The Algorithmic Mandate Opportunities, Risks, and Governance of Artificial Intelligence in Modern Central Banking
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As central banks navigate an era of unprecedented data complexity, artificial intelligence (AI) has emerged as a transformative tool for enhancing monetary policy and financial oversight. This paper examines the shifting methodological landscape of central banking, exploring how AI- driven frameworks—including transformer-based text analysis, agent-based modeling, and machine learning-enhanced forecasting—are outperforming traditional econometric models. Empirical evidence suggests significant gains in "nowcasting" economic shifts, extracting narrative policy surprises, and predicting market volatility. However, the integration of AI introduces profound institutional and systemic risks. The "black box" nature of deep learning threatens the transparency and accountability essential to central bank legitimacy. Furthermore, the paper identifies emerging threats such as "model monocultures," where synchronized AI-driven market expectations may trigger self-fulfilling economic prophecies. While 89% of surveyed central banks have already adopted AI tools, significant hurdles remain regarding data quality, cybersecurity, and third-party dependencies. The study concludes that while AI is no longer a novelty but a strategic necessity, its success depends on hybrid "human-AI" governance frameworks that balance predictive power with institutional credibility and financial stability.
Keywords
Accounting Automation, Artificial Intelligence (AI), Central Banks, Financial Systems, Fraud Detection