Abstract
With the development of technology, e-commerce became an essential part of an individual’s life, where individuals could easily purchase and sell products over the internet. However, fraud attempts; specifically credit card fraudulent attacks are rapidly increasing. Cards may potentially be stolen; fake records are being used and credit cards are subject to being hacked. Artificial Intelligence techniques tackle these credit card fraud attacks, by identifying patterns that predict false transactions. Both Machine Learning and Deep Learning models are used to detect and prevent fraud attacks. Machine Learning techniques provide positive results only when the dataset is small and do not have complex patterns. In contrast, Deep Learning deals with huge and complex datasets. However, most of the existing studies on Deep Learning have used private datasets, and therefore, did not provide a broad comparative study. This paper aims to improve the detection of credit card fraud attacks using Long Short-Term Memory Recurrent Neural Network (LSTM RNN) with a public dataset. Our proposed model proved to be effective. It achieved an accuracy rate of 99.4% which is higher compared to other existing Machine and Deep Learning techniques.
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Ackowledgment
We would like to acknowledge the Artificial Intelligence and Data Analytics (AIDA) Lab, Prince Sultan University, Riyadh, Saudi Arabia for supporting this work.
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Marie-Sainte, S.L., Alamir, M.B., Alsaleh, D., Albakri, G., Zouhair, J. (2020). Enhancing Credit Card Fraud Detection Using Deep Neural Network. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1229. Springer, Cham. https://doi.org/10.1007/978-3-030-52246-9_21
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