Empirical Evaluation of Ensemble Learning for Credit Scoring

Abstract

Credit scoring is an important finance activity. Both statistical techniques and Artificial Intelligence (AI) techniques have been explored for this topic. But different techniques have different advantages and disadvantages on different datasets. Recent studies draw no consistent conclusions to show that one technique is superior to the other, while they suggest combining multiple classifiers, i.e., ensemble learning, may have a better performance. In this study, we conduct an empirical evaluation of the performance of three popular ensemble methods, i.e., bagging, boosting, and stacking, based on four base learners, i.e., Logistic Regression Analysis (LRA), Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The experiment uses the credit dataset including 239 companies’ financial records from China, collected by the Industrial and Commercial Bank of China. Results reveal that ensemble learning can substantially improve individual base learners. Stacking, in our experiments, gets the best performance in terms of all six performance indicators, i.e., type I error, type II error, average accuracy, precision, recall, and F-value.