Machine Learning Models and Data-Balancing Techniques for Credit Scoring: What Is the Best Combination?

Forecasting the creditworthiness of customers is a central issue banking activity. This task requires analysis large datasets with many variables, for which machine learning algorithms and feature selection techniques are crucial tool. Moreover, percentages “good” “bad” typically imbalanced such that over- undersampling should be employed. In literature, most investigations tackle these three issues individually. Since there little evidence about their joint performance, in this paper, we try to fill gap. We use five classifiers, each them combined different various data-balancing approaches. According empirical retail credit bank dataset, find best combination given by random forests, forest recursive elimination oversampling.