Articles
| Open Access |
DOI: Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems
Mohammad Iftekhar Ayub , Master of Science in Information Technology, Washington University of Science and Technology, USA. Biswanath Bhattacharjee , Department of Management Science and Quantitative Methods, Gannon University, USA. Pinky Akter , Master Of Science in Information Technology, Washington University of Science and Technology, USA. Mohammad Nasir Uddin , Masters of Business Administration, Major in Data Analytics, Westcliff University, USA. Arun Kumar Gharami , Master of science in computer science, Westcliff university, USA. Md Iftakhayrul Islam , MBA in Management Information Systems, International American University, USA. Shaidul Islam Suhan , MBA in Business analytics, International American University, USA. Md Sayem Khan , Master of Science in Project Management, Saint Francis College (SFC), Brooklyn, New York, USA. Lisa Chambugong , Department of Management Science and Quantitative Methods, Gannon University, USAAbstract
In this study, we present a deep learning-based approach for real-time credit card fraud detection in banking systems, with a primary focus on Long Short-Term Memory (LSTM) networks. Using a highly imbalanced credit card transaction dataset, we implemented comprehensive preprocessing, feature engineering, and model evaluation strategies to enhance the detection accuracy. Our experimental results reveal that the LSTM model significantly outperformed traditional machine learning algorithms such as Logistic Regression, Decision Tree, and Random Forest. The LSTM achieved an accuracy of 99.38%, precision of 99.40%, recall of 99.22%, and F1-score of 99.31%, demonstrating its superior capability to detect fraud while minimizing false positives. Through comparative analysis, we establish that deep learning not only improves predictive performance but also adapts better to temporal patterns inherent in financial transactions. This research underscores the transformative potential of AI-driven fraud detection in modern banking infrastructures, ensuring enhanced security, operational efficiency, and customer trust.
Keywords
Deep Learning, LSTM, Credit Card Fraud Detection, Banking Systems, Real-Time Detection, Machine Learning, Financial Security, Fraud Prevention, Imbalanced Dataset, Artificial Intelligence
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Copyright (c) 2025 Mohammad Iftekhar Ayub, Biswanath Bhattacharjee, Pinky Akter, Mohammad Nasir Uddin, Arun Kumar Gharami, Md Iftakhayrul Islam, Shaidul Islam Suhan, Md Sayem Khan, Lisa Chambugong
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