BIS Bulletin No 84 Artificial intelligence in central banking Douglas Araujo, Sebastian Doerr, Leonardo Gambacorta and Bruno Tissot 23 January 2024 BIS Bulletins are written by staff members of the Bank for International Settlements, and from time to time by other economists, and are published by the Bank. The papers are on subjects of topical interest and are technical in character. The views expressed in them are those of their authors and not necessarily the views of the BIS. The authors are grateful to Bryan Hardy and Galo Nuño for comments, Ilaria Mattei and Krzysztof Zdanowicz for excellent research assistance, and to Louisa Wagner for administrative support. The editor of the BIS Bulletin series is Hyun Song Shin. This publication is available on the BIS website (www.bis.org). © Bank for International Settlements 2024. All rights reserved. Brief excerpts may be reproduced or translated provided the source is stated. ISSN: 2708-0420 (online) ISBN: 978-92-9259-738-2 (online) BIS Bulletin 1 Douglas AraujoDouglas.Araujo@bis.orgSebastian DoerrSebastian.Doerr@bis.orgLeonardo GambacortaLeonardo.Gambacorta@bis.orgBruno TissotBruno.Tissot@bis.org Artificial intelligence in central banking Long before artificial intelligence (AI) became a focal point of popular commentary and widespread fascination, central banks were early adopters of machine learning methods to obtain valuable insights for statistics, research and policy (Doerr et al (2021), Araujo et al (2022, 2023)). The greater capabilities and performance of the new generation of machine learning techniques open up further opportunities. Yet harnessing these requires central banks to build up the necessary infrastructure and expertise. Central banks also need to address concerns about data quality and privacy as well as risks emanating from dependence on a few providers. This Bulletin first provides a brief summary of concepts in the machine learning and AI space. It then discusses central bank use cases in four areas: (i) information collection and the compilation of official statistics; (ii) macroeconomic and financial analysis to support monetary policy; (iii) oversight of payment systems; and (iv) supervision and financial stability. The Bulletin also summarises the lessons learned and the opportunities and challenges arising from the use of machine learning and AI. It concludes by discussing how central bank cooperation can play a key role going forward. Overview of machine learning methods and AI Broadly speaking, machine learning comprises the set of techniques designed to extract information from data, especially with a view to making predictions. Machine learning can be seen as an outgrowth of traditional statistical and econometric techniques, although it does not rely on a pre-specified model or on statistical assumptions such as linearity or normality. The process of fitting a machine learn...
