Big data and machine learning in central banking

Sebastian Doerr, Leonardo Gambacorta and Jose Maria Serena Garralda

No 930, BIS Working Papers from Bank for International Settlements

Abstract: This paper reviews the use of big data and machine learning in central banking, leveraging on a recent survey conducted among the members of the Irving Fischer Committee (IFC). The majority of central banks discuss the topic of big data formally within their institution. Big data is used with machine learning applications in a variety of areas, including research, monetary policy and financial stability. Central banks also report using big data for supervision and regulation (suptech and regtech applications). Data quality, sampling and representativeness are major challenges for central banks, and so is legal uncertainty around data privacy and confidentiality. Several institutions report constraints in setting up an adequate IT infrastructure and in developing the necessary human capital. Cooperation among public authorities could improve central banks' ability to collect, store and analyse big data.

Keywords: big data; central banks; machine learning; artificial intelligence; data science (search for similar items in EconPapers)
JEL-codes: G17 G18 G23 G32 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2021-03
New Economics Papers: this item is included in nep-big, nep-cba, nep-cmp and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:bis:biswps:930

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