Credit Risk Identification in Supply Chains Using Generative Adversarial Networks

TL;DR

This paper presents a novel application of Generative Adversarial Networks to improve credit risk identification in supply chains by generating synthetic data, which enhances predictive accuracy and captures complex dependencies across industries.

Contribution

The study introduces a GAN-based model for supply chain credit risk analysis, addressing data scarcity and imbalanced datasets, and demonstrates its superiority over traditional methods across multiple industries.

Findings

GANs improve predictive accuracy in credit risk detection

The model outperforms logistic regression, decision trees, and neural networks

Industry-specific risk contagion patterns are effectively captured

Abstract

Credit risk management within supply chains has emerged as a critical research area due to its significant implications for operational stability and financial sustainability. The intricate interdependencies among supply chain participants mean that credit risks can propagate across networks, with impacts varying by industry. This study explores the application of Generative Adversarial Networks (GANs) to enhance credit risk identification in supply chains. GANs enable the generation of synthetic credit risk scenarios, addressing challenges related to data scarcity and imbalanced datasets. By leveraging GAN-generated data, the model improves predictive accuracy while effectively capturing dynamic and temporal dependencies in supply chain data. The research focuses on three representative industries-manufacturing (steel), distribution (pharmaceuticals), and services (e-commerce) to assess industry-specific credit risk contagion. Experimental results demonstrate that the GAN-based model outperforms traditional methods, including logistic regression, decision trees, and neural networks, achieving superior accuracy, recall, and F1 scores. The findings underscore the potential of GANs in proactive risk management, offering robust tools for mitigating financial disruptions in supply chains. Future research could expand the model by incorporating external market factors and supplier relationships to further enhance predictive capabilities. Keywords- Generative Adversarial Networks (GANs); Supply Chain Risk; Credit Risk Identification; Machine Learning; Data Augmentation