A Proposed Architecture for Real Time Credit Card Fraud Detection

  • Authors

    • Soumaya Ounacer
    • Soufiane Ardchir
    • Zahira Rachad
    • Mohamed Azouazi
    2018-12-06
    https://doi.org/10.14419/ijet.v7i4.32.23254
  • Big data, Hadoop, Machine learning, Real time credit card fraud detection, Spark, Storm.

  • The world has known a huge evolution especially in the field of e-commerce. Owing to this technology, everything has become available to the user the thing that resulted in a significant increase of the credit card use. As a consequence, it has become the most popular tool for online payment transactions. Nevertheless, the risk of credit card transactions constitutes a major problem as it plays a crucial role in criminal activities. Thanks to e-commerce, the online payment does not require the physical card or the cardholder's presence. However, it is a two faced coin; anyone who knows the details of a certain card can easily make fraud transactions and the cardholder comes to know only after the fraud transaction is carried out. When it comes to security, all actors are affected namely the cardholder, the bank and the merchants. Hence, the urgent need for a powerful system that allows fraud transactions to be detected and processed in real time. In the work at hand, we are going to propose a system that "shields" the credit card from any kind of fraud. In other words, we intend to create a system which detects the fraud based on big data technology; precisely Apache storm and Machine Learning in order to minimize the latency and to process transactions data in real time.

     

     

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  • How to Cite

    Ounacer, S., Ardchir, S., Rachad, Z., & Azouazi, M. (2018). A Proposed Architecture for Real Time Credit Card Fraud Detection. International Journal of Engineering & Technology, 7(4.32), 93-97. https://doi.org/10.14419/ijet.v7i4.32.23254