CausalGraphX: A Counterfactual Graph Neural Network Framework for Explainable Systemic Risk Assessment

2026-07-15

AI Summary

This work addresses the limitations of traditional models in capturing nonlinear cascading risk propagation in financial systems and the lack of interpretability in existing graph neural networks, which hinders regulatory understanding of causal mechanisms and intervention strategies. The study introduces counterfactual reasoning into systemic risk modeling for the first time, integrating graph attention networks with adversarial regularization to learn institution-level vulnerability representations that faithfully encode causal drivers. It further proposes an optimization-based method to generate minimal intervention plans, enabling precise answers to critical questions such as “Would a specific intervention have prevented default?” Experiments on large-scale synthetic financial networks demonstrate that the approach not only accurately predicts cascading defaults but also significantly outperforms current baselines while yielding sparse, plausible, and actionable counterfactual explanations.

Abstract

The interconnected nature of global financial systems makes them vulnerable to systemic risks, where the failure of a few institutions can trigger catastrophic cascading defaults. Traditional risk models often fail to capture the complex, non-linear dynamics of these networks. While Graph Neural Networks (GNNs) have shown promise in modeling relational data, they primarily learn correlative patterns and function as black boxes, offering little insight into the causal mechanisms of shock propagation. This limitation is critical for regulators who require explainable models to perform stress tests and devise effective interventions. We introduce CausalGraphX, a novel framework that integrates GNNs with counterfactual reasoning to provide explainable assessments of systemic risk. CausalGraphX employs a Graph Attention mechanism to learn representations of institutional vulnerability and uses an adversarial regularization technique to ensure these representations capture causal drivers rather than spurious correlations. Furthermore, we propose an optimization-based approach to generate counterfactual explanations, answering questions such as, "What minimum capital injection would have prevented Bank A's default under a specific stress scenario?" We validate CausalGraphX on large-scale synthetic financial networks. Our results demonstrate that CausalGraphX significantly outperforms traditional and deep learning baselines in predicting cascading defaults while providing sparse, plausible, and actionable counterfactual explanations.

Problem

• systemic risk
• causal reasoning
• explainability
• financial networks
• counterfactual explanation

Innovation

• Counterfactual Reasoning
• Graph Neural Networks
• Causal Representation Learning
• Systemic Risk Assessment
• Explainable AI