Articles
| Open Access |
DOI:
https://doi.org/10.55640/ijcsis/Volume09Issue12-03
ENHANCED BANKING FRAUD DETECTION: A COMPARATIVE ANALYSIS OF SUPERVISED MACHINE LEARNING ALGORITHMS
Md Nur Hossain , Master’s in information technology management, Webster University, USA Safayet Hossain , Master of Science in Cybersecurity, Washington University of Science and Technology, USA Ayan Nath , Master’s in computer and information science, International American University, USA Paresh Chandra Nath , Master of Science in Information Technology, Washington University of Science and Technology, USA Mohammad Iftekhar Ayub , 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 Md Tarake Siddique , Master of Science in Information Technology, Washington University of Science and Technology, USA Mohammad Rasel , Masters in Business Analytics, International American University, LA, California, USAAbstract
Banking fraud has become a pervasive challenge, necessitating innovative solutions to protect financial institutions and their customers. This study investigates the effectiveness of supervised machine learning algorithms in detecting fraudulent activities within the banking sector. We conducted a comparative analysis of five widely used algorithms: Logistic Regression, Random Forest, Support Vector Machines, Gradient Boosting, and Neural Networks. Using a real-world banking dataset, we employed robust preprocessing and fine-tuning techniques to address class imbalances and optimize model performance. The evaluation metrics, including accuracy, precision, recall, F1-score, and area under the ROC curve (AUC), revealed that Gradient Boosting and Neural Networks consistently outperformed other models, achieving high precision and recall rates. The results highlight the potential of machine learning to detect subtle patterns of fraud while minimizing false positives and negatives. Furthermore, we discuss the implications of these findings for real-time fraud prevention systems and emphasize the importance of algorithm selection and scalability in operational environments.
Keywords
Real-world banking dataset, Fraud prevention systems, financial institutions
References
Md Habibur Rahman, Ashim Chandra Das, Md Shujan Shak, Md Kafil Uddin, Md Imdadul Alam, Nafis Anjum, Md Nad Vi Al Bony, & Murshida Alam. (2024). TRANSFORMING CUSTOMER RETENTION IN FINTECH INDUSTRY THROUGH PREDICTIVE ANALYTICS AND MACHINE LEARNING. The American Journal of Engineering and Technology, 6(10), 150–163. https://doi.org/10.37547/tajet/Volume06Issue10-17
Chen, Y., Donaldson, J., & McMillan, M. S. (2001). Market Dynamics and Economic Impacts of Real-Time Pricing in Competitive Markets. Economics Review, 58(4), 567-590.
Clements, M. P., Harris, M. N., & Szafarz, A. (2004). Econometric Modeling of Dynamic Pricing Systems. Journal of Economic Surveys, 18(5), 715-750.
Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5), 1189-1232.
Gal-Or, E. (1985). Strategic Pricing of New Products in Markets with Network Externalities. Quarterly Journal of Economics, 100(2), 295-308.
Ontor, M. R. H., Iqbal, A., Ahmed, E., & Rahman, A. (2024). LEVERAGING DIGITAL TRANSFORMATION AND SOCIAL MEDIA ANALYTICS FOR OPTIMIZING US FASHION BRANDS'PERFORMANCE: A MACHINE LEARNING APPROACH. American Research Index Library, 45-56.
Iqbal, A., Ahmed, E., Rahman, A., & Ontor, M. R. H. (2024). ENHANCING FRAUD DETECTION AND ANOMALY DETECTION IN RETAIL BANKING USING GENERATIVE AI AND MACHINE LEARNING MODELS. International journal of networks and security, 4(01), 33-43.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
Kannan, P. K., & Kopalle, P. K. (2001). Dynamic Pricing on the Internet: Optimization and Consumer Behavior. Marketing Science, 20(1), 42-61.
Kumar, A., Gupta, S., & Mehta, R. (2019). Real-Time Dynamic Pricing Strategies in Retail and E-commerce. International Journal of Machine Learning Research, 20(6), 456-473.
Lemke, A., Grinblatt, M., & Kannan, S. (2019). Ensemble Models for Competitive Market Pricing. Computational Economics, 30(4), 678-702.
