IDEAS home Printed from https://ideas.repec.org/a/aza/jfc000/y2024v7i3p202-221.html
   My bibliography  Save this article

Intelligence-led analytics for anti-financial crime compliance

Author

Listed:
  • Bostel, Ashley

    (Head of Financial Crime Analytics, NatWest Group, UK)

Abstract

Financial institutions are continually looking for novel ways to improve the effectiveness of their anti-financial crime controls. Increasingly, they are turning to analytics to do this by embedding analytics into their anti-financial crime processes. Historically, analytics was used to gather information and provide summarisation and insights to support assessment and optimisation of financial crime controls, but advanced analytics and machine learning solutions have transformed the way financial crime is detected and investigated. In particular, the use of ‘intelligence-led’ analytics can improve the effectiveness of an institution's anti-financial crime controls by guiding them with additional financial crime intelligence information delivered through analytics solutions.

Suggested Citation

  • Bostel, Ashley, 2024. "Intelligence-led analytics for anti-financial crime compliance," Journal of Financial Compliance, Henry Stewart Publications, vol. 7(3), pages 202-221, April.
    • Handle: RePEc:aza:jfc000:y:2024:v:7:i:3:p:202-221
    as

    Download full text from publisher

    File URL: https://hstalks.com/article/8332/download/
    Download Restriction: Requires a paid subscription for full access.
    File URL: https://hstalks.com/article/8332/
    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

    intelligence-led analytics; analytics; financial crime; compliance; financial crime analysis; anti-financial crime analytics; transaction monitoring; financial crime investigation; machine learning; network analytics; big data; cloud computing; artificial intelligence; generative AI;
    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
    • K2 - Law and Economics - - Regulation and Business 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:jfc000:y:2024:v:7:i:3:p:202-221. 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.