Azzone, M., Barucci, E., Moncayo, G. G. & Marazzina, D. (2022). A machine learning model for lapse prediction in life insurance contracts. Expert Systems with Applications, 191, 116261. https://doi.org/10.1016/j.eswa.2021.116261
Bacinello, A. R. (2005). Endogenous model of surrender conditions in equity-linked life insurance. Insurance: Mathematics and Economics, 37(2), 270-296. https://doi.org/10.1016/j.insmatheco.2005.02.002
Bauer, D., Gao, J., Moenig, T., Ulm, E. R. & Zhu, N. (2017). Policyholder exercise behavior in life insurance: the state of affairs. North American Actuarial Journal, 21(4), 485-501. https://doi.org/10.1080/10920277.2017.1314816
Betancourt, G. A. (2005). Las máquinas de soporte vectorial (SVMs). Scientia et Technica, 11(27), 67-72. https://www.redalyc.org/pdf/849/84911698014.pdf
Canadian Institute of Actuarie. (2014). Lapse experience study for 10-year term insurance - report individual life experience subcommittee.
Cortés, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273-297. https://doi.org/10.1007/BF00994018
Cristianini, N. & Shawe-Taylor, J. (2000). An introduction to support vector machine and other kernel-based learning methods. Cambridge University Press https://doi.org/10.1017/CBO9780511801389
Eling, M. & Kochanski, M. (2013). Research on lapse in life insurance: what has been done and what needs to be done? Journal of Risk Finance, 14(4), 392-413. https://doi.org/10.1108/JRF-12-2012-0088
Eling, M. & Kiesenbauer, D. (2014). What policy features determine life insurance lapse? An analysis of the German market. Journal of Risk and Insurance, 81(2), 241-269. https://doi.org/10.1111/j.1539-6975.2012.01504.x
Fier, S. G. & Liebenberg, A. P. (2013). Life insurance lapse behavior. North American Actuarial Journal, 17(2), 153-167. https://doi.org/10.1080/10920277.2013.803438
Fiuza Pérez, M. y Rodríguez Pérez, J. C. (2000). La regresión logística: una herramienta versátil. Nefrología, 20(6), 477-565. https://www.revistanefrologia.com/es-la-regresion-logistica-una-herramienta-articulo-X0211699500035664
Goodacre, R., Neal, M. J. & Kell, D. B. (1996). Quantitative analysis of multivariate data using artificial neural networks: a tutorial review and applications to the deconvolution of pyrolysis mass spectra. Zentralblatt für Bakteriologie, 284(4), 516-539. https://doi.org/10.1016/S0934-8840(96)80004-1
Gottlieb, D. & Smetters, K. (2021). Lapse-based insurance. American Economic Review, 111(8), 2377-2416. https://doi.org/10.1257/aer.20160868
Guillén, M., Pérez, A. M. & Alcañiz, M, (2011). A logistic regression approach to estimating customer profit loss due to lapses in insurance. Document de Treball No. XREAP 2011-13. http://dx.doi.org/10.2139/ssrn.1942278
Harrell, F. E. (2015). Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis (2nd ed.). Springer.
Hastie, T., Tibshirani, R. & Freidman, J. (2001). The elements of statistical learning: data mining, inference and prediction. Springer. http://dx.doi.org/10.1007/978-0-387-21606-5
He, K., Zhang, X., Ren, S. & Sun, J. (2015). Delving deep into rectifiers: surpassing human-level performance on imagenet classification. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, pp. 1026-1034. https://doi.org/10.1109/ICCV.2015.123
Heldt, R., Silveira, C. S. & Luce, F. B. (2021). Predicting customer value per product: from RFM to RFM/P. Journal of Business Research, 127, 444-453. https://doi.org/10.1016/j.jbusres.2019.05.001
Hosmer, D. W., Lemeshow, S. & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). John Wiley & Sons. https://onlinelibrary.wiley.com/doi/chapter-epub/10.1002/9781118548387.fmatter
Hu, S., O’Hagan, A., Sweeney, J. & Ghahramani, M. (2021). A spatial machine learning model for analysing customers’ lapse behaviour in life insurance. Annals of Actuarial Science, 15(2), 367-393. https://doi.org/10.1017/S1748499520000329
Kiesenbauer, D. (2012). Main determinants of lapse in the German life insurance industry. North American Actuarial Journal, 16(1), 52-73. https://doi.org/10.1080/10920277.2012.10590632
Kim, C. (2005). Modeling surrender and lapse rates with economic variables. North American Actuarial Journal, 9(4), 56-70. https://doi.org/10.1080/10920277.2005.10596225
Kuo, W., Tsai, C. & Chen, W. K. (2003). An empirical study on the lapse rate: the cointegration approach. Journal of Risk and Insurance, 70(3), 489-508. https://doi.org/10.1111/1539-6975.t01-1-00061
Moreno, L. G. (2019). Notas de clase: teoría del riesgo y contingencias.
