl León

  2. University Jaume I, Castellón de la Plana, Spain

    María Jesús Muñoz-Torres

  3. Dept of Accounting & Finance, University of Zaragoza, Zaragoza, Spain

    Jose M. Moneva

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© 2016 Springer International Publishing Switzerland

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Bolancé, C., Guillen, M., Padilla-Barreto, A.E. (2016). Predicting Probability of Customer Churn in Insurance. In: León, R., Muñoz-Torres, M., Moneva, J. (eds) Modeling and Simulation in Engineering, Economics and Management. MS 2016. Lecture Notes in Business Information Processing, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-319-40506-3_9

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