Abdallah, A., Maarof, M. A., & Zainal, A. (2016). Fraud detection system: A survey. Journal of Network and Computer Applications, 68, 90-113. https://doi.org/10.1016/j.jnca.2016.04.007
Bahnsen, A. C., Stojanovic, J., Aouada, D., & Ottersten, B. (2014). Cost sensitive credit card fraud detection using Bayes minimum risk. 2013 12th International Conference on Machine Learning and Applications, 333-338. https://doi.org/10.1109/ICMLA.2013.66
Zhou, Y., Zhao, Y., & Zhang, X. (2017). Comparative Study of Machine Learning Models for Demand Forecasting in Retail. Journal of Business Analytics, 12(3), 233-250.
Al-Imran, M., Akter, S., Mozumder, M. A. S., Bhuiyan, R. J., Rahman, T., Ahmmed, M. J., ... & Hossen, M. E. (2024). EVALUATING MACHINE LEARNING ALGORITHMS FOR BREAST CANCER DETECTION: A STUDY ON ACCURACY AND PREDICTIVE PERFORMANCE. The American Journal of Engineering and Technology, 6(09), 22-33.
Shinde, N. K., Seth, A., & Kadam, P. (2023). Exploring the synergies: a comprehensive survey of blockchain integration with artificial intelligence, machine learning, and iot for diverse applications. Machine Learning and Optimization for Engineering Design, 85-119.
Tauhedur Rahman, Md Kafil Uddin, Biswanath Bhattacharjee, Md Siam Taluckder, Sanjida Nowshin Mou, Pinky Akter, Md Shakhaowat Hossain, Md Rashel Miah, & Md Mohibur Rahman. (2024). BLOCKCHAIN APPLICATIONS IN BUSINESS OPERATIONS AND SUPPLY CHAIN MANAGEMENT BY MACHINE LEARNING. International Journal of Computer Science & Information System, 9(11), 17–30. https://doi.org/10.55640/ijcsis/Volume09Issue11-03
Md Jamil Ahmmed, Md Mohibur Rahman, Ashim Chandra Das, Pritom Das, Tamanna Pervin, Sadia Afrin, Sanjida Akter Tisha, Md Mehedi Hassan, & Nabila Rahman. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BANKING FRAUD DETECTION: A STUDY ON PERFORMANCE, PRECISION, AND REAL-TIME APPLICATION. International Journal of Computer Science & Information System, 9(11), 31–44. https://doi.org/10.55640/ijcsis/Volume09Issue11-04
Rahman, M. M., Akhi, S. S., Hossain, S., Ayub, M. I., Siddique, M. T., Nath, A., ... & Hassan, M. M. (2024). EVALUATING MACHINE LEARNING MODELS FOR OPTIMAL CUSTOMER SEGMENTATION IN BANKING: A COMPARATIVE STUDY. The American Journal of Engineering and Technology, 6(12), 68-83.
Bhattacharjee, B., Mou, S. N., Hossain, M. S., Rahman, M. K., Hassan, M. M., Rahman, N., ... & Haque, M. S. U. (2024). MACHINE LEARNING FOR COST ESTIMATION AND FORECASTING IN BANKING: A COMPARATIVE ANALYSIS OF ALGORITHMS. International journal of business and management sciences, 4(12), 6-17.
Bhandari, A., Cherukuri, A. K., & Kamalov, F. (2023). Machine learning and blockchain integration for security applications. In Big Data Analytics and Intelligent Systems for Cyber Threat Intelligence (pp. 129-173). River Publishers.
Nafis Anjum, Md Nad Vi Al Bony, Murshida Alam, Mehedi Hasan, Salma Akter, Zannatun Ferdus, Md Sayem Ul Haque, Radha Das, & Sadia Sultana. (2024). COMPARATIVE ANALYSIS OF SENTIMENT ANALYSIS MODELS ON BANKING INVESTMENT IMPACT BY MACHINE LEARNING ALGORITHM. International Journal of Computer Science & Information System, 9(11), 5–16. https://doi.org/10.55640/ijcsis/Volume09Issue11-02
Al Mamun, A., Hossain, M. S., Rishad, S. S. I., Rahman, M. M., Tisha, S. A., Shakil, F., ... & Sultana, S. (2024). MACHINE LEARNING FOR STOCK MARKET SECURITY MEASUREMENT: A COMPARATIVE ANALYSIS OF SUPERVISED, UNSUPERVISED, AND DEEP LEARNING MODELS. International journal of networks and security, 4(01), 22-32.
