IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-64273-9_33.html
   My bibliography  Save this book chapter

Variable Selection and Asymmetric Links to Predict Credit Card Fraud

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Francesco Giordano

    (Università degli Studi di Salerno)

  • Michele La Rocca

    (Università degli Studi di Salerno)

  • Marcella Niglio

    (Università degli Studi di Salerno)

  • Marialuisa Restaino

    (Università degli Studi di Salerno)

Abstract

Credit card fraud identification is a challenging problem for different reasons: it needs to be suddenly detected; it is based on the use of huge data sets that have to be properly managed; the number of fraudulent transactions is definitely lower than the number of genuine transactions and then, this imbalance requires the use of proper statistical models. Here we discuss how the data reduction, performed through the variable selection, can be combined with the use of Generalized Linear Models with asymmetric link functions which are able to handle imbalanced data. We illustrate how these theoretical results can be used for credit card fraud-detection purposes.

Suggested Citation

  • Francesco Giordano & Michele La Rocca & Marcella Niglio & Marialuisa Restaino, 2024. "Variable Selection and Asymmetric Links to Predict Credit Card Fraud," Springer Books, in: Marco Corazza & Frédéric Gannon & Florence Legros & Claudio Pizzi & Vincent Touzé (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 198-204, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-64273-9_33
    DOI: 10.1007/978-3-031-64273-9_33
    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:sprchp:978-3-031-64273-9_33. 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.