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

2025-06-18

AI Summary

Small- and medium-sized enterprises (SMEs) face significant financing constraints in cross-border e-commerce due to insufficient credit histories. In third-party logistics (3PL)-led supply chain finance (SCF), a critical gap exists between credit risk assessment and loan amount determination. To address this, we propose the first joint decision-making framework leveraging conditional generative AI for SCF risk modeling. Our approach innovatively integrates a quantile regression generative mixture model (QRGMM) with a functionalized risk measurement methodology, enabling theoretically grounded, simultaneous estimation of multi-dimensional risk metrics—including default probability and loss distribution. Augmented by DeepFM for enhanced feature interaction modeling, the framework achieves substantial improvements in default prediction accuracy and loan amount recommendation rationality on both synthetic and real-world e-commerce logistics datasets. This work delivers a verifiable, deployable, AI-driven credit solution tailored for SMEs.

Technology Category

• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

The rapid expansion of cross-border e-commerce (CBEC) has created significant opportunities for small and medium-sized enterprises (SMEs), yet financing remains a critical challenge due to SMEs' 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 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 represents a pioneering application of generative AI in CBEC SCF risk management, offering a solid foundation for enhanced credit practices and improved SME access to capital.

Problem

• Addresses credit risk assessment in 3PL-led SCF for SMEs
• Determines optimal loan sizes using conditional generative modeling
• Integrates QRGMM and DeepFM for complex e-commerce sales data

Innovation

• Conditional generative modeling for risk estimation
• Quantile-Regression-based Generative Metamodeling foundation
• Integration of QRGMM with Deep Factorization Machines