MONETARY POLICY MECHANISMS IN THE ERA OF ARTIFICIAL INTELLIGENCE: OPTIMIZING MACRO-FINANCIAL FRAMEWORKS FOR GROWTH

This research explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) architectures within macro-financial frameworks to optimize contemporary monetary regulation in emerging and transition economies. Modern macroeconomic landscapes are increasingly characterized by high-frequency structural shocks, rendering traditional linear econometric models progressively inefficient. Utilizing a comparative efficiency matrix, this research analyzes how AI-driven predictive analytics stabilize the monetary transmission mechanism by mitigating systemic policy and recognition lags. The study demonstrates that by substituting lagging indicators with real-time, non-linear alternative data streams—such as digital transaction velocities and automated consumer sentiment scraping—deep learning frameworks significantly minimize forecasting errors in inflation-targeting regimes. Consequently, this computational optimization dampens inflation volatility, reinforces exchange rate stability, and anchors long-term public expectations. The final simulations suggest that embedding algorithmic models into central banking operations establishes a highly predictable macroeconomic environment, serving as a fundamental catalyst for private sector credit expansion and sustainable GDP growth.