Articles
| Open Access |
DOI: EVALUATING MACHINE LEARNING MODELS FOR OPTIMAL CUSTOMER SEGMENTATION IN BANKING: A COMPARATIVE STUDY
Md Mohibur Rahman , Fred DeMatteis School of Engineering and Applied Science, Hofstra University, USA Sharmin Sultana Akhi , Department of Computer Science, Monroe University, USA Safayet Hossain , Master of Science in Cybersecurity, Washington University of Science and Technology, USA Mohammad Iftekhar Ayub , 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 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 Md Mehedi Hassan , Master of Science in Information Technology, Washington University of Science and Technology, USAAbstract
This study presents a comparative analysis of machine learning algorithms for customer segmentation in the banking sector, utilizing a comprehensive dataset that includes transactional, demographic, and engagement attributes. Various clustering models, including K-Means, Gaussian Mixture Models (GMM), Hierarchical Clustering, DBSCAN, and Spectral Clustering, were evaluated to identify the most effective approach in terms of segmentation accuracy, scalability, and interpretability. The results revealed that Spectral Clustering consistently outperformed other models, offering superior accuracy and valuable insights into customer interactions across multiple banking touchpoints. While K-Means delivered fast and scalable segmentation, it lacked the flexibility needed for non-spherical clusters. The study also highlighted the benefits of a multi-dimensional dataset approach, which provided deeper insights into customer behavior, engagement, and loyalty. Although limitations such as computational complexity and scalability challenges remain, future research should focus on real-time data integration and multi-channel interactions across banking operations. This research not only contributes to machine learning applications in banking but also offers actionable strategies for targeted marketing, personalized customer engagement, risk management, and overall service optimization.
Keywords
Customer Segmentation, Machine Learning, Banking Analytics, Clustering Algorithms
References
Bolton, R. N., & Drew, J. H. (1991). A longitudinal analysis of the impact of service changes on customer attitudes. Journal of Marketing, 55(1), 1-9. https://doi.org/10.1177/002224299105500101
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1-38.
Everitt, B. S., Landau, S. N., & Leese, M. (2011). Cluster Analysis (5th ed.). Wiley.
Ghosh, S., & Gupta, A. (2015). An advanced study on Gaussian Mixture Models in financial applications. Financial Analytics Journal, 12(4), 220-234.
Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd ed.). Elsevier.
Kotler, P., & Keller, K. L. (2012). Marketing Management (14th ed.). Pearson.
MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, 281-297.
Smith, S. (2003). The evolution of customer segmentation in banking. International Journal of Banking Studies, 15(2), 56-78.
Wedel, M., & Kamakura, W. A. (2000). Market Segmentation: Conceptual and Methodological Foundations. Springer.
Von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17(4), 395-416.
Tanwar, S., Bhatia, Q., Patel, P., Kumari, A., Singh, P. K., & Hong, W. C. (2019). Machine learning adoption in blockchain-based smart applications: The challenges, and a way forward. IEEE Access, 8, 474-488.
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
Nimmagadda, V. S. P. (2021). Artificial Intelligence and Blockchain Integration for Enhanced Security in Insurance: Techniques, Models, and Real-World Applications. African Journal of Artificial Intelligence and Sustainable Development, 1(2), 187-224.
Venkatesan, K., & Rahayu, S. B. (2024). Blockchain security enhancement: an approach towards hybrid consensus algorithms and machine learning techniques. Scientific Reports, 14(1), 1149.
DYNAMIC PRICING IN FINANCIAL TECHNOLOGY: EVALUATING MACHINE LEARNING SOLUTIONS FOR MARKET ADAPTABILITY. (2024). International Interdisciplinary Business Economics Advancement Journal, 5(10), 13-27. https://doi.org/10.55640/business/volume05issue10-03
Hayadi, B. H., & El Emary, I. M. (2024). Enhancing Security and Efficiency in Decentralized Smart Applications through Blockchain Machine Learning Integration. Journal of Current Research in Blockchain, 1(2), 139-154.
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.
M. S. Haque, M. S. Taluckder, S. Bin Shawkat, M. A. Shahriyar, M. A. Sayed and C. Modak, "A Comparative Study of Prediction of Pneumonia and COVID-19 Using Deep Neural Networks," 2023 3rd International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), Yogyakarta, Indonesia, 2023, pp. 218-223, doi: 10.1109/ICE3IS59323.2023.10335362.
Zhao, L., Zhang, Y., Chen, X., & Huang, Y. (2021). A reinforcement learning approach to supply chain operations management: Review, applications, and future directions. Computers & Operations Research, 132, 105306. https://doi.org/10.1016/j.cor.2021.105306
Sweet, M. M. R., Ahmed, M. P., Mozumder, M. A. S., Arif, M., Chowdhury, M. S., Bhuiyan, R. J., ... & Mamun, M. A. I. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR ACCURATE LUNG CANCER PREDICTION. The American Journal of Engineering and Technology, 6(09), 92-103.
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.
Dibaei, M., Zheng, X., Xia, Y., Xu, X., Jolfaei, A., Bashir, A. K., ... & Vasilakos, A. V. (2021). Investigating the prospect of leveraging blockchain and machine learning to secure vehicular networks: A survey. IEEE Transactions on Intelligent Transportation Systems, 23(2), 683-700.
