Credit Scoring for SMEs Using Machine Learning Techniques

Abstract

A credit scoring model is a useful tool for Small and Medium-sized Enterprises (SMEs) lending. In this study, we investigated methods to improve the accuracy of the scoring model using machine learning. As a result, we have shown that Gradient Boosting Decision Tree (GBDT) can obtain the highest accuracy. We found out that GBDT shows better performance than other methods especially when we use more than 10000 learning data. In addition, we demonstrated that ensemble learning can further improve accuracy. According to our simplified estimation, it was suggested that the ensemble learning can reduce the default rate by 16% compared with the conventional method.