IDEAS home Printed from https://ideas.repec.org/a/igg/jkbo00/v6y2016i1p60-73.html
   My bibliography  Save this article

A Hybrid Approach Using Maximum Entropy and Bayesian Learning for Detecting Delinquency in Financial Industry

Author

Listed:
  • Dharminder Kumar

    (Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, India)

  • Suman Arora

    (Department of Computer Science and Engineering, Hindu College of Engineering, Sonepat, India)

Abstract

The use of credit card has increased tremendously in the past few years because of the boom in the economy which has also resulted in the increase in the credit card fraud cases. Various leading banks and software development companies worldwide are taking serious measures to deal with the gravity of this situation. This paper proposes a framework for credit card fraud detection that will detect frauds using maximum entropy according to the irregular behavior of the customers in various transactions of credit card. The comparative study of above approach with existing approaches is also addressed. Results show the feasibility and validity of each approach.

Suggested Citation

  • Dharminder Kumar & Suman Arora, 2016. "A Hybrid Approach Using Maximum Entropy and Bayesian Learning for Detecting Delinquency in Financial Industry," International Journal of Knowledge-Based Organizations (IJKBO), IGI Global, vol. 6(1), pages 60-73, January.
    • Handle: RePEc:igg:jkbo00:v:6:y:2016:i:1:p:60-73
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJKBO.2016010105
    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:jkbo00:v:6:y:2016:i:1:p:60-73. 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.