Machine learning applications in central banking

Douglas Araujo, Giuseppe Bruno, Juri Marcucci, Rafael Schmidt and Bruno Tissot
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Douglas Araujo: Bank for International Settlements (BIS), Switzerland
Giuseppe Bruno: Bank of Italy, Italy
Rafael Schmidt: Bank for International Settlements (BIS), Switzerland
Bruno Tissot: Bank for International Settlements (BIS), Switzerland

Journal of AI, Robotics & Workplace Automation, 2023, vol. 2, issue 3, 271-293

Abstract: Central banks are deploying machine learning (ML) across a variety of use cases, reflecting its potential and usefulness in dealing with an increasingly complex environment. The new techniques can help gather more and better information, which is essential for central banks that rely heavily on data. In addition, a key issue is to make sense of the wealth of data available to derive useful analyses on specific economic and financial situations and, in turn, ensure that the insights gained can effectively back the conduct of evidence-based policies. Yet the deployment of the new tools requires further modifications in central banks’ current operational processes and collaboration models, calling for close cooperation between core IT experts, data scientists and business specialists. It also puts a premium on promoting cooperation between central banks through the sharing of national use cases and on drawing relevant lessons from the experiences observed outside the public community.

Keywords: central banks; machine learning; big data; evidence-based policy; information management; international cooperation (search for similar items in EconPapers)
JEL-codes: G2 M15 (search for similar items in EconPapers)
Date: 2023
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