Semi-supervised heterogeneous domain adaptation for few-sample credit risk classification

Abstract

Credit risk classification is a crucial area in machine learning-enhanced financial decision support systems, and numerous studies have achieved significant progress. However, data in the modern financial landscape is inherently complex, characterized by a lack of labeled data, cross-domain heterogeneity, and class imbalance. These three major problems hinder the achievement of credit risk classification. Therefore, we propose a semi-supervised heterogeneous domain adaptation method and an imbalanced data augmentation method to overcome these challenges with a single neural network-based model. Experimental results obtained from four representative benchmark datasets confirm the superior performance of the proposed method.