A Web-Based Credit Card Payment Architecture for Dealer Portals: Android POS Integration, Microservice Design, and Behavioural Segmentation for Data-Driven Dealer Management
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Abstract
Digital transformation in financial services has accelerated the need for secure, scalable, and user-centric payment infrastructures across various industries This study presents the design and implementation of a web-based credit card payment architecture integrated into the Dealer Web Portal (BWP), enabling dealer-initiated bill payments through Android POS ecosystem. The work covers three major dimensions: the development of a microservice-based web architecture using REST/SOAP services; real-time, bi-directional communication between the web portal and Android POS devices; and an unsupervised machine learning framework for behavioural segmentation using large-scale bill payment data. Multiple clustering algorithms, including K-Means, DBSCAN, Mean Shift, Spectral Clustering, and Hierarchical Clustering, were evaluated, with K-Means yielding the most meaningful segmentation results based on Purity, NMI, and Silhouette metrics. Segment outputs enabled dynamic commission policies, targeted dealer interventions, and time-series behavioral insights. The results demonstrate that the proposed architecture significantly enhances operational efficiency and data-driven decision making. This study provides one of the first integrated examples of Android POS–web portal interoperability combined with large-scale behavioural segmentation in Türkiye’s bill-payment ecosystem.
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References
Stankic, R., Aleksic, J. S., & Jakovljevic, A. (2019). DIGITAL TRANSFORMATION TRENDS IN PAYMENT SYSTEMS. New Economist/Novi Ekonomist, (26).
Khando, Khando, M. Sirajul Islam, and Shang Gao. "The emerging technologies of digital payments and associated challenges: a systematic literature review." Future Internet 15.1 (2022): 21. DOI: https://doi.org/10.3390/fi15010021
Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (pp. 226–231). AAAI Press
Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems (NIPS 14), 2001/2002.
Comaniciu, D., & Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603–619. https://doi.org/10.1109/34.1000236 DOI: https://doi.org/10.1109/34.1000236
Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. Science, 315(5814), 972–976. https://doi.org/10.1126/science.1136800 DOI: https://doi.org/10.1126/science.1136800
Kahveci, E., & Gurgur, T. (2025). Digital Payments and Sustainable Economic Growth: Transmission Mechanisms and Evidence from an Emerging Economy, Turkey. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), 142. DOI: https://doi.org/10.3390/jtaer20020142 DOI: https://doi.org/10.3390/jtaer20020142
Teker, S., Teker, D., & Orman, I. (2022). Digital Payment Systems: A Future Outlook. PressAcademia Procedia, 15(1), 175–176. DOI: https://doi.org/10.17261/Pressacademia.2022.1613 DOI: https://doi.org/10.17261/Pressacademia.2022.1613
Dhandapani, A. (2025). Microservices Architecture in Financial Services: Enabling Real-Time Transaction Processing and Enhanced Scalability. European Journal of Computer Science and Information Technology, 13(32), 145–159. DOI: https://doi.org/10.37745/ejcsit.2013/vol13n32145159 DOI: https://doi.org/10.37745/ejcsit.2013/vol13n32145159
Sitek, T., & Kotulski, Z. (2018). POS-originated transaction traces for downtime and risk management in banking and payment card systems. EURASIP Journal on Information Security, 2018(1), 1–15. DOI: https://doi.org/10.1186/s13635-018-0076-9. DOI: https://doi.org/10.1186/s13635-018-0076-9
Yang, M.-H. (2014). Security Enhanced EMV-Based Mobile Payment Protocol. The Scientific World Journal, 2014, 864571. DOI: https://doi.org/10.1155/2014/864571. DOI: https://doi.org/10.1155/2014/864571
Luo, J.-N., & Yang, M.-H. (2019). EMV-Compatible Offline Mobile Payment Protocol with Mutual Authentication. Sensors, 19(21), 4611. DOI: https://doi.org/10.3390/s19214611. DOI: https://doi.org/10.3390/s19214611
Nezhad, M. M., et al. (2025). SoK: Security of EMV Contactless Payment Systems. arXiv preprint. arXiv: https://arxiv.org/abs/2504.12812.
Diyabi, N., Çakır, D., Gül, Ö. M., Aytekin, T., & Kadry, S. (2024). Evaluating Customer Segmentation Techniques in the Retail Sector. International Journal of Interactive Multimedia and Artificial Intelligence, 9(3), 175–190. DOI: https://doi.org/10.9781/ijimai.2025.05.001. DOI: https://doi.org/10.9781/ijimai.2025.05.001
An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market. Analytics, 2(4), 809–823. DOI: https://doi.org/10.3390/analytics2040042. DOI: https://doi.org/10.3390/analytics2040042
AI-based Classification of Customer Support Tickets: State of the Art and Implementation with AutoML. arXiv preprint, arXiv:2406.01789. arXiv: https://arxiv.org/abs/2406.01789.
TaDaa: Real Time Ticket Assignment Deep Learning Auto Advisor for Customer Support, Help Desk, and Issue Ticketing Systems. arXiv preprint, arXiv:2207.11187. arXiv (PDF): https://arxiv.org/pdf/2207.11187