In this study, we investigate the effectiveness of various deep learning algorithms for customer segmentation in the banking sector, aiming to enhance targeted service delivery and customer experience. We employ a comprehensive pipeline encompassing data collection, preprocessing, feature selection, feature extraction, model development, and rigorous evaluation. Our dataset, derived from real-world banking customer profiles, was processed using normalization, encoding, and dimensionality reduction techniques. We implemented and compared eight models: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), CNN-LSTM Hybrid, Autoencoder-Based Network, and Deep Neural Network (DNN). Among them, the Autoencoder-Based model achieved the highest accuracy of 91.56%, outperforming others in terms of segmentation clarity and computational efficiency. These findings suggest that deep learning methods, particularly Autoencoder-Based architectures, offer robust solutions for real-time banking customer segmentation, enabling institutions to tailor products and services more effectively.

Customer Segmentation, Deep Learning, Banking Analytics, Autoencoder

Cheng, C., Wang, H., & Liu, J. (2021). A hybrid deep learning framework for customer segmentation and behavior prediction in e-banking. Expert Systems with Applications, 175, 114824. https://doi.org/10.1016/j.eswa.2021.114824

Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys (CSUR), 31(3), 264–323. https://doi.org/10.1145/331499.331504

Khan, A., Alsaqer, M., & Rehman, S. U. (2019). Customer segmentation using deep convolutional neural networks for banking sector. International Journal of Advanced Computer Science and Applications, 10(6), 500–506. https://doi.org/10.14569/IJACSA.2019.0100659

Nguyen, T. T., Nguyen, G. N., & Vo, B. (2020). An efficient customer segmentation framework using deep learning and clustering. IEEE Access, 8, 116148–116160. https://doi.org/10.1109/ACCESS.2020.3003660

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems, 30, 5998–6008.

Xie, J., Girshick, R., & Farhadi, A. (2016). Unsupervised deep embedding for clustering analysis. In Proceedings of the 33rd International Conference on International Conference on Machine Learning (Vol. 48, pp. 478–487). PMLR.

Hossain, S., Siddique, M. T., Hosen, M. M., Jamee, S. S., Akter, S., Akter, P., ... & Khan, M. S. (2025). Comparative Analysis of Sentiment Analysis Models for Consumer Feedback: Evaluating the Impact of Machine Learning and Deep Learning Approaches on Business Strategies. Frontline Social Sciences and History Journal, 5(02), 18-29.

Nguyen, A. T. P., & Akter, A. (2025). Intelligent Skincare: AI-Driven Cosmetic Product Recommendation Using Advanced Machine Learning Models. American Journal of Health, Medicine and Nursing Practice, 11(1), 26-35.

PHAN, H. T. N., & AKTER, A. (2024). HYBRID MACHINE LEARNING APPROACH FOR ORAL CANCER DIAGNOSIS AND CLASSIFICATION USING HISTOPATHOLOGICAL IMAGES. Universal Publication Index e-Library, 63-76.

Akhi, S. S., Shakil, F., Dey, S. K., Tusher, M. I., Kamruzzaman, F., Jamee, S. S., ... & Rahman, N. (2025). Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach. The American Journal of Engineering and Technology, 7(03), 88-97.

Nath, F., Asish, S., Debi, H. R., Chowdhury, M. O. S., Zamora, Z. J., & Muñoz, S. (2023, August). Predicting hydrocarbon production behavior in heterogeneous reservoir utilizing deep learning models. In Unconventional Resources Technology Conference, 13–15 June 2023 (pp. 506-521). Unconventional Resources Technology Conference (URTeC).

Ahmmed, M. J., Rahman, M. M., Das, A. C., Das, P., Pervin, T., Afrin, S., ... & Rahman, N. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BANKING FRAUD DETECTION: A STUDY ON PERFORMANCE, PRECISION, AND REAL-TIME APPLICATION. American Research Index Library, 31-44.

Pabel, M. A. H., Bhattacharjee, B., Dey, S. K., Jamee, S. S., Obaid, M. O., Mia, M. S., ... & Sharif, M. K. (2025). BUSINESS ANALYTICS FOR CUSTOMER SEGMENTATION: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS IN PERSONALIZED BANKING SERVICES. American Research Index Library, 1-13.

Siddique, M. T., Jamee, S. S., Sajal, A., Mou, S. N., Mahin, M. R. H., Obaid, M. O., ... & Hasan, M. (2025). Enhancing Automated Trading with Sentiment Analysis: Leveraging Large Language Models for Stock Market Predictions. The American Journal of Engineering and Technology, 7(03), 185-195.

