Graph Neural Networks for Modeling Systemic Risk in Financial Networks
Keywords:
Graph Neural Networks, Systemic Risk, Financial Stability, Contagion Modeling, Algorithmic Governance, Socio-Technical Infrastructure, Relational Learning.Abstract
The global financial system is a highly coupled, non-linear network of institutions, markets, and sovereign entities where the connectivity between agents is as critical as their individual solvency. Traditional risk assessment frameworks, which largely rely on localized balance sheet analysis or linear correlation matrices, have proven insufficient for capturing the topological shifts and cascading contagion pathways inherent in modern systemic crises. This paper explores the integration of Graph Neural Networks (GNNs) as a foundational computational architecture for modeling systemic risk within these complex networks. By leveraging the message-passing paradigm, GNNs allow for the extraction of relational features that describe the structural importance and vulnerability of nodes within a non-Euclidean financial landscape. We conduct an extensive system-level investigation of GNN deployment, emphasizing the structural trade-offs between graph density and predictive robustness. The discussion encompasses the socio-technical infrastructures required for real-time risk monitoring, the data governance challenges associated with proprietary network disclosures, and the environmental sustainability of high-compute graph processing. Furthermore, we analyze the policy implications of GNN-driven risk assessment, addressing concerns regarding algorithmic fairness, model-driven market convergence, and the necessity for transparency in automated regulatory oversight. By synthesizing insights from systems engineering, graph theory, and financial policy, this research proposes a resilient framework for utilizing relational intelligence to safeguard global financial stability.
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