2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 17 , Issue 1 , PP: 209-220, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ola Imran Obaid 1 * , Ali Yakoob Al-Sultan 2
Doi: https://doi.org/10.54216/FPA.170115
The increasing use of credit cards, especially for online payments, has led to a significant increase in fraud involving credit card payment technologies. Financial companies must enhance fraud detection systems to mitigate significant losses. This study introduces a methodology for developing a credit card fraud detection system that uses the Synthetic Minority Oversampling Technique (SMOTE) to address an imbalanced dataset problem and an attention layer to identify important features in the input sequence, two long short-term memory (LSTM) layers modeling long-run dependencies within a sequence of transactions, a dropout layer that neglects values lower than 0.3, and two dense layers, which allows enhancing the accuracy of prediction of fraudulent transactions. When implemented, the proposed system achieves an accuracy of 0.9434% on the IEEE dataset, 0.9850% on the Banksim dataset, and 0.9757% on the European dataset. This methodology shows improvements in fraud detection, emphasizing its ability to enhance financial security systems and reduce misclassification in credit card transactions.
Fraud Detection , Credit Cards , Deep Learning , SMOTE , Attention Mechanism , Long short-Term Memory
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