# Credit Risk Identification in Supply Chains Using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2501.10348 · HTML: https://mlsanity.com/paper/2501.10348/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 47 references that are in the map.
- First posted on arXiv 17 Jan 2025.

## Builds on (most cited first, 17 of 17)

- [Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow](https://mlsanity.com/paper/2306.07209/) (2023)
- [Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning](https://mlsanity.com/paper/2407.01531/) (2024)
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- [Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Video](https://mlsanity.com/paper/2203.06667/) (2022)
- [Decoding by Contrasting Knowledge: Enhancing LLMs' Confidence on Edited Facts](https://mlsanity.com/paper/2405.11613/) (2024)
- [Backdooring Vision-Language Models with Out-Of-Distribution Data](https://mlsanity.com/paper/2410.01264/) (2024)
- [Attention-Enhancing Backdoor Attacks Against BERT-based Models](https://mlsanity.com/paper/2310.14480/) (2023)
- [More but Correct: Generating Diversified and Entity-revised Medical Response](https://mlsanity.com/paper/2108.01266/) (2021)
- [Task-Agnostic Detector for Insertion-Based Backdoor Attacks](https://mlsanity.com/paper/2403.17155/) (2024)
- [OMPGPT: A Generative Pre-trained Transformer Model for OpenMP](https://mlsanity.com/paper/2401.16445/) (2024)
- [GM-DF: Generalized Multi-Scenario Deepfake Detection](https://mlsanity.com/paper/2406.20078/) (2024)
- [Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models](https://mlsanity.com/paper/2501.07033/) (2025)
- [Towards Better Chinese-centric Neural Machine Translation for Low-resource Languages](https://mlsanity.com/paper/2204.04344/) (2022)
- [Robust Nonparametric Regression under Poisoning Attack](https://mlsanity.com/paper/2305.16771/) (2023)
- [Data Race Detection Using Large Language Models](https://mlsanity.com/paper/2308.07505/) (2023)
- [In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before An Ongoing Trajectory Terminates](https://mlsanity.com/paper/2410.15612/) (2024)
- [Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms](https://mlsanity.com/paper/2412.18202/) (2024)

## Built on by (most cited first, 5 of 5)

- [JanusVLN: Decoupling Semantics and Spatiality with Dual Implicit Memory for Vision-Language Navigation](https://mlsanity.com/paper/2509.22548/) (2025)
- [Regression and Forecasting of U.S. Stock Returns Based on LSTM](https://mlsanity.com/paper/2502.05210/) (2025)
- [Credit Risk Analysis for SMEs Using Graph Neural Networks in Supply Chain](https://mlsanity.com/paper/2507.07854/) (2025)
- [Conditional Generative Modeling for Enhanced Credit Risk Management in Supply Chain Finance](https://mlsanity.com/paper/2506.15305/) (2025)
- [Adversarial Training for Tabular Credit Scoring: A Multi-Attack Robustness Evaluation in P2P Lending](https://mlsanity.com/paper/2609.09945/) (2026)

## Abstract (arXiv)

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