A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique

Abstract

In recent years, research has found that in many credit risk evaluation domains, deep learning is superior to traditional machine learning methods and classifier ensembles perform significantly better than single classifiers. However, credit evaluation model based on deep learning ensemble algorithm has rarely been studied. Moreover, credit data imbalance still challenges the performance of credit scoring models. Therefore, to go some way to filling this research gap, this study developed a new deep learning ensemble credit risk evaluation model to deal with imbalanced credit data. First, an improved synthetic minority oversampling technique (SMOTE) method was developed to overcome known SMOTE shortcomings, after which a new deep learning ensemble classification method combined with the long-short-term-memory (LSTM) network and the adaptive boosting (AdaBoost) algorithm was developed to train and learn the processed credit data. Then, area under the curve (AUC), the Kolmogorov–Smirnov (KS) and the non-parametric Wilcoxon test were employed to compare the performance of the proposed model and other widely used credit scoring models on two imbalanced credit datasets. The experimental test results indicated that the proposed deep learning ensemble model was generally more competitive when addressing imbalanced credit risk evaluation problems than other models.