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

arXiv cs.AI · 6 hours ago

Researchers developed CausalGraphX, a framework combining Graph Neural Networks with counterfactual reasoning to assess systemic risk in financial networks. The system uses graph attention mechanisms and adversarial regularization to identify causal factors in shock propagation and generates explanations such as minimum capital injections needed to prevent defaults. CausalGraphX outperforms traditional models on synthetic financial network datasets, enabling regulators to conduct stress tests with interpretable results rather than black-box predictions.