  Financial literacy (FL) represents a person's ability to turn assets into
income, and understanding digital currencies has been added to the modern
definition. FL can be predicted by exploiting unlabelled recorded data in
financial networks via semi-supervised learning (SSL). Measuring and predicting
FL has not been widely studied, resulting in limited understanding of customer
financial engagement consequences. Previous studies have shown that low FL
increases the risk of social harm. Therefore, it is important to accurately
estimate FL to allocate specific intervention programs to less financially
literate groups. This will not only increase company profitability, but will
also reduce government spending. Some studies considered predicting FL in
classification tasks, whereas others developed FL definitions and impacts. The
current paper investigated mechanisms to learn customer FL level from their
financial data using sampling by synthetic minority over-sampling techniques
for regression with Gaussian noise (SMOGN). We propose the SMOGN-COREG model
for semi-supervised regression, applying SMOGN to deal with unbalanced datasets
and a nonparametric multi-learner co-regression (COREG) algorithm for labeling.
We compared the SMOGN-COREG model with six well-known regressors on five
datasets to evaluate the proposed models effectiveness on unbalanced and
unlabelled financial data. Experimental results confirmed that the proposed
method outperformed the comparator models for unbalanced and unlabelled
financial data. Therefore, SMOGN-COREG is a step towards using unlabelled data
to estimate FL level.
