IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-256-9_77.html

Credit Card Fraud Detection Prediction: Machine Learning Algorithm

In: Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023)

Author

Listed:
  • Yi Qu

    (Wenzhou-Kean University, Department of Finance)

  • Jiani Jin

    (Wenzhou-Kean University, Department of Mathematics)

Abstract

Someone commits payment fraud when they obtain the payment information of another person and use it for unauthorized transactions or purchases. Owing to the ease and convenience of e-commerce, digital purchasing is becoming increasingly popular today and because of the convenience of online shopping, many individuals prefer to shop online. This has resulted in a substantial rise in credit card fraud. Detecting and preventing payment fraud is difficult because the standard rules-based anti-fraud systems deployed by banks cannot manage the high volume of online transactions. This creates unique difficulties for banks and a substantial increase in losses. Therefore, it is crucial to effectively identify and eliminate fraud. In our research, we use machine learning methods to construct models that can detect and analyze fraudulent payments. We primarily employ the Generalized Linear, Decision Tree, Gradient Boosting, and Naive Bayes Models, and determine that the Generalized Linear Model is the most effective.

Suggested Citation

  • Yi Qu & Jiani Jin, 2024. "Credit Card Fraud Detection Prediction: Machine Learning Algorithm," Advances in Economics, Business and Management Research, in: Suhaiza Hanim Binti Dato Mohamad Zailani & Kosga Yagapparaj & Norhayati Zakuan (ed.), Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023), pages 760-767, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-256-9_77
    DOI: 10.2991/978-94-6463-256-9_77
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:spr:advbcp:978-94-6463-256-9_77. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.