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Credit Card Fraud Detection using Deep Learning Techniques

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  • Oona VOICAN

Abstract

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
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    References listed on IDEAS

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    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.
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    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.

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