IDEAS home Printed from https://ideas.repec.org/p/bis/biswps/930.html
   My bibliography  Save this paper

Big data and machine learning in central banking

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.bis.org/publ/work930.pdf
    File Function: Full PDF document
    Download Restriction: no
    File URL: https://www.bis.org/publ/work930.htm
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sean Cao Robert H. Smith & Wei Jiang & Baozhong Yang J. Mack Robinson & Alan L Zhang & Tarun Ramadorai, 2023. "How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI," The Review of Financial Studies, Society for Financial Studies, vol. 36(9), pages 3603-3642.
    2. Pagano, Marco & Jappelli, Tullio, 1993. "Information Sharing in Credit Markets," Journal of Finance, American Finance Association, vol. 48(5), pages 1693-1718, December.
    3. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    4. D’Amuri, Francesco & Marcucci, Juri, 2017. "The predictive power of Google searches in forecasting US unemployment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
    5. Altig, Dave & Baker, Scott & Barrero, Jose Maria & Bloom, Nicholas & Bunn, Philip & Chen, Scarlet & Davis, Steven J. & Leather, Julia & Meyer, Brent & Mihaylov, Emil & Mizen, Paul & Parker, Nicholas &, 2020. "Economic uncertainty before and during the COVID-19 pandemic," Journal of Public Economics, Elsevier, vol. 191(C).
    6. Stephen Hansen & Michael McMahon, 2016. "Shocking Language: Understanding the Macroeconomic Effects of Central Bank Communication," NBER Chapters, in: NBER International Seminar on Macroeconomics 2015, National Bureau of Economic Research, Inc.
    7. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    8. Padilla, A. Jorge & Pagano, Marco, 2000. "Sharing default information as a borrower discipline device," European Economic Review, Elsevier, vol. 44(10), pages 1951-1980, December.
    9. Giulio Cornelli & Sebastian Doerr & Leonardo Gambacorta & Ouarda Merrouche, 2020. "Inside the regulatory sandbox: effects on fintech funding," BIS Working Papers 901, Bank for International Settlements.
    10. Hassan, Tarek & Hollander, Stephan & van Lent, Laurence & Schwedeler, Markus & Tahoun, Ahmed, 2020. "Firm-Level Exposure to Epidemic Diseases: Covid-19, SARS, and H1N1," CEPR Discussion Papers 14573, C.E.P.R. Discussion Papers.
    11. Sophia Chen & Ms. Deniz O Igan & Mr. Nicola Pierri & Mr. Andrea F Presbitero, 2020. "Tracking the Economic Impact of COVID-19 and Mitigation Policies in Europe and the United States," IMF Working Papers 2020/125, International Monetary Fund.
    12. Bruno Tissot & Timur Hülagü & Per Nymand-Andersen & Laura Comino Suarez, 2015. "Central banks' use of and interest in "big data"," IFC Reports 3, Bank for International Settlements.
    13. Daniel J. Lewis & Karel Mertens & James H. Stock, 2020. "Tracking the COVID-19 Economy with the Weekly Economic Index (WEI)," Liberty Street Economics 20200804, Federal Reserve Bank of New York.
    14. Charles I. Jones & Christopher Tonetti, 2020. "Nonrivalry and the Economics of Data," American Economic Review, American Economic Association, vol. 110(9), pages 2819-2858, September.
    15. Eleni Kalamara & Arthur Turrell & Chris Redl & George Kapetanios & Sujit Kapadia, 2022. "Making text count: Economic forecasting using newspaper text," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 896-919, August.
    16. Patrick C. Higgins, 2014. "GDPNow: A Model for GDP \"Nowcasting\"," FRB Atlanta Working Paper 2014-7, Federal Reserve Bank of Atlanta.
    17. Marlene Amstad & Leonardo Gambacorta & Chao He & Dora Xia, 2021. "Trade sentiment and the stock market: new evidence based on big data textual analysis of Chinese media," BIS Working Papers 917, Bank for International Settlements.
    18. Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor, 2022. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," International Journal of Forecasting, Elsevier, vol. 38(2), pages 596-612.
    19. Asger Lau Andersen & Emil Toft Hansen & Niels Johannesen & Adam Sheridan, 2022. "Consumer responses to the COVID‐19 crisis: evidence from bank account transaction data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(4), pages 905-929, October.
    20. Chen, S. & Doerr, S. & Frost, J. & Gambacorta, L. & Shin, H.S., 2023. "The fintech gender gap," Journal of Financial Intermediation, Elsevier, vol. 54(C).
    21. Carvalho, V & Garcia, Juan R. & Hansen, S. & Ortiz, A. & Rodrigo, T. & More, J. V. R., 2020. "Tracking the COVID-19 Crisis with High-Resolution Transaction Data," Cambridge Working Papers in Economics 2030, Faculty of Economics, University of Cambridge.
    22. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    23. Sebastian Doerr & Leonardo Gambacorta, 2020. "Identifying regions at risk with Google Trends: the impact of Covid-19 on US labour markets," BIS Bulletins 8, Bank for International Settlements.
    24. Scott R Baker & Robert A Farrokhnia & Steffen Meyer & Michaela Pagel & Constantine Yannelis & Jeffrey Pontiff, 0. "How Does Household Spending Respond to an Epidemic? Consumption during the 2020 COVID-19 Pandemic," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(4), pages 834-862.
