Machine Learning and Financial Literacy: An Exploration of Factors Influencing Financial Knowledge in Italy
Susanna Levantesi () and
Giulia Zacchia ()
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Susanna Levantesi: Department of Statistics, Sapienza University of Rome, 00161 Rome, Italy
JRFM, 2021, vol. 14, issue 3, 1-21
In recent years, machine learning techniques have assumed an increasingly central role in many areas of research, from computer science to medicine, including finance. In the current study, we applied it to financial literacy to test its accuracy, compared to a standard parametric model, in the estimation of the main determinants of financial knowledge. Using recent data on financial literacy and inclusion among Italian adults, we empirically tested how tree-based machine learning methods, such as decision trees, random, forest and gradient boosting techniques, can be a valuable complement to standard models (generalized linear models) for the identification of the groups in the population in most need of improving their financial knowledge.
Keywords: financial literacy; machine learning; decision trees; random forest; gradient boosting (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:14:y:2021:i:3:p:120-:d:516369
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