In the face of accelerating global economic complexity, central banks are increasingly exploring the potential of artificial intelligence (AI) and machine learning (ML) to enhance financial stability. This paper examines the integration of AI and ML into central bank strategies, focusing on their applications in systemic risk assessment, real-time macroprudential surveillance, monetary policy optimization, fraud detection, and predictive analytics for financial crises. The research synthesizes recent developments, case studies, and central bank initiatives globally, providing empirical insights into the efficacy, limitations, and ethical concerns associated with AI adoption. Findings reveal that AI-powered tools enhance early warning systems, strengthen regulatory oversight, and enable faster decision-making, yet they also introduce risks related to data governance, model interpretability, and systemic dependence. This study advocates for a balanced framework wherein central banks can harness AI's benefits while mitigating its unintended consequences. The paper concludes with strategic recommendations for policymakers, regulators, and technologists to collaboratively shape resilient, transparent, and adaptive financial infrastructures
Download
Download XML