    25. Rajashri Chakrabarti & Sebastian Heise & Davide Melcangi & Maxim L. Pinkovskiy & Giorgio Topa, 2020. "Did State Reopenings Increase Consumer Spending?," Liberty Street Economics 20200918b, Federal Reserve Bank of New York.
    26. Claudia Buch, 2019. "Building pathways for policy making with big data," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    27. Carlos Cantú & Gong Cheng & Sebastian Doerr & Jon Frost & Leonardo Gambacorta, 2020. "On health and privacy: technology to combat the pandemic," BIS Bulletins 17, Bank for International Settlements.
    28. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    29. Sebastian Doerr, 2019. "Unintended side effects: stress tests, entrepreneurship, and innovation," BIS Working Papers 823, Bank for International Settlements.
    30. Michael Bailey & Ruiqing Cao & Theresa Kuchler & Johannes Stroebel, 2018. "The Economic Effects of Social Networks: Evidence from the Housing Market," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2224-2276.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
    2. 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.
    3. 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.
    4. Jorge Tejero, 2022. "Unwrapping black box models A case study in credit risk," Revista de Estabilidad Financiera, Banco de España, issue Otoño.
    5. Sonan Memon, 2022. "Inflation in Pakistan: High-Frequency Estimation and Forecasting," PIDE-Working Papers 2022:12, Pakistan Institute of Development Economics.
    6. 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.
    7. Pedro Guerra & Mauro Castelli, 2021. "Machine Learning Applied to Banking Supervision a Literature Review," Risks, MDPI, vol. 9(7), pages 1-24, July.
    8. Jorge Tejero, 2022. "Unwrapping black box models A case study in credit risk," Financial Stability Review, Banco de España, issue Autumn.
    9. 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).
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    2. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
    3. van der Wielen, Wouter & Barrios, Salvador, 2021. "Economic sentiment during the COVID pandemic: Evidence from search behaviour in the EU," Journal of Economics and Business, Elsevier, vol. 115(C).
    4. Martin Baumgaertner & Johannes Zahner, 2021. "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics 202130, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    5. Abel Brodeur & David Gray & Anik Islam & Suraiya Bhuiyan, 2021. "A literature review of the economics of COVID‐19," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1007-1044, September.
    6. 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.
    7. David Bounie & Youssouf Camara & John Galbraith, 2020. "Consumers’ Mobility, Expenditure and Online-Offline Substitution Response to COVID-19: Evidence from French Transaction Data," Cahiers de recherche 14-2020, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    8. Hansen, Stephen & Davis, Steven & Seminario-Amez, Cristhian, 2020. "Firm-level Risk Exposures and Stock Returns in the Wake of COVID-19," CEPR Discussion Papers 15314, C.E.P.R. Discussion Papers.
    9. Fezzi, Carlo & Fanghella, Valeria, 2021. "Tracking GDP in real-time using electricity market data: Insights from the first wave of COVID-19 across Europe," European Economic Review, Elsevier, vol. 139(C).
    10. 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.
    11. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    12. George, Ammu & Li, Changtai & Lim, Jing Zhi & Xie, Taojun, 2021. "From SARS to COVID-19: The evolving role of China-ASEAN production network," Economic Modelling, Elsevier, vol. 101(C).
    13. John Gathergood & Fabian Gunzinger & Benedict Guttman-Kenney & Edika Quispe-Torreblanca & Neil Stewart, 2020. "Levelling Down and the COVID-19 Lockdowns: Uneven Regional Recovery in UK Consumer Spending," Papers 2012.09336, arXiv.org, revised Dec 2020.
    14. Di Bartolomeo, Giovanni & D'Imperio, Paolo & Felici, Francesco, 2022. "The fiscal response to the Italian COVID-19 crisis: A counterfactual analysis," Journal of Macroeconomics, Elsevier, vol. 73(C).
    15. Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," MPRA Paper 110703, University Library of Munich, Germany.
    16. Hodbod, Alexander & Hommes, Cars & Huber, Stefanie J. & Salle, Isabelle, 2021. "The COVID-19 consumption game-changer: Evidence from a large-scale multi-country survey," European Economic Review, Elsevier, vol. 140(C).
    17. Necmettin Alpay Koçak, 2020. "The Role of Ecb Speeches in Nowcasting German Gdp," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2020(2), pages 05-20.
    18. Martin O'Connell & Áureo de Paula & Kate Smith, 2021. "Preparing for a pandemic: spending dynamics and panic buying during the COVID‐19 first wave," Fiscal Studies, John Wiley & Sons, vol. 42(2), pages 249-264, June.
    19. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News media vs. FRED-MD for macroeconomic forecasting," Working Papers No 08/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    20. Aprigliano, Valentina & Emiliozzi, Simone & Guaitoli, Gabriele & Luciani, Andrea & Marcucci, Juri & Monteforte, Libero, 2023. "The power of text-based indicators in forecasting Italian economic activity," International Journal of Forecasting, Elsevier, vol. 39(2), pages 791-808.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bis:biswps:930. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Martin Fessler (email available below). General contact details of provider: https://edirc.repec.org/data/bisssch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.