Paper: arXiv 2506.15305

Authors: Qingkai Zhang, L. Jeff Hong, Houmin Yan

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a novel integration of Quantile-Regression-based Generative Metamodeling (QRGMM) and DeepFM to construct functional risk measures for supply chain finance. It provides strong theoretical convergence guarantees alongside empirical validation on both synthetic and real-world e-commerce data.

Research Flowchart

  flowchart TD
    A[Goal: Enhance Credit Risk & Loan Determination in Cross-Border SCF] --> B[Input: E-Commerce Sales Data & Multi-Source Covariates]
    B --> C[Core Model: DeepFM Integrated with QRGMM]
    C --> D[Computational Step: Quantile-Regression Generative Modeling]
    D --> E[Outcome Framework: Functional Risk Measures vs. Loan Size]
    E --> F[Findings: Improved Credit Assessment & Optimal Collateralized Financing]

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0 of 67 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.