IDEAS home Printed from
   My bibliography  Save this article

Credit Card Fraud Detection using Deep Learning Techniques


  • Oona VOICAN


The objective of this paper is to identify credit card fraud and this topic can be solved with the help of advanced machine learning and deep learning techniques. Due to the fact that credit card fraud is a serious worldwide problem, we have chosen to create a model for detecting im-poster scams by using deep neural networks. The purpose is to understand, determine and learn the normal behavior of the user and more precisely the detection of identity fraud. Each person has a trading pattern, uses certain operating systems, has a specific time to complete the transaction and spends large amounts of money usually within a certain time range. Transac-tions made by a certain user have a certain pattern, which can be identified with the help of neural networks. Machine learning involves teaching computers to recognize patterns in data in the same way as our brains do. Deep learning is just a subfield of machine learning that deals with algorithms inspired by the structure and function of the brain. Deep learning at the core is the ability to form higher and higher level of abstractions of representations in data and raw patterns. The data used to train the model is real, and it will be processed using the one-hot encoding method, so that categorical data/variables can be used by the machine learning algorithm.

Suggested Citation

  • Oona VOICAN, 2021. "Credit Card Fraud Detection using Deep Learning Techniques," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 25(1), pages 70-85.
  • Handle: RePEc:aes:infoec:v:25:y:2021:i:1:p:70-85

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Leonard, Kevin J., 1995. "The development of a rule based expert system model for fraud alert in consumer credit," European Journal of Operational Research, Elsevier, vol. 80(2), pages 350-356, January.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Tzu-Hsuan Lin & Jehn-Ruey Jiang, 2021. "Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest," Mathematics, MDPI, vol. 9(21), pages 1-16, October.
    2. Ariyo Olorunmeye Omolade & Rasheed Gbenga Jimoh, 2023. "A Comparative Analysis of Machine Learning Algorithms on Card-Based Financial Fraud Detection with Infusion of Sigmoid and Isotonic Functions," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 10(11), pages 319-335, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Philippe Bernard & Najat El Mekkaoui De Freitas & Bertrand B. Maillet, 2022. "A financial fraud detection indicator for investors: an IDeA," Annals of Operations Research, Springer, vol. 313(2), pages 809-832, June.
    2. Galeotti, Marcello & Rabitti, Giovanni & Vannucci, Emanuele, 2020. "An evolutionary approach to fraud management," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1167-1177.
    3. Choicharoon, Aritad & Hodgett, Richard & Summers, Barbara & Siraj, Sajid, 2024. "Hit or miss: A decision support system framework for signing new musical talent," European Journal of Operational Research, Elsevier, vol. 312(1), pages 324-337.


    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:aes:infoec:v:25:y:2021:i:1:p:70-85. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Paul Pocatilu (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.