Namvar, M., Gholamian, M. R. & KhakAbi, S. (2010). A two phase clustering method for intelligent customer segmentation. 2010 International Conference on Intelligent Systems, Modelling and Simulation, Liverpool, UK, pp. 215-219. https://doi.org/10.1109/ISMS.2010.48
Outreville, J. F. (1990). Whole-life insurance lapse rates and the emergency fund hypothesis. Insurance: Mathematics and Economics, 9(4), 249-255. https://doi.org/10.1016/0167-6687(90)90002-U
Outreville, J. F. (2013). The relationship between insurance and economic development: 85 empirical papers for a review of the literature. Risk Management and Insurance Review, 16(1), 71-122. https://doi.org/10.1111/j.1540-6296.2012.01219.x
Pinquet, J., Guillén, M. & Ayuso, M. (2011). Commitment and lapse behavior in long-term insurance: a case study. Journal of Risk and Insurance, 78(4), 983-1002. https://doi.org/10.1111/j.1539-6975.2011.01420.x
Pölsterl, S. (2020). Scikit-survival: a library for time-to-event analysis built on top of scikit-learn. The Journal of Machine Learning Research, 21, 1-6. https://jmlr.org/papers/volume21/20-729/20-729.pdf
Ramchandani, P., Paich, M. & Rao, A. (2017). Incorporating learning into decision making in agent based models. In E. Oliveira, J. Gama, Z. Vale, H. Lopes (Eds.), Progress in Artificial Intelligence: 18th EPIA Conference on Artificial Intelligence, EPIA 2017, Porto, Portugal, (pp. 789-800). https://doi.org/10.1007/978-3-319-65340-2_64
Richman, R. (2018). AI in actuarial science. SSRN. http://dx.doi.org/10.2139/ssrn.3218082
Rozar, T., Scott, R. & Susan, W. (2010). Report on the lapse and mortality experience of post-level premium period term plans.
Rudin, C. (2012). 15.097 Lecture 13: Kernels. MIT Open Course Ware. http://ocw.mit.edu/courses/15-097-prediction-machine-learning-and-statistics-spring-2012/resources/mit15_097s12_lec13/
Tang, S. & Yang, Y. (2021). Why neural networks apply to scientific computing? Theoretical and Applied Mechanics Letters, 11(3), 100242. https://doi.org/10.1016/j.taml.2021.100242
Tsai, C., Kuo, W. & Chiang, D. M. (2009). The distributions of policy reserves considering the policy-year structures of surrender rates and expense ratios. Journal of Risk and Insurance, 76(4), 909-931. https://doi.org/10.1111/j.1539-6975.2009.01324.x
Weindorfer, B. (2012). QIS5: a review of the results for EEA Member States, Austria and Germany. https://www.fh-vie.ac.at/uploads/WP-070_2012.pdf
Xong, L. J. & Kang, H. M. (2019). A comparison of classification models for life insurance lapse risk. International Journal of Recent Technology and Engineering, 7(5S), 245-250.
Zaki, M. & Meira, W. (2014). Data mining and Machine Learning: fundamental concepts and algorithms. (2nd ed.). Cambridge University Press. https://www.cambridge.org/co/academic/subjects/computer-science/knowledge-management-databases-and-data-mining/data-mining-and-machine-learning-fundamental-concepts-and-algorithms-2nd-edition?format=HB&isbn=9781108473989
Zhu, H., Zeng, H., Liu, J. & Zhang, X. (2021). Logish: A new nonlinear nonmonotonic activation function for convolutional neural network. Neurocomputing, 458, 490-499. https://doi.org/10.1016/j.neucom.2021.06.067
