Articles
| Open Access |
DOI:
https://doi.org/10.55640/ijcsis/Volume09Issue11-04
COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BANKING FRAUD DETECTION: A STUDY ON PERFORMANCE, PRECISION, AND REAL-TIME APPLICATION
Md Jamil Ahmmed , Department of Information Technology Project Management, Business Analytics, St. Francis College, USA Md Mohibur Rahman , Fred DeMatteis School of Engineering and Applied Science, Hofstra University, USA Ashim Chandra Das , Master of Science in Information Technology, Washington University of Science and Technology, USA Pritom Das , College of Computer Science, Pacific States University, Los Angeles, CA Tamanna Pervin , Department of Business Administration, International American University, Los Angeles, California Sadia Afrin , Department of Computer & Information Science, Gannon University, USA Sanjida Akter Tisha , Master of Science in Information Technology, Washington University of Science and Technology, USA Md Mehedi Hassan , Master of Science in Information Technology, Washington University of Science and Technology, USA Nabila Rahman , Masters in information technology management, Webster University, USAAbstract
This study investigates the application of machine learning algorithms for fraud detection in the banking sector, addressing the increasing sophistication of fraudulent activities in digital banking. A comparative analysis was conducted on various models, including logistic regression, decision trees, random forests, support vector machines, neural networks, and ensemble methods. Performance metrics such as precision, recall, F1-score, and AUC-ROC were used to evaluate model effectiveness. Results indicate that ensemble models, specifically the stacked ensemble, outperformed other algorithms in balancing precision and recall, thus minimizing false positives and false negatives. These models demonstrated superior accuracy and adaptability to complex fraud patterns, making them particularly suitable for real-time fraud detection. However, challenges related to model interpretability and data quality highlight the need for further research on explainable AI and unsupervised learning approaches. This study underscores the promise of machine learning as a strategic solution for enhancing fraud detection in banking, offering a path to more robust and responsive financial security measures.
Keywords
Banking fraud detection, Machine learning in finance, Ensemble learning
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Copyright (c) 2024 Md Jamil Ahmmed, Md Mohibur Rahman, Ashim Chandra Das, Pritom Das, Tamanna Pervin, Sadia Afrin, Sanjida Akter Tisha, Md Mehedi Hassan, Nabila Rahman
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