IDEAS home Printed from https://ideas.repec.org/a/arh/jmabec/v94y2020i5-6p219-230.html
   My bibliography  Save this article

The application of Artificial Intelligence in banks in the context of the three lines of defence model

Author

Listed:
  • Alette Tammenga

    (Vrije Universiteit, Amersfoort, Netherlands)

Abstract

The use of Artificial Intelligence (AI) and Machine Learning (ML) techniques within banks is rising, especially for risk management purposes. The question arises whether the commonly used three lines of defence model is still fit for purpose given these new techniques, or if changes to the model are necessary. If AI and ML models are developed with involvement of second line functions, or for pure risk management purposes, independent oversight should be performed by a separate function. Other prerequisites to apply AI and ML in a controlled way are sound governance, a risk framework, an oversight function and policies and processes surrounding the use of AI and ML.

Suggested Citation

  • Alette Tammenga, 2020. "The application of Artificial Intelligence in banks in the context of the three lines of defence model," Maandblad Voor Accountancy en Bedrijfseconomie Articles, Maandblad Voor Accountancy en Bedrijfseconomie, vol. 94(5-6), pages 219-230, June.
  • Handle: RePEc:arh:jmabec:v:94:y:2020:i:5-6:p:219-230
    DOI: 10.5117/mab.94.47158
    as

    Download full text from publisher

    File URL: https://mab-online.nl/article/47158/
    Download Restriction: no

    File URL: https://libkey.io/10.5117/mab.94.47158?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:arh:jmabec:v:94:y:2020:i:5-6:p:219-230. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Teodor Georgiev (email available below). General contact details of provider: https://mab-online.nl/ .

    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.