Graph Neural Networks for Modeling and Preventing Large-Scale Cyber Contagion in Interconnected Financial Networks

Abstract

The increasing complexity and interconnectivity of modern financial systems have heightened the risk of large-scale cyber contagion, wherein localized security breaches propagate rapidly across networks of financial institutions. Traditional network risk assessment methods and static analytical models often fail to capture the nonlinear dependencies and dynamic behaviors inherent in these systems. Graph Neural Networks (GNNs), a class of deep learning architectures specifically designed for graph-structured data, offer a promising framework for modeling such complex interconnections and predicting systemic risk propagation. This paper explores the theoretical underpinnings, computational frameworks, and practical implementations of GNNs for modeling cyber contagion in financial networks. We present a comprehensive methodology for integrating transaction data, interbank exposures, and behavioral indicators into graph-based representations suitable for GNN modeling. Furthermore, we discuss strategies for cyber contagion prevention, including anomaly detection, scenario simulations, and adaptive defense mechanisms. Empirical case studies illustrate the potential of GNNs to enhance financial stability through predictive modeling and real-time risk mitigation. Finally, we consider ethical, regulatory, and operational challenges in deploying AI-driven network analytics in sensitive financial environments.