IDEAS home Printed from https://ideas.repec.org/a/aza/jsoc00/y2020v12i2p102-115.html
   My bibliography  Save this article

How AI is changing operations: From settlement optimisation to automating risk monitoring

Author

Listed:
  • Brownlee, Tony

    (President, Kingland, USA)

  • Sommerfeld, Jesse

    (Head of Data Science, Kingland, USA)

  • Hansen, Kyle

    (Head of AI Engineering, Kingland, USA)

Abstract

Banking executives continue to evaluate new ways to incorporate artificial intelligence (AI) into the middle and back office to improve efficiency, mitigate risk and reduce cost. Even with these efforts, a Business Insider report1 estimates the aggregate potential cost savings from AI applications at US$248bn by 2023 in the middle and back office. In this paper, the authors discuss two uses of AI that are experiencing investments from firms: settlement optimisation (maximising daily resolved trades by using AI to optimise the order that transactions are settled) and automating risk monitoring (using natural language processing to systematically review and filter content relevant to a specific risk-monitoring purpose).

Suggested Citation

  • Brownlee, Tony & Sommerfeld, Jesse & Hansen, Kyle, 2020. "How AI is changing operations: From settlement optimisation to automating risk monitoring," Journal of Securities Operations & Custody, Henry Stewart Publications, vol. 12(2), pages 102-115, March.
    • Handle: RePEc:aza:jsoc00:y:2020:v:12:i:2:p:102-115
    as

    Download full text from publisher

    File URL: https://hstalks.com/article/5534/download/
    Download Restriction: Requires a paid subscription for full access.
    File URL: https://hstalks.com/article/5534/
    Download Restriction: Requires a paid subscription for full access.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    settlement optimisation; text analytics; risk monitoring; natural language processing; text summarisation; content collection; robotic process automation; event logging;
    All these keywords.

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit
    • K22 - Law and Economics - - Regulation and Business Law - - - Business and Securities Law

    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:aza:jsoc00:y:2020:v:12:i:2:p:102-115. 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: Henry Stewart Talks (email available below). General contact details of provider: .

    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.