Mohammad Iftekhar Ayub, Biswanath Bhattacharjee, Pinky Akter, Mohammad Nasir Uddin, Arun Kumar Gharami, Md Iftakhayrul Islam, Shaidul Islam Suhan, Md Sayem Khan, & Lisa Chambugong. (2025). Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems. The American Journal of Engineering and Technology, 7(04), 141–150. https://doi.org/10.37547/tajet/Volume07Issue04-19

Nguyen, A. T. P., Jewel, R. M., & Akter, A. (2025). Comparative Analysis of Machine Learning Models for Automated Skin Cancer Detection: Advancements in Diagnostic Accuracy and AI Integration. The American Journal of Medical Sciences and Pharmaceutical Research, 7(01), 15-26.

Nguyen, A. T. P., Shak, M. S., & Al-Imran, M. (2024). ADVANCING EARLY SKIN CANCER DETECTION: A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR MELANOMA DIAGNOSIS USING DERMOSCOPIC IMAGES. International Journal of Medical Science and Public Health Research, 5(12), 119-133.

Phan, H. T. N., & Akter, A. (2025). Predicting the Effectiveness of Laser Therapy in Periodontal Diseases Using Machine Learning Models. The American Journal of Medical Sciences and Pharmaceutical Research, 7(01), 27-37.

Phan, H. T. N. (2024). EARLY DETECTION OF ORAL DISEASES USING MACHINE LEARNING: A COMPARATIVE STUDY OF PREDICTIVE MODELS AND DIAGNOSTIC ACCURACY. International Journal of Medical Science and Public Health Research, 5(12), 107-118.

Al Mamun, A., Nath, A., Dey, S. K., Nath, P. C., Rahman, M. M., Shorna, J. F., & Anjum, N. (2025). Real-Time Malware Detection in Cloud Infrastructures Using Convolutional Neural Networks: A Deep Learning Framework for Enhanced Cybersecurity. The American Journal of Engineering and Technology, 7(03), 252-261.

Mazharul Islam Tusher, “Deep Learning Meets Early Diagnosis: A Hybrid CNN-DNN Framework for Lung Cancer Prediction and Clinical Translation”, ijmsphr, vol. 6, no. 05, pp. 63–72, May 2025.

Integrating Consumer Sentiment and Deep Learning for GDP Forecasting: A Novel Approach in Financial Industry”., Int Bus & Eco Adv Jou, vol. 6, no. 05, pp. 90–101, May 2025, doi: 10.55640/business/volume06issue05-05.

Mohammad Iftekhar Ayub, Biswanath Bhattacharjee, Pinky Akter, Mohammad Nasir Uddin, Arun Kumar Gharami, Md Iftakhayrul Islam, Shaidul Islam Suhan, Md Sayem Khan, & Lisa Chambugong. (2025). Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems. The American Journal of Engineering and Technology, 7(04), 141–150. https://doi.org/10.37547/tajet/Volume07Issue04-19

Mazharul Islam Tusher, Han Thi Ngoc Phan, Arjina Akter, Md Rayhan Hassan Mahin, & Estak Ahmed. (2025). A Machine Learning Ensemble Approach for Early Detection of Oral Cancer: Integrating Clinical Data and Imaging Analysis in the Public Health. International Journal of Medical Science and Public Health Research, 6(04), 07–15. https://doi.org/10.37547/ijmsphr/Volume06Issue04-02

Safayet Hossain, Ashadujjaman Sajal, Sakib Salam Jamee, Sanjida Akter Tisha, Md Tarake Siddique, Md Omar Obaid, MD Sajedul Karim Chy, & Md Sayem Ul Haque. (2025). Comparative Analysis of Machine Learning Models for Credit Risk Prediction in Banking Systems. The American Journal of Engineering and Technology, 7(04), 22–33. https://doi.org/10.37547/tajet/Volume07Issue04-04

Ayub, M. I., Bhattacharjee, B., Akter, P., Uddin, M. N., Gharami, A. K., Islam, M. I., ... & Chambugong, L. (2025). Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems. The American Journal of Engineering and Technology, 7(04), 141-150.

Siddique, M. T., Uddin, M. J., Chambugong, L., Nijhum, A. M., Uddin, M. N., Shahid, R., ... & Ahmed, M. (2025). AI-Powered Sentiment Analytics in Banking: A BERT and LSTM Perspective. International Interdisciplinary Business Economics Advancement Journal, 6(05), 135-147.

