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

TL;DR

This paper introduces a novel framework using conditional generative models to improve credit risk assessment and loan sizing in supply chain finance for cross-border e-commerce, leveraging sales data and risk measures.

Contribution

It develops a unified, theoretically supported framework combining QRGMM and DeepFM for enhanced risk estimation and loan decision-making in supply chain finance.

Findings

Model accurately estimates multiple risk measures.

Framework effectively determines optimal loan sizes.

Experimental results validate model performance on real data.

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.