IDEAS home Printed from https://ideas.repec.org/a/eme/jmlcpp/jmlc-04-2022-0058.html
   My bibliography  Save this article

Financial fraud detection: the use of visualization techniques in credit card fraud and money laundering domains

Author

Listed:
  • Mark E. Lokanan

Abstract

Purpose - This paper aims to reviews the literature on applying visualization techniques to detect credit card fraud (CCF) and suspicious money laundering transactions. Design/methodology/approach - In surveying the literature on visual fraud detection in these two domains, this paper reviews: the current use of visualization techniques, the variations of visual analytics used and the challenges of these techniques. Findings - The findings reveal how visual analytics is used to detect outliers in CCF detection and identify links to criminal networks in money laundering transactions. Graph methodology and unsupervised clustering analyses are the most dominant types of visual analytics used for CCF detection. In contrast, network and graph analytics are heavily used in identifying criminal relationships in money laundering transactions. Originality/value - Some common challenges in using visualization techniques to identify fraudulent transactions in both domains relate to data complexity and fraudsters’ ability to evade monitoring mechanisms.

Suggested Citation

  • Mark E. Lokanan, 2022. "Financial fraud detection: the use of visualization techniques in credit card fraud and money laundering domains," Journal of Money Laundering Control, Emerald Group Publishing Limited, vol. 26(3), pages 436-444, May.
    • Handle: RePEc:eme:jmlcpp:jmlc-04-2022-0058
    DOI: 10.1108/JMLC-04-2022-0058
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/JMLC-04-2022-0058/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/JMLC-04-2022-0058/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1108/JMLC-04-2022-0058?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
    ---><---

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

    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:eme:jmlcpp:jmlc-04-2022-0058. 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: Emerald Support (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.