Algorithmic Monetary Neutralization: Central Bank Policy Transmission in AI-Driven Financial Markets

Abstract

This article investigates how effective traditional monetary policies can be amid monetary systems, which are increasingly decelerated by technologies such as robots, bots, and digital finance. A combination of a literature review of the monetary policy channel, an empirical assessment of case studies that have been explored between 2015 and 2025, and the newly developed theory of algorithmic monetary neutrality (AMN) forms the basis of this paper's conclusion that the primary channels through which monetary authorities apply their influence on economic output, inflation level, credit funds, prices of assets, exchange rates, and expectations are substantially weakened by high-speed transactions taking place in the market. AI technologies facilitate the anticipation of policy actions, reduce response time in making policy decisions, increase fluctuations in the market due to correlations that exist, as well as evade conventional banking by means of the usage of fintech solutions, point of sale, and DeFi technologies. The analysis shows how important the expectations channel has become but how ever more preempted and structurally damaged the interest and credit channels have become. With references to historical case studies that cover events from Federal Reserve tightening measures of 2018 to the yield curve control in 2024 by Bank of Japan, the paper proves that the monetary policy is still effective, but with reduced time lag, lower predictability, and higher risk of overshooting and market manipulation. The paper suggests that central banks must enhance their operations through the use of AI technology along with the adoption of new surveillance capabilities to implement digital currencies along with the interest rate instruments.