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
Hisham, S., Makhtar, M., & Aziz, A. A. (2022). Combining multiple classifiers using ensemble method for anomaly detection in blockchain networks: A comprehensive review. International Journal of Advanced Computer Science and Applications, 13(8).
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
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.
Diro, A., Chilamkurti, N., Nguyen, V. D., & Heyne, W. (2021). A comprehensive study of anomaly detection schemes in IoT networks using machine learning algorithms. Sensors, 21(24), 8320.
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
Shahbazi, Z., & Byun, Y. C. (2021). Integration of blockchain, IoT and machine learning for multistage quality control and enhancing security in smart manufacturing. Sensors, 21(4), 1467.
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.
Kumar, R., Verma, S., & Singh, A. (2023). Lightweight machine learning models for IoT blockchain security. Journal of Network Security, 15(3), 210-226.
Miller, T., & Johnson, P. (2021). Explainable AI for blockchain applications: Challenges and opportunities. AI Ethics Review, 12(4), 356-372.
MACHINE LEARNING FOR STOCK MARKET SECURITY MEASUREMENT: A COMPARATIVE ANALYSIS OF SUPERVISED, UNSUPERVISED, AND DEEP LEARNING MODELS. (2024). International Journal of Networks and Security, 4(01), 22-32. https://doi.org/10.55640/ijns-04-01-06
Wang, X., Li, J., & Zhao, Y. (2022). Reinforcement learning approaches to enhance blockchain consensus mechanisms. Blockchain Research Journal, 18(1), 45-60.
Akter, S., Mahmud, F., Rahman, T., Ahmmed, M. J., Uddin, M. K., Alam, M. I., ... & Jui, A. H. (2024). A COMPREHENSIVE STUDY OF MACHINE LEARNING APPROACHES FOR CUSTOMER SENTIMENT ANALYSIS IN BANKING SECTOR. The American Journal of Engineering and Technology, 6(10), 100-111.
Shahid, R., Mozumder, M. A. S., Sweet, M. M. R., Hasan, M., Alam, M., Rahman, M. A., ... & Islam, M. R. (2024). Predicting Customer Loyalty in the Airline Industry: A Machine Learning Approach Integrating Sentiment Analysis and User Experience. International Journal on Computational Engineering, 1(2), 50-54.
Zhuang, M., Huang, L., & Chen, Z. (2021). Machine learning for blockchain security: A survey of algorithms and applications. Computers & Security, 103, 102-118.
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
COMPARATIVE PERFORMANCE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BUSINESS INTELLIGENCE: A STUDY ON CLASSIFICATION AND REGRESSION MODELS. (2024). International Journal of Business and Management Sciences, 4(11), 06-18. https://doi.org/10.55640/ijbms-04-11-02
Zheng, Q., Wu, H., & Zhang, T. (2020). Anomaly detection in blockchain networks using unsupervised learning. Cybersecurity Advances, 9(2), 89-102.
ENHANCING SMALL BUSINESS MANAGEMENT THROUGH MACHINE LEARNING: A COMPARATIVE STUDY OF PREDICTIVE MODELS FOR CUSTOMER RETENTION, FINANCIAL FORECASTING, AND INVENTORY OPTIMIZATION. (2024). International Interdisciplinary Business Economics Advancement Journal, 5(11), 21-32. https://doi.org/10.55640/business/volume05issue11-03
Sweet, M. M. R., Arif, M., Uddin, A., Sharif, K. S., Tusher, M. I., Devi, S., ... & Sarkar, M. A. I. (2024). Credit risk assessment using statistical and machine learning: Basic methodology and risk modeling applications. International Journal on Computational Engineering, 1(3), 62-67.
ENHANCING FRAUD DETECTION AND ANOMALY DETECTION IN RETAIL BANKING USING GENERATIVE AI AND MACHINE LEARNING MODELS. (2024). International Journal of Networks and Security, 4(01), 33-43. https://doi.org/10.55640/ijns-04-01-07
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
Mozumder, M. A. S., Nguyen, T. N., Devi, S., Arif, M., Ahmed, M. P., Ahmed, E., ... & Uddin, A. (2024). Enhancing Customer Satisfaction Analysis Using Advanced Machine Learning Techniques in Fintech Industry. J. Comput. Sci. Technol. Stud, 6, 35-41.
Sweet, M. M. R., Arif, M., Uddin, A., Sharif, K. S., Tusher, M. I., Devi, S., ... & Sarkar, M. A. I. (2024). Credit risk assessment using statistical and machine learning: Basic methodology and risk modeling applications. International Journal on Computational Engineering, 1(3), 62-67.
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.
Mozumder, M. A. S., Nguyen, T. N., Devi, S., Arif, M., Ahmed, M. P., Ahmed, E., ... & Uddin, A. (2024). Enhancing Customer Satisfaction Analysis Using Advanced Machine Learning Techniques in Fintech Industry. J. Comput. Sci. Technol. Stud, 6, 35-41.
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Copyright (c) 2024 Md Mohibur Rahman, Sharmin Sultana Akhi, Safayet Hossain, Mohammad Iftekhar Ayub, Md Tarake Siddique, Ayan Nath, Paresh Chandra Nath, Md Mehedi Hassan
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