Template-Type: ReDIF-Paper 1.0
Author-Name: Sebastian Doerr
Author-X-Name-First: Sebastian
Author-X-Name-Last: Doerr
Author-Name: Leonardo Gambacorta
Author-X-Name-First: Leonardo
Author-X-Name-Last: Gambacorta
Author-Name: José María Serena Garralda
Author-X-Name-First: José María
Author-X-Name-Last:  Serena Garralda
Title: Big data and machine learning in central banking
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.
Length: 26 pages
Creation-Date: 2021-03
File-URL: https://www.bis.org/publ/work930.pdf
File-Format: Application/pdf
File-Function: Full PDF document
File-URL: https://www.bis.org/publ/work930.htm
File-Format: text/html
Number: 930
Keywords: big data, central banks, machine learning, artificial intelligence, data science
Classification-JEL: G17, G18, G23, G32
Handle: RePEc:bis:biswps:930