TransCORALNet: A two-stream transformer CORAL networks for supply chain credit assessment cold start

Abstract

Supply chain credit assessment is critical for financial decision-making due to limited historical data for new borrowers and the domain shift between segment industries. Existing models often struggle with challenges such as domain shift, cold start, imbalanced classes, and lack of interpretability. This paper proposes an interpretable two-stream transformer CORAL network (TransCORALNet) for supply chain credit assessment, designed to address these challenges. The two-stream domain adaptation architecture with correlation alignment (CORAL) loss serves as the core model and is equipped with a transformer, which provides insights into the learned features and allows efficient parallelization during training. Thanks to the domain adaptation capability of the proposed model, the domain shift between the source and target domains is minimized. Furthermore, we employ Local Interpretable Model-agnostic Explanations (LIME) to provide additional insights into the model predictions and identify the key features contributing to supply chain credit assessment decisions. Experimental results on a real-world dataset demonstrate the superiority of TransCORALNet over several state-of-the-art baselines in terms of accuracy. The code is available on GitHub.¹