Utilizing Effective Dynamic Graph Learning to Shield Financial Stability from Risk Propagation

TL;DR

This paper introduces GraphShield, a dynamic graph learning framework that enhances risk detection and visualization to prevent the propagation of financial risks, thereby strengthening financial stability.

Contribution

The paper presents a novel dynamic graph learning approach with risk recognition and propagation visualization modules, advancing AI-based financial risk management.

Findings

Robust performance on real-world datasets

Effective identification of hidden financial threats

Visualization of risk propagation pathways

Abstract

Financial risks can propagate across both tightly coupled temporal and spatial dimensions, posing significant threats to financial stability. Moreover, risks embedded in unlabeled data are often difficult to detect. To address these challenges, we introduce GraphShield, a novel approach with three key innovations: Enhanced Cross-Domain Infor mation Learning: We propose a dynamic graph learning module to improve information learning across temporal and spatial domains. Advanced Risk Recognition: By leveraging the clustering characteristics of risks, we construct a risk recognizing module to enhance the identification of hidden threats. Risk Propagation Visualization: We provide a visualization tool for quantifying and validating nodes that trigger widespread cascading risks. Extensive experiments on two real-world and two open-source datasets demonstrate the robust performance of our framework. Our approach represents a significant advancement in leveraging artificial intelligence to enhance financial stability, offering a powerful solution to mitigate the spread of risks within financial networks.