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DOI: TRANSFORMING BANKING SECURITY: THE ROLE OF DEEP LEARNING IN FRAUD DETECTION SYSTEMS
Md Al-Imran , College Of Graduate And Professional Studies Trine University, USA Eftekhar Hossain Ayon , Department Of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA Md Rashedul Islam , Master Of Business Administration, Westcliff University, Irvine, California Fuad Mahmud , Department Of Information Assurance And Cybersecurity, Gannon University, USA Sharmin Akter , Department Of Information Technology Project Management, St. Francis College, USA Md Khorshed Alam , Department Of Professional Security Studies, New Jersey City University, Jersey City, New Jersey, USA Md Tarek Hasan , Department Of Professional Security Studies, New Jersey City University, Jersey City, New Jersey, USA Sadia Afrin , Department Of Computer & Information Science, Gannon University, USA Jannatul Ferdous Shorna , College Of Engineering And Computer Science, Florida Atlantic University, Boca Raton, Florida Md Munna Aziz , Master Of Business Administration, Westcliff University, Irvine, California, USAAbstract
In the digital banking landscape, the increasing volume of online transactions has heightened the risk of fraudulent activities, necessitating the development of more effective detection systems. This study investigates the efficacy of various machine learning and deep learning algorithms in identifying fraudulent transactions, emphasizing Long Short-Term Memory (LSTM) networks. We implemented and evaluated multiple algorithms, including Logistic Regression, Random Forest, Gradient Boosting Machines (GBM), and XGBoost, on a large-scale credit card transaction dataset. Our results demonstrate that the LSTM model outperforms traditional machine learning algorithms, achieving an accuracy of 98.5%, precision of 87.2%, recall of 85.0%, and an Area Under the Curve (AUC) score of 0.94. These findings highlight the superior capability of LSTM networks to capture complex patterns in sequential transaction data, making them an asset for real-time fraud detection in banking. This research underscores the need for financial institutions to adopt advanced deep learning techniques to enhance their fraud detection systems, thereby minimizing financial losses and improving customer trust.
ZENODO DOI :- https://doi.org/10.5281/zenodo.14044576
Keywords
Fraud Detection, Banking, Machine Learning, Deep Learning
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Copyright (c) 2024 Md Al-Imran, Eftekhar Hossain Ayon, Md Rashedul Islam, Fuad Mahmud, Sharmin Akter, Md Khorshed Alam, Md Tarek Hasan, Sadia Afrin, Jannatul Ferdous Shorna, Md Munna Aziz
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