Skip to main content
SpringerLink
Log in

Predicting Financial Well-Being Using Observable Features and Gradient Boosting

  • Conference paper
  • First Online:
AI 2019: Advances in Artificial Intelligence (AI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11919))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    National Financial Well-being Survey Public User File User’s Guide https://www.consumerfinance.gov/data-research/research-reports/financial-well-being-scale/.

  2. 2.

    URL: https://www.consumerfinance.gov/consumer-tools/financial-well-being/.

  3. 3.

    National Financial Well-Being Survey Public Use File Codebook https://files.consumerfinance.gov/f/documents/cfpb_nfwbs-puf-codebook.pdf.

  4. 4.

    URL: https://catboost.ai/docs/concepts/fstr.html.

  5. 5.

    URL: https://catboost.ai/docs/concepts/shap-values.html.

  6. 6.

    Plotting validation metrics against the number of training examples.

References

  1. CFPB: CFPB Financial Well-Being Scale: scale development technical report (2017)

    Google Scholar 

  2. Likert, R.: A technique for the measurement of attitudes. Arch. Psychol. (1932)

    Google Scholar 

  3. Embretson, S.E., Reise, S.P.: Item Response Theory. Psychology Press (2013)

    Google Scholar 

  4. Joo, S.: Personal financial wellness. In: Xiao, J.J. (ed.) Handbook of Consumer Finance Research, pp. 21–33. Springer, New York (2008). https://doi.org/10.1007/978-0-387-75734-6_2

    Chapter  Google Scholar 

  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. 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. Bureau, C.F.P.: Financial well-being: the goal of financial education. Iowa City, IA (2015)

    Google Scholar 

  8. Bureau, C.F.P.: Financial Well-Being in America, Washington, DC (2017)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. Parker, S., Castillo, N., Garon, T., Levy, R.: Eight ways to measure financial health. Center for Financial Services Innovation, Chicago (2016)

    Google Scholar 

  12. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189–1232 (2001)

    Article  MathSciNet  Google Scholar 

  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. Dorogush, A.V., Ershov, V., Gulin, A.: CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363 (2018)

  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. Lundberg, S.M., Erion, G.G., Lee, S.I.: Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:1802.03888 (2018)

  17. Ng, A.: Machine learning yearning (2017). https://www.deeplearning.ai/machine-learning-yearning/

  18. Flach, P.: Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, New York (2012)

    Book  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

  1. Computational Learning Systems Laboratory, School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia

    Iqbal Madakkatel, Belinda Chiera & Mark D. McDonnell

Authors
  1. Iqbal Madakkatel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Belinda Chiera
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mark D. McDonnell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Iqbal Madakkatel .

Editor information

Editors and Affiliations

  1. University of South Australia, Adelaide, SA, Australia

    Jixue Liu

  2. The University of Melbourne, Melbourne, VIC, Australia

    James Bailey

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Madakkatel, I., Chiera, B., McDonnell, M.D. (2019). Predicting Financial Well-Being Using Observable Features and Gradient Boosting. In: Liu, J., Bailey, J. (eds) AI 2019: Advances in Artificial Intelligence. AI 2019. Lecture Notes in Computer Science(), vol 11919. Springer, Cham. https://doi.org/10.1007/978-3-030-35288-2_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-35288-2_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35287-5

  • Online ISBN: 978-3-030-35288-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation