Predicting Financial Well-Being Using Observable Features and Gradient Boosting

  • Iqbal MadakkatelEmail author
  • Belinda Chiera
  • Mark D. McDonnell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11919)


Financial well-being and its measurement are well researched topics in personal finance, yet there is no universally agreed definition of financial well-being. Machine learning is proliferating into new application domains. In this study we investigate the use of state-of-the-art gradient boosting methods for predicting subjective levels of financial well-being, using the Consumer Finance Protection Bureau (CFPB) National Financial Well-being dataset. To enable interpretability, we identify the most important observable features required for accurate predictions. These important features are then analysed using factor analysis to understand hidden themes in the data.


Personal finance Financial well-being Machine learning Gradient boosting Decision trees Exploratory factor analysis 



I. Madakkatel acknowledges the support of an Australian Government Research Training Program (RTP) Scholarship.


  1. 1.
    CFPB: CFPB Financial Well-Being Scale: scale development technical report (2017)Google Scholar
  2. 2.
    Likert, R.: A technique for the measurement of attitudes. Arch. Psychol. (1932)Google Scholar
  3. 3.
    Embretson, S.E., Reise, S.P.: Item Response Theory. Psychology Press (2013)Google Scholar
  4. 4.
    Joo, S.: Personal financial wellness. In: Xiao, J.J. (ed.) Handbook of Consumer Finance Research, pp. 21–33. Springer, New York (2008). Scholar
  5. 5.
    Kempson, E., Finney, A., Poppe, C.: Financial well-being: a conceptual model and preliminary analysis. Final Edition: Project Note, (3) (2017)Google Scholar
  6. 6.
    Muir, K., et al.: Exploring financial wellbeing in the Australian context. Centre for Social Impact & Social Policy Research Centre, University of New South Wales, Sydney (2017)Google Scholar
  7. 7.
    Bureau, C.F.P.: Financial well-being: the goal of financial education. Iowa City, IA (2015)Google Scholar
  8. 8.
    Bureau, C.F.P.: Financial Well-Being in America, Washington, DC (2017)Google Scholar
  9. 9.
    Brüggen, E.C., Hogreve, J., Holmlund, M., Kabadayi, S., Löfgren, M.: Financial well-being: a conceptualization and research agenda. J. Bus. Res. 79, 228–237 (2017)CrossRefGoogle Scholar
  10. 10.
    Comerton-Forde, C., Ip, E., Ribar, D.C., Ross, J., Salamanca, N., Tsiaplias, S.: Using Survey and Banking Data to Measure Financial Wellbeing (2018)Google Scholar
  11. 11.
    Parker, S., Castillo, N., Garon, T., Levy, R.: Eight ways to measure financial health. Center for Financial Services Innovation, Chicago (2016)Google Scholar
  12. 12.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189–1232 (2001)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Olson, R.S., La Cava, W., Mustahsan, Z., Varik, A., Moore, J.H.: Data-driven advice for applying machine learning to bioinformatics problems. arXiv preprint arXiv:1708.05070 (2017)
  14. 14.
    Dorogush, A.V., Ershov, V., Gulin, A.: CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363 (2018)
  15. 15.
    Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, pp. 3146–3154 (2017)Google Scholar
  16. 16.
    Lundberg, S.M., Erion, G.G., Lee, S.I.: Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:1802.03888 (2018)
  17. 17.
    Ng, A.: Machine learning yearning (2017).
  18. 18.
    Flach, P.: Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, New York (2012)CrossRefGoogle Scholar
  19. 19.
    Gutter, M., Copur, Z.: Financial behaviors and financial well-being of college students: evidence from a national survey. J. Fam. Econ. Issues 32(4), 699–714 (2011)CrossRefGoogle Scholar
  20. 20.
    Joo, S.H., Grable, J.E.: An exploratory framework of the determinants of financial satisfaction. J. Fam. Econ. Issues 25(1), 25–50 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Iqbal Madakkatel
    • 1
    Email author
  • Belinda Chiera
    • 1
  • Mark D. McDonnell
    • 1
  1. 1.Computational Learning Systems Laboratory, School of Information Technology and Mathematical SciencesUniversity of South AustraliaMawson LakesAustralia

Personalised recommendations