The Function and Effect of Artificial Intelligence (AI) in Central Bank Monetary Policy Decision Making, Inflation Prediction, and Economic Forecasting
Year: 2024
Type: article
Abstract: Artificial Intelligence (AI) is rapidly transforming the landscape of economic policy-making, particularly within the operations of central banks.This research explores the function and impact of AI in enhancing the effectiveness of monetary policy decision-making, inflation prediction, and economic forecasting.Traditional forecasting models often struggle to keep pace with the growing complexity, volume, and velocity of economic data.AI, through machine learning algorithms and big data analytics, offers a solution by enabling central banks to process vast datasets, identify non-linear patterns, and generate real-time insights with increased accuracy and efficiency.The study investigates how AI-driven tools can support central banks in formulating timely and well-informed policy interventions aimed at maintaining price stability and promoting economic growth.By analysing diverse economic indicators such as interest rates, employment figures, consumer behaviour, and global financial trends, AI facilitates a more holistic understanding of economic dynamics.The research also examines case studies of AI adoption by leading central banks and evaluates the benefits, limitations, and ethical considerations of integrating AI into monetary policy frameworks.Furthermore, the study assesses the comparative performance of AI models versus traditional econometric approaches in predicting inflation and economic trends.It also highlights potential risks associated with AI, including model transparency, data bias, and regulatory challenges.Ultimately, this research provides valuable insights into how AI can reshape the future of central banking by enhancing policy precision, improving forecasting accuracy, and reinforcing financial stability in an increasingly data-driven global economy.
Author: Omkar Rajendra Nalla
Language: English
Domain: Social Sciences
SDG: Climate action
Open Access status: hybrid
OpenAlex ID: W4410185000