# Credit Risk Analysis for SMEs Using Graph Neural Networks in Supply Chain

arXiv: https://arxiv.org/abs/2507.07854 · HTML: https://mlsanity.com/paper/2507.07854/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 47 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 18 of 18)

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- [LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion](https://mlsanity.com/paper/2505.23809/) (2025)
- [Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks](https://mlsanity.com/paper/2506.05873/) (2025)
- [Credit Risk Identification in Supply Chains Using Generative Adversarial Networks](https://mlsanity.com/paper/2501.10348/) (2025)
- [Towards Multimodal Large-Language Models for Parent-Child Interaction: A Focus on Joint Attention](https://mlsanity.com/paper/2502.19877/) (2025)
- [Regression and Forecasting of U.S. Stock Returns Based on LSTM](https://mlsanity.com/paper/2502.05210/) (2025)
- [A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planning](https://mlsanity.com/paper/2506.07236/) (2025)
- [Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms](https://mlsanity.com/paper/2412.18202/) (2024)
- [An Optimal Control Approach for Inverse Problems with Deep Learnable Regularizers](https://mlsanity.com/paper/2409.00498/) (2024)

## Built on by (most cited first, 2 of 2)

- [Temporal Visual Semantics-Induced Human Motion Understanding with Large Language Models](https://mlsanity.com/paper/2512.22249/) (2025)
- [ScatterFusion: A Hierarchical Scattering Transform Framework for Enhanced Time Series Forecasting](https://mlsanity.com/paper/2601.20401/) (2026)

## Abstract (arXiv)

Small and Medium-sized Enterprises (SMEs) are vital to the modern economy, yet their credit risk analysis often struggles with scarce data, especially for online lenders lacking direct credit records. This paper introduces a Graph Neural Network (GNN)-based framework, leveraging SME interactions from transaction and social data to map spatial dependencies and predict loan default risks. Tests on real-world datasets from Discover and Ant Credit (23.4M nodes for supply chain analysis, 8.6M for default prediction) show the GNN surpasses traditional and other GNN baselines, with AUCs of 0.995 and 0.701 for supply chain mining and default prediction, respectively. It also helps regulators model supply chain disruption impacts on banks, accurately forecasting loan defaults from material shortages, and offers Federal Reserve stress testers key data for CCAR risk buffers. This approach provides a scalable, effective tool for assessing SME credit risk.