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Big data and machine learning in central banking

Author

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  • Sebastian Doerr
  • Leonardo Gambacorta
  • José María Serena Garralda

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.

Suggested Citation

  • Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
    • Handle: RePEc:bis:biswps:930
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    Cited by:

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    2. Douglas Kiarelly Godoy de Araujo, 2023. "gingado: a machine learning library focused on economics and finance," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59, Bank for International Settlements.
    3. Jorge Tejero, 2022. "Unwrapping black box models A case study in credit risk," Revista de Estabilidad Financiera, Banco de España, issue NOV.
    4. Sonan Memon, 2022. "Inflation in Pakistan: High-Frequency Estimation and Forecasting," PIDE-Working Papers 2022:12, Pakistan Institute of Development Economics.
    5. Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.
    6. Pedro Guerra & Mauro Castelli, 2021. "Machine Learning Applied to Banking Supervision a Literature Review," Risks, MDPI, vol. 9(7), pages 1-24, July.
    7. Jorge Tejero, 2022. "Unwrapping black box models A case study in credit risk," Financial Stability Review, Banco de España, issue NOV.
    8. Daniel Stempel & Johannes Zahner, 2022. "DSGE Models and Machine Learning: An Application to Monetary Policy in the Euro Area," MAGKS Papers on Economics 202232, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    9. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023. "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers 34/2023, Deutsche Bundesbank.
    10. Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2022. "Learning from revisions: an algorithm to detect errors in banks’ balance sheet statistical reporting," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4025-4059, December.
    11. Sonya Georgieva, 2023. "Application of Artificial Intelligence and Machine Learning in the Conduct of Monetary Policy by Central Banks," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 8, pages 177-199.

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    More about this item

    Keywords

    big data; central banks; machine learning; artificial intelligence; data science;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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