Credit Risk Identification in Supply Chains Using Generative Adversarial Networks

2025-01-17

AI Summary

Credit risk modeling in supply chains faces three key challenges: scarcity of labeled data, severe class imbalance, and difficulty in capturing cross-industry dynamic contagion. Method: This paper proposes the first framework jointly leveraging generative adversarial networks (GANs) for both credit risk data augmentation and contagion process modeling. It incorporates temporal feature learning and evaluates performance across diverse industries—steel manufacturing, pharmaceutical distribution, and e-commerce services—to ensure generalizability. Contribution/Results: Compared to logistic regression, decision trees, and conventional neural networks, our method achieves significant improvements in accuracy, recall, and F1-score. It enhances model robustness and cross-domain generalization capability, enabling differentiated risk assessment and forward-looking early warnings. By unifying data synthesis with contagion dynamics, the approach establishes a novel paradigm for risk management in supply chain finance.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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

Problem

• Supply Chain Management
• Credit Risk Prediction
• Financial Health

Innovation

• Generative Adversarial Networks
• Credit Risk Prediction
• Supply Chain