IDEAS home Printed from https://ideas.repec.org/a/igg/jiit00/v17y2021i4p1-26.html
   My bibliography  Save this article

Evaluation of Data Imbalance Algorithms on the Prediction of Credit Card Fraud

Author

Listed:
  • Godlove Otoo

    (Ashesi University, Ghana)

  • Justice Kwame Appati

    (University of Ghana, Ghana)

  • Winfred Yaokumah

    (University of Ghana, Ghana)

  • Michael Agbo Tettey Soli

    (University of Ghana, Ghana)

  • Stephane Jnr Nwolley

    (Npontu Technology, Ghana)

  • Julius Yaw Ludu

    (University of Ghana, Ghana)

Abstract

Credit card fraud has been on the rise for some years now after the introduction of card payment systems. To curb this menace, computational methods have been proposed. Unfortunately, the data available for such a study is highly skewed resulting in the data imbalance problem. In this study, the authors investigate the performance of some selected data imbalance algorithms employed in the prediction of credit card fraud. A dataset from Kaggle containing 284,315 genuine transactions and 492 fraudulent transactions was used for the evaluation. The machine learning algorithms deployed for the study is logistic regression, naïve bayes, and the k-nearest neighbour algorithm with F1 score and precision-recall area under the curve (PR AUC) as the metric. Numerical assessment of the performance of the adopted algorithm gave a rate of 82.5% and 81%, respectively, using neighbourhood cleaning rule for undersampling.

Suggested Citation

  • Godlove Otoo & Justice Kwame Appati & Winfred Yaokumah & Michael Agbo Tettey Soli & Stephane Jnr Nwolley & Julius Yaw Ludu, 2021. "Evaluation of Data Imbalance Algorithms on the Prediction of Credit Card Fraud," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 17(4), pages 1-26, October.
    • Handle: RePEc:igg:jiit00:v:17:y:2021:i:4:p:1-26
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIIT.289967
    Download Restriction: no
    ---><---

    More about this item

    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:igg:jiit00:v:17:y:2021:i:4:p:1-26. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.