Automated Underwriting in Life Insurance: Predictions and Optimisation

  • Rhys Biddle
  • Shaowu Liu
  • Peter Tilocca
  • Guandong XuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10837)


Underwriting is an important stage in the life insurance process and is concerned with accepting individuals into an insurance fund and on what terms. It is a tedious and labour-intensive process for both the applicant and the underwriting team. An applicant must fill out a large survey containing thousands of questions about their life. The underwriting team must then process this application and assess the risks posed by the applicant and offer them insurance products as a result. Our work implements and evaluates classical data mining techniques to help automate some aspects of the process to ease the burden on the underwriting team as well as optimise the survey to improve the applicant experience. Logistic Regression, XGBoost and Recursive Feature Elimination are proposed as techniques for the prediction of underwriting outcomes. We conduct experiments on a dataset provided by a leading Australian life insurer and show that our early-stage results are promising and serve as a foundation for further work in this space.


  1. 1.
    Howlette, B., Rajan, M., Chieng, S.P.: Future of life insurance in Australia. Technical report, PricewaterhouseCoopers (2017)Google Scholar
  2. 2.
    Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)Google Scholar
  3. 3.
    Gandhi, D., Kaul, R.: Life and health - future of life underwriting. Asia Insur. Rev. 52, 76–77 (2016)Google Scholar
  4. 4.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Granitto, P.M., Furlanello, C., Biasioli, F., Gasperi, F.: Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometr. Intell. Lab. Syst. 83(2), 83–90 (2006)CrossRefGoogle Scholar
  7. 7.
    Hu, Q., Liu, J., Daren, Y.: Mixed feature selection based on granulation and approximation. Knowl.-Based Syst. 21(4), 294–304 (2008)CrossRefGoogle Scholar
  8. 8.
    Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002)CrossRefGoogle Scholar
  9. 9.
    Joram, M.K., Harrison, B.K., Joseph, K.N.: A knowledge-based system for life insurance underwriting. Int. J. Inf. Technol. Comput. Sci. 9, 40–49 (2017)Google Scholar
  10. 10.
    Guelman, L.: Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Syst. Appl. 39, 3659–3667 (2012)CrossRefGoogle Scholar
  11. 11.
    Liu, S., Xu, G., Zhu, X., Zhou, Z.: Towards simplified insurance application via sparse questionnaire optimization. In: 2017 International Conference on Behavioral, Economic, Socio-Cultural Computing (BESC), pp. 1–2, October 2017Google Scholar
  12. 12.
    Arora, N., Vij, S.: A hybrid neuro-fuzzy network for underwriting of life insurance. Int. J. Adv. Res. Comput. Sci. 3, 231–236 (2012)Google Scholar
  13. 13.
    Jensen, R., Shen, Q.: Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. IEEE Trans. Knowl. Data Eng. 16, 1457–1471 (2004)CrossRefGoogle Scholar
  14. 14.
    Kacelan, V., Kacelan, L., Buric, M.N.: A nonparametric data mining approach for risk prediction in car insurance: a case study from the montenegrin market. Econ. Res.-Ekonomska Istraivanja 29, 545–558 (2017)CrossRefGoogle Scholar
  15. 15.
    Rodriguez-Galiano, V.F., Luque-Espinar, J.A., Chica-Olmo, M., Mendes, M.P.: Feature selection approaches for predictive modelling of groundwater nitrate pollution: an evaluation of filters, embedded and wrapper methods. Sci. Total Environ. 624, 661–672 (2018)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Rhys Biddle
    • 1
  • Shaowu Liu
    • 1
  • Peter Tilocca
    • 2
  • Guandong Xu
    • 1
    Email author
  1. 1.Advanced Analytics InstituteUniversity of Technology SydneySydneyAustralia
  2. 2.OnePath Insurance, ANZ WealthSydneyAustralia

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