Graph Neural Networks for Systemic Risk Detection in Interconnected Financial Markets

Abstract

Systemic risk in financial markets arises from the interaction of balance-sheet exposures, market prices, funding conditions, and behavioral responses across institutions and infrastructures. Conventional risk models often treat institutions as independent or rely on aggregate indicators, which limits their capacity to detect contagion pathways that emerge from network structure. This paper examines graph neural networks as a computational and institutional architecture for systemic risk detection in interconnected financial markets. It argues that graph learning is valuable not merely because it can model relational data, but because it can support supervisory workflows that require temporal awareness, interpretability, robustness, and cross-border coordination. The discussion situates graph neural networks within a broader socio-technical system that includes data governance, model validation, regulatory reporting, cloud and high-performance computing infrastructure, and policy accountability. The paper analyzes architectural trade-offs among graph convolutional, attention-based, and inductive message-passing designs, and it considers how these models handle heterogeneous exposures, regime shifts, missing data, and adversarial manipulation. It further explores evaluation criteria beyond predictive accuracy, including stability under stress, fairness across jurisdictions and institution types, and sustainability in deployment. The paper concludes that graph neural networks can improve systemic risk detection when embedded in a governance framework that treats models as supervisory instruments rather than autonomous decision-makers. The central challenge is therefore not only technical performance but the design of trustworthy, auditable, and adaptable infrastructures capable of detecting financial fragility without amplifying it.