Das, A. C., Mozumder, M. S. A., Hasan, M. A., Bhuiyan, M., Islam, M. R., Hossain, M. N., ... & Alam, M. I. (2024). MACHINE LEARNING APPROACHES FOR DEMAND FORECASTING: THE IMPACT OF CUSTOMER SATISFACTION ON PREDICTION ACCURACY. The American Journal of Engineering and Technology, 6(10), 42-53.
Md Risalat Hossain Ontor, Asif Iqbal, Emon Ahmed, Tanvirahmedshuvo, & Ashequr Rahman. (2024). LEVERAGING DIGITAL TRANSFORMATION AND SOCIAL MEDIA ANALYTICS FOR OPTIMIZING US FASHION BRANDS’ PERFORMANCE: A MACHINE LEARNING APPROACH. International Journal of Computer Science & Information System, 9(11), 45–56. https://doi.org/10.55640/ijcsis/Volume09Issue11-05
Zheng, Q., Wu, H., & Zhang, T. (2020). Anomaly detection in blockchain networks using unsupervised learning. Cybersecurity Advances, 9(2), 89-102.
Naznin, R., Sarkar, M. A. I., Asaduzzaman, M., Akter, S., Mou, S. N., Miah, M. R., ... & Sajal, A. (2024). ENHANCING SMALL BUSINESS MANAGEMENT THROUGH MACHINE LEARNING: A COMPARATIVE STUDY OF PREDICTIVE MODELS FOR CUSTOMER RETENTION, FINANCIAL FORECASTING, AND INVENTORY OPTIMIZATION. International Interdisciplinary Business Economics Advancement Journal, 5(11), 21-32.
Iqbal, A., Ahmed, E., Rahman, A., & Ontor, M. R. H. (2024). ENHANCING FRAUD DETECTION AND ANOMALY DETECTION IN RETAIL BANKING USING GENERATIVE AI AND MACHINE LEARNING MODELS. International journal of networks and security, 4(01), 33-43.
Md Jamil Ahmmed, Md Mohibur Rahman, Ashim Chandra Das, Pritom Das, Tamanna Pervin, Sadia Afrin, Sanjida Akter Tisha, Md Mehedi Hassan, & Nabila Rahman. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BANKING FRAUD DETECTION: A STUDY ON PERFORMANCE, PRECISION, AND REAL-TIME APPLICATION. International Journal of Computer Science & Information System, 9(11), 31–44. https://doi.org/10.55640/ijcsis/Volume09Issue11-04
Arif, M., Ahmed, M. P., Al Mamun, A., Uddin, M. K., Mahmud, F., Rahman, T., ... & Helal, M. (2024). DYNAMIC PRICING IN FINANCIAL TECHNOLOGY: EVALUATING MACHINE LEARNING SOLUTIONS FOR MARKET ADAPTABILITY. International Interdisciplinary Business Economics Advancement Journal, 5(10), 13-27.
Uddin, M. K., Akter, S., Das, P., Anjum, N., Akter, S., Alam, M., ... & Pervin, T. (2024). MACHINE LEARNING-BASED EARLY DETECTION OF KIDNEY DISEASE: A COMPARATIVE STUDY OF PREDICTION MODELS AND PERFORMANCE EVALUATION. International Journal of Medical Science and Public Health Research, 5(12), 58-75.
Shak, M. S., Uddin, A., Rahman, M. H., Anjum, N., Al Bony, M. N. V., Alam, M., ... & Pervin, T. (2024). INNOVATIVE MACHINE LEARNING APPROACHES TO FOSTER FINANCIAL INCLUSION IN MICROFINANCE. International Interdisciplinary Business Economics Advancement Journal, 5(11), 6-20.
Rahman, A., Iqbal, A., Ahmed, E., & Ontor, M. R. H. (2024). PRIVACY-PRESERVING MACHINE LEARNING: TECHNIQUES, CHALLENGES, AND FUTURE DIRECTIONS IN SAFEGUARDING PERSONAL DATA MANAGEMENT. International journal of business and management sciences, 4(12), 18-32.
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Copyright (c) 2024 Nur Hossain, Safayet Hossain, Ayan Nath, Paresh Chandra Nath, Mohammad Iftekhar Ayub, Md Mehedi Hassan, Md Tarake Siddique, Mohammad Rasel
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