  This paper proposes an interpretable two-stream transformer CORAL networks
(TransCORALNet) for supply chain credit assessment under the segment industry
and cold start problem. The model aims to provide accurate credit assessment
prediction for new supply chain borrowers with limited historical data. Here,
the two-stream domain adaptation architecture with correlation alignment
(CORAL) loss is used as a core model and is equipped with transformer, which
provides insights about the learned features and allow efficient
parallelization during training. Thanks to the domain adaptation capability of
the proposed model, the domain shift between the source and target domain is
minimized. Therefore, the model exhibits good generalization where the source
and target do not follow the same distribution, and a limited amount of target
labeled instances exist. Furthermore, we employ Local Interpretable
Model-agnostic Explanations (LIME) to provide more insight into the model
prediction and identify the key features contributing to supply chain credit
assessment decisions. The proposed model addresses four significant supply
chain credit assessment challenges: domain shift, cold start, imbalanced-class
and interpretability. Experimental results on a real-world data set demonstrate
the superiority of TransCORALNet over a number of state-of-the-art baselines in
terms of accuracy. The code is available on GitHub
https://github.com/JieJieNiu/TransCORALN .