Al Mamun, A., Nath, A., Dey, S. K., Nath, P. C., Rahman, M. M., Shorna, J. F., & Anjum, N. (2025). Real-Time Malware Detection in Cloud Infrastructures Using Convolutional Neural Networks: A Deep Learning Framework for Enhanced Cybersecurity. The American Journal of Engineering and Technology, 7(03), 252-261.

Tusher, M. I., Hasan, M. M., Akter, S., Haider, M., Chy, M. S. K., Akhi, S. S., ... & Shaima, M. (2025). Deep Learning Meets Early Diagnosis: A Hybrid CNN-DNN Framework for Lung Cancer Prediction and Clinical Translation. International Journal of Medical Science and Public Health Research, 6(05), 63-72.

Sajal, A., Chy, M. S. K., Jamee, S. S., Uddin, M. N., Khan, M. S., Gharami, A. K., ... & Ahmed, M. (2025). Forecasting Bank Profitability Using Deep Learning and Macroeconomic Indicators: A Comparative Model Study. International Interdisciplinary Business Economics Advancement Journal, 6(06), 08-20.

Paresh Chandra Nath, Md Sajedul Karim Chy, Md Refat Hossain, Md Rashel Miah, Sakib Salam Jamee, Mohammad Kawsur Sharif, Md Shakhaowat Hossain, & Mousumi Ahmed. (2025). Comparative Performance of Large Language Models for Sentiment Analysis of Consumer Feedback in the Banking Sector: Accuracy, Efficiency, and Practical Deployment. Frontline Marketing, Management and Economics Journal, 5(06), 07–19. https://doi.org/10.37547/marketing-fmmej-05-06-02

Hossain, S., Siddique, M. T., Hosen, M. M., Jamee, S. S., Akter, S., Akter, P., ... & Khan, M. S. (2025). Comparative Analysis of Sentiment Analysis Models for Consumer Feedback: Evaluating the Impact of Machine Learning and Deep Learning Approaches on Business Strategies. Frontline Social Sciences and History Journal, 5(02), 18-29.

Jamee, S. S., Sajal, A., Obaid, M. O., Uddin, M. N., Haque, M. S. U., Gharami, A. K., ... & FARHAN, M. (2025). Integrating Consumer Sentiment and Deep Learning for GDP Forecasting: A Novel Approach in Financial Industry. International Interdisciplinary Business Economics Advancement Journal, 6(05), 90-101.

Hossain, S., Sajal, A., Jamee, S. S., Tisha, S. A., Siddique, M. T., Obaid, M. O., ... & Haque, M. S. U. (2025). Comparative Analysis of Machine Learning Models for Credit Risk Prediction in Banking Systems. The American Journal of Engineering and Technology, 7(04), 22-33.

Sajal, A., Chy, M. S. K., Jamee, S. S., Uddin, M. N., Khan, M. S., Gharami, A. K., ... & Ahmed, M. (2025). Forecasting Bank Profitability Using Deep Learning and Macroeconomic Indicators: A Comparative Model Study. International Interdisciplinary Business Economics Advancement Journal, 6(06), 08-20.

Mohammad Iftekhar Ayub, Arun Kumar Gharami, Fariha Noor Nitu, Mohammad Nasir Uddin, Md Iftakhayrul Islam, Alifa Majumder Nijhum, Molay Kumar Roy, & Syed Yezdani. (2025). AI-Driven Demand Forecasting for Multi-Echelon Supply Chains: Enhancing Forecasting Accuracy and Operational Efficiency through Machine Learning and Deep Learning Techniques. The American Journal of Management and Economics Innovations, 7(07), 74–85. https://doi.org/10.37547/tajmei/Volume07Issue07-09

Sharmin Sultana Akhi, Sadia Akter, Md Refat Hossain, Arjina Akter, Nur Nobe, & Md Monir Hosen. (2025). Early-Stage Chronic Disease Prediction Using Deep Learning: A Comparative Study of LSTM and Traditional Machine Learning Models. Frontline Medical Sciences and Pharmaceutical Journal, 5(07), 8–17. https://doi.org/10.37547/medical-fmspj-05-07-02

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.

Mann, R. A., Hossen, M. E., Withrow, A. D. M., Burton, J. T., Blythe, S. M., & Evett, C. G. (2024). Mesoporous silica nanoparticles-based smart nanocarriers for targeted drug delivery in colorectal cancer therapy. arXiv preprint arXiv:2409.18809.