Application of Artificial Intelligence and Machine Learning in the Conduct of Monetary Policy by Central Banks Cover Image

Application of Artificial Intelligence and Machine Learning in the Conduct of Monetary Policy by Central Banks
Application of Artificial Intelligence and Machine Learning in the Conduct of Monetary Policy by Central Banks

Author(s): Sonya Georgieva
Subject(s): Economy, National Economy, Financial Markets, Public Finances, ICT Information and Communications Technologies
Published by: Институт за икономически изследвания при Българска академия на науките
Keywords: artificial intelligence; big data; machine learning; central banks; monetary policy; economic uncertainty

Summary/Abstract: Over the past few years, artificial intelligence (AI) and machine learning (ML) have become increasingly important in central banks’ policy-making and monetary policy-making processes. The global financial crisis of 2008-2009, the COVID-19 pandemic, as well as various other episodes of high economic uncertainty since the turn of the millennium have adjusted central banks to a number of serious challenges and have led to the expansion of these mandates and emerging and exploiting new and extensive data. The study briefly notes on this as a big database (big data) and applications of AI/ML-based techniques that can provide support on monetary policy decisions, especially during times of uncertainty in the economy, referring to the latest research in this area. Also, concrete examples based on the creation of big data and AI/ML techniques applied in the activities of the European Central Bank and other central banks in Europe and the rest of the world are considered and analyzed. The analysis reveals that big data and AI/ML methods have demonstrated successful utility in conducting monetary policy by central banks. Although useful as a complement, these tools cannot be regarded as replacements for conventional data and methods due to issues related to statistics, the ability to interpret outcomes and ethical dilemmas.

  • Issue Year: 2023
  • Issue No: 8
  • Page Range: 177-199
  • Page Count: 23
  • Language: English