Abstract The emergence of artificial intelligence (AI) marks a transformative shift in central banking, presenting new opportunities for economic forecasting, financial supervision, and operational efficiency. Traditionally, central banks have depended on structured frameworks and statistical models, but AI technologies—particularly machine learning and generative models—are redefining these core functions. AI models enhance central banks’ ability to predict economic trends, detect financial irregularities, and streamline administrative tasks, supporting informed decision-making in complex, data-driven environments. Despite these advantages, AI adoption introduces significant challenges, including data security concerns, systemic risk, and algorithmic bias. Central banks must navigate these risks through robust data governance, ethical AI frameworks, and strategic human capital investments. This article examines AI's applications, risks, and strategic requirements in central banking, illustrating how early adopters like the European Central Bank and BIS Innovation Hub leverage AI for forecasting and regulatory compliance. By fostering international collaboration and transparency, central banks can responsibly harness AI’s potential to strengthen financial stability and maintain public trust. This balanced approach underscores AI’s role in enabling central banks to adapt to an evolving global financial landscape while safeguarding ethical standards and regulatory integrity.
Al-Maadeed et al. (2026) studied this question.