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.
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Notes
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National Financial Well-being Survey Public User File User’s Guide https://www.consumerfinance.gov/data-research/research-reports/financial-well-being-scale/.
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National Financial Well-Being Survey Public Use File Codebook https://files.consumerfinance.gov/f/documents/cfpb_nfwbs-puf-codebook.pdf.
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Plotting validation metrics against the number of training examples.
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Acknowledgments
I. Madakkatel acknowledges the support of an Australian Government Research Training Program (RTP) Scholarship.
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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
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