IDEAS home Printed from https://ideas.repec.org/h/bis/bisifc/64-05.html
   My bibliography  Save this book chapter

Experiences, essentials and perspectives for data science in the hearts of central banks and supervisors: a case study of the Dutch central bank

In: Data science in central banking: enhancing the access to and sharing of data

Author

Listed:
  • Patty Duijm
  • Iman van Lelyveld

Abstract

No abstract is available for this item.

Suggested Citation

  • Patty Duijm & Iman van Lelyveld, 2025. "Experiences, essentials and perspectives for data science in the hearts of central banks and supervisors: a case study of the Dutch central bank," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: enhancing the access to and sharing of data, volume 64, Bank for International Settlements.
    • Handle: RePEc:bis:bisifc:64-05
    as

    Download full text from publisher

    File URL: https://www.bis.org/ifc/publ/ifcb64_05.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Dorinth van Dijk & Jasper de Winter, 2023. "Nowcasting GDP using tone-adjusted time varying news topics: Evidence from the financial press," Working Papers 766, DNB.
    3. Hüser, Anne-Caroline & Kok, Christoffer, 2019. "Mapping bank securities across euro area sectors: comparing funding and exposure networks," Working Paper Series 2273, European Central Bank.
    4. Jean-Charles Bricongne & Baptiste Meunier & Thomas Pical, 2021. "Can satellite data on air pollution predict industrial production?," Working papers 847, Banque de France.
    Full references (including those not matched with items on IDEAS)

    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. 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. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1181-1200, September.
    3. d'Aspremont, Alexandre & Ben Arous, Simon & Bricongne, Jean-Charles & Lietti, Benjamin & Meunier, Baptiste, 2025. "Satellites turn “concrete”: Tracking cement with satellite data and neural networks," Journal of Econometrics, Elsevier, vol. 249(PC).
    4. Jorge Tejero, 2022. "Unwrapping black box models A case study in credit risk," Revista de Estabilidad Financiera, Banco de España, issue Otoño.
    5. 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.
    6. Farmer, J. Doyne & Kleinnijenhuis, Alissa & Nahai-Williamson, Paul & Wetzer, Thom, 2020. "Foundations of system-wide financial stress testing with heterogeneous institutions," INET Oxford Working Papers 2020-14, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    7. Andres Alonso-Robisco & Jose Carbo & Emily Kormanyos & Elena Triebskorn, 2025. "Houston, we have a problem: can satellite information bridge the climate-related data gap?," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Addressing climate change data needs: the central banks' contribution, volume 63, Bank for International Settlements.
    8. Bricongne, Jean-Charles & Meunier, Baptiste & Pouget, Sylvain, 2023. "Web-scraping housing prices in real-time: The Covid-19 crisis in the UK," Journal of Housing Economics, Elsevier, vol. 59(PB).
    9. Adämmer, Philipp & Prüser, Jan & Schüssler, Rainer A., 2025. "Forecasting macroeconomic tail risk in real time: Do textual data add value?," International Journal of Forecasting, Elsevier, vol. 41(1), pages 307-320.
    10. Wishnu Badrawani, 2025. "An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia," Papers 2506.10369, arXiv.org.
    11. 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.
    12. Artiga Gonzalez, Tanja & Dyakov, Teodor & Inhoffen, Justus & Wipplinger, Evert, 2024. "Crowding of international mutual funds," Journal of Banking & Finance, Elsevier, vol. 164(C).
    13. 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).
    14. Jorge Tejero, 2022. "Unwrapping black box models A case study in credit risk," Financial Stability Review, Banco de España, issue Autumn.
    15. 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.
    16. Mitja Steinbacher & Matthias Raddant & Fariba Karimi & Eva Camacho Cuena & Simone Alfarano & Giulia Iori & Thomas Lux, 2021. "Advances in the agent-based modeling of economic and social behavior," SN Business & Economics, Springer, vol. 1(7), pages 1-24, July.
    17. Martijn Boermans, 2022. "A literature review of securities holdings statistics research and a practitioner’s guide," Working Papers 757, DNB.
    18. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
    19. Pedro Guerra & Mauro Castelli, 2021. "Machine Learning Applied to Banking Supervision a Literature Review," Risks, MDPI, vol. 9(7), pages 1-24, July.
    20. 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.

    More about this item

    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:bisifc:64-05. 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.