标题： 供应链金融中的条件生成建模以增强信用风险管理 显示英文标题

标题： Conditional Generative Modeling for Enhanced Credit Risk Management in Supply Chain Finance

摘要： 跨境电子商务（CBEC）的迅速扩展为中小型卖家创造了重大机遇，但由于他们的信用历史有限，融资仍然是一个关键挑战。 第三方物流（3PL）主导的供应链金融（SCF）已成为一种有前景的解决方案，利用在途库存作为抵押品。 我们提出了一种针对3PL主导的SCF的先进信用风险管理框架，解决了信用风险评估和贷款规模确定的双重挑战。 具体而言，我们通过基于分位数回归的生成元模型（QRGMM）对销售分布进行条件生成建模，作为风险度量估计的基础。 我们提出了一种统一的框架，能够在引入功能风险度量公式的同时，灵活地估计多个风险度量，该公式系统地捕捉这些风险度量与不同贷款水平之间的关系，并得到理论保证的支持。 为了捕捉电子商务销售数据中的复杂协变量交互，我们将QRGMM与深度因子分解机（DeepFM）相结合。 在合成数据和真实数据上的大量实验验证了我们的模型在信用风险评估和贷款规模确定方面的有效性。 本研究探讨了生成模型在CBEC SCF风险管理中的应用，展示了它们在加强信用评估和支持中小型卖家融资方面的潜力。 显示英文摘要

摘要： The rapid expansion of cross-border e-commerce (CBEC) has created significant opportunities for small- and medium-sized sellers, yet financing remains a critical challenge due to their limited credit histories. Third-party logistics (3PL)-led supply chain finance (SCF) has emerged as a promising solution, leveraging in-transit inventory as collateral. We propose an advanced credit risk management framework tailored for 3PL-led SCF, addressing the dual challenges of credit risk assessment and loan size determination. Specifically, we leverage conditional generative modeling of sales distributions through Quantile-Regression-based Generative Metamodeling (QRGMM) as the foundation for risk measures estimation. We propose a unified framework that enables flexible estimation of multiple risk measures while introducing a functional risk measure formulation that systematically captures the relationship between these risk measures and varying loan levels, supported by theoretical guarantees. To capture complex covariate interactions in e-commerce sales data, we integrate QRGMM with Deep Factorization Machines (DeepFM). Extensive experiments on synthetic and real-world data validate the efficacy of our model for credit risk assessment and loan size determination. This study explores the use of generative models in CBEC SCF risk management, illustrating their potential to strengthen credit assessment and support financing for small- and medium-sized sellers.