Multi-Scale Temporal Scenario Planning for Financial Networks: A GNN Approach to Stress Testing

This work proposes a robust multi-scenario stress testing framework based on graph neural networks that enables large-scale anomaly detection and systematic risk assessment across pre-and post-pandemic financial landscapes and provides regulators and financial institutions with practical tools to identify emergent risks and enhance system resilience against future structural and information-based shocks.