Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Qingkai Zhang, L. Jeff Hong, Houmin Yan

TL;DR

This paper develops a distributional credit risk framework for 3PL-led CBEC supply chain finance by learning the full conditional sales distribution with Quantile-Regression-based Generative Metamodeling (QRGMM) and linking it to loan-level risk measures for adaptive loan sizing. It introduces a functional risk formulation (r1, r2, r3) and proves uniform convergence results, extending QRGMM with DeepFM to capture complex covariate interactions (QRGMM^dagger). Through extensive synthetic and real-world experiments, the approach demonstrates superior distributional accuracy and risk-estimation performance over baselines, enabling more informed, data-driven lending decisions. The work provides both theoretical guarantees and practical deployment insights, highlighting the potential of generative distributional modeling to strengthen inclusive finance in CBEC-SCF contexts.

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.

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

TL;DR

This paper develops a distributional credit risk framework for 3PL-led CBEC supply chain finance by learning the full conditional sales distribution with Quantile-Regression-based Generative Metamodeling (QRGMM) and linking it to loan-level risk measures for adaptive loan sizing. It introduces a functional risk formulation (r1, r2, r3) and proves uniform convergence results, extending QRGMM with DeepFM to capture complex covariate interactions (QRGMM^dagger). Through extensive synthetic and real-world experiments, the approach demonstrates superior distributional accuracy and risk-estimation performance over baselines, enabling more informed, data-driven lending decisions. The work provides both theoretical guarantees and practical deployment insights, highlighting the potential of generative distributional modeling to strengthen inclusive finance in CBEC-SCF contexts.

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.

Paper Structure

This paper contains 29 sections, 4 theorems, 44 equations, 27 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. 3PL-led CBEC SCF Model and Functional Risk Formulation
  3. 3PL-led CBEC SCF Model
  4. Functional Risk Formulation
  5. Constructing a Conditional Generative Model for Sales Distribution
  6. Problem Formulation
  7. The QRGMM Algorithm
  8. Asymptotic Analysis of Risk Measures Estimation via QRGMM
  9. Uniform Convergence of Distribution
  10. Asymptotic Analysis for Estimators of Risk Measures
  11. Practical Choice of $m$
  12. Extension of QRGMM for Practical Applicability in SCF Risk Management
  13. Overview of Related Techniques
  14. Matrix Factorization (MF)
  15. Factorization Machines (FM)
  16. ...and 14 more sections

Key Result

Lemma 1

Under Assumptions ass:cons1--ass:cons3, the following results hold:

Figures (27)

  • Figure 1: Schematic of the 3PL-led CBEC-SCF process.
  • Figure 2: Illustration of the Matrix Factorization process.
  • Figure 3: From Matrix Factorization to Factorization Machines.
  • Figure 4: Architecture of Deep Factorization Machines.
  • Figure 5: QRGMM$^\dagger$: integration of DeepFM and QRNN.
  • ...and 22 more figures

Theorems & Definitions (11)

  • Remark 1
  • Remark 2
  • Lemma 1: Hong et.al, 2023
  • Theorem 1
  • proof
  • Lemma 2: Hong et.al, 2023
  • Proposition 1
  • Remark 3
  • Remark 4
  • Remark 5
  • ...and 1 more