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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)

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.

Keywords

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

Notes

Acknowledgments

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

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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

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