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 .

翻译：摘要：本文提出了一种可解释的双流Transformer CORAL网络（TransCORALNet），用于解决细分行业及冷启动问题下的供应链信用评估。该模型旨在为历史数据有限的新供应链借款人提供准确的信用评估预测。模型采用基于相关对齐（CORAL）损失的双流域适应架构作为核心，并配备Transformer模块，既能提供学习特征的洞察，又能在训练过程中实现高效并行化。凭借所提模型的域适应能力，源域与目标域之间的域偏移得以最小化。因此，当源域与目标域不服从相同分布且目标域标记样本有限时，模型仍表现出良好的泛化性能。此外，我们采用局部可解释模型无关解释（LIME）来深入理解模型预测结果，并识别对供应链信用评估决策起关键作用的特征。该模型解决了供应链信用评估中的四个重大挑战：域偏移、冷启动、类别不平衡及可解释性。在真实数据集上的实验结果表明，TransCORALNet在准确性上优于多个最先进的基线模型。代码已开源至GitHub https://github.com/JieJieNiu/TransCORALN。