AI-Driven Predictive Models for Systemic Risk in Global Financial Networks
Abstract
This paper investigates the application of AI in identifying and predicting systemic risks within interconnected global financial networks. Using graph-based machine learning and deep learning techniques, the study models relationships between financial institutions to detect vulnerabilities and cascading failures. Case studies from historical financial crises demonstrate the effectiveness of AI in providing early warnings and insights into risk contagion. The paper concludes with recommendations for integrating AI into regulatory frameworks to enhance global financial stability.
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