Real-Time Cloud Security and AI Analytics in Digital Banking Systems: SDN-Enabled Modular DC Converter Integration with Ethical Oversight
DOI:
https://doi.org/10.15662/IJRAI.2025.0805004Keywords:
AI-Driven Analytics, Real-Time Cloud Security, Digital Banking Systems, Modular DC Converter, Ethical AI Governance, Predictive Monitoring, Cybersecurity Risk Management, Sustainable FinTech Infrastructure, Intelligent Financial EcosystemsAbstract
This paper presents a Real-Time Cloud Security and AI Analytics Framework for digital banking systems, emphasizing modular DC converter integration and ethical oversight in financial technology environments. The proposed architecture leverages AI-driven predictive analytics and cloud-based monitoring to detect anomalies, ensure data integrity, and mitigate cybersecurity risks in real time. By incorporating modular DC converter systems, the framework enhances energy efficiency and operational stability in cloud data centers supporting digital banking infrastructure. Ethical AI governance mechanisms are embedded to ensure fairness, transparency, and accountability in automated financial decision-making. The model promotes sustainable, secure, and compliant digital banking operations through intelligent monitoring, adaptive control, and responsible AI deployment.
References
1. Boulieris, P., Pavlopoulos, J., Xenos, A., et al. (2024). Fraud detection with natural language processing. Machine Learning, 113, 5087 5108. SpringerLink
2. Frose, J. W. (2022). Architectures of Interpretability in Deep Neural Networks for Transparent Clinical Decision Support in High-Stakes Diagnostic Environments. ISCSITR-INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND ENGINEERING (ISCSITR-IJCSE)-ISSN: 3067-7394, 3(01), 6-14.
3. Branco, B., Abreu, P., Gomes, A. S., Almeida, M. S. C., Ascensão, J. T., & Bizarro, P. (2020). Interleaved Sequence RNNs for Fraud Detection. arXiv preprint arXiv:2002.05988. arXiv
4. Fursov, I., Morozov, M., Kaploukhaya, N., Kovtun, E., Rivera Castro, R., Gusev, G., … Burnaev, E. (2021). Adversarial Attacks on Deep Models for Financial Transaction Records. arXiv preprint arXiv:2106.08361. arXiv
5. Kalyani, S., & Gupta, N. (2023). Is artificial intelligence and machine learning changing the ways of banking: a systematic literature review and meta analysis. Discover Artificial Intelligence, 3, Article 41. SpringerLink
6. Karvannan, R. (2023). Real‑Time Prescription Management System Intake & Billing System. International Journal of Humanities and Information Technology, 5(02), 34-43.
7. Dave, B. L. (2024). An Integrated Cloud-Based Financial Wellness Platform for Workplace Benefits and Retirement Management. International Journal of Technology, Management and Humanities, 10(01), 42-52.
8. Kurshan, E., Shen, H., & Chen, J. (2020). Towards Self Regulating AI: Challenges and Opportunities of AI Model Governance in Financial Services. arXiv preprint arXiv:2010.04827. arXiv
9. Boggarapu, N. B. (2024). Modernizing Banking Compliance: An Analysis of AI Powered Data Governance in a Hybrid Cloud Environment. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 1840 1849. ijsrcseit.com
10. Nallamothu, T. K. (2024). Real-Time Location Insights: Leveraging Bright Diagnostics for Superior User Engagement. International Journal of Technology, Management and Humanities, 10(01), 13-23.
11. Real time suspicious detection framework for financial data streams. (2024). International Journal of Information Technology.
12. Naveen Kumar Kokkalakonda. (2022). AI powered fraud detection in banking: enhancing security with machine learning algorithms. International Journal of Science and Research Archive, 7(1), 564 575. ijsra.net
13. Lanka, S. (2023). Built for the Future How Citrix Reinvented Security Monitoring with Analytics. International Journal of Humanities and Information Technology, 5(02), 26-33. Amaral, O., Azeem, M. I., Abualhaija, S., & Briand, L. C. (2022). NLP-based automated compliance checking of data processing agreements against GDPR. *arXiv*. https://doi.org/10.48550/arXiv.2209.09722
14. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2023). Ethical analysis and decision-making framework for marketing communications: A weighted product model approach. Data Analytics and Artificial Intelligence, 3(5), 44–53. https://doi.org/10.46632/daai/3/5/7
15. Venkata Surendra Reddy Narapareddy. (2023). MODULAR FOUNDATION OF A BLUEPRINT MODEL. International Journal of
16. Engineering Technology Research & Management (IJETRM), 07(10), 59–67. https://doi.org/10.5281/zenodo.1554 7718
17. Konda, S. K. (2022). STRATEGIC EXECUTION OF SYSTEM-WIDE BMS UPGRADES IN PEDIATRIC HEALTHCARE ENVIRONMENTS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7123-7129.
18. Azmi, S. K. (2021). Delaunay Triangulation for Dynamic Firewall Rule Optimization in Software-Defined Networks. Well Testing Journal, 30(1), 155-169.
19. Bommarito, M. J. II, Katz, D. M., & Detterman, E. M. (2018). LexNLP: Natural language processing and information extraction for legal and regulatory texts. *arXiv*. https://doi.org/10.48550/arXiv.1806.03688
20. Sankar,, T., Venkata Ramana Reddy, B., & Balamuralikrishnan, A. (2023). AI-Optimized Hyperscale Data Centers: Meeting the Rising Demands of Generative AI Workloads. In International Journal of Trend in Scientific Research and Development (Vol. 7, Number 1, pp. 1504–1514). IJTSRD. https://doi.org/10.5281/zenodo.15762325
21. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2023). Ethical analysis and decision-making framework for marketing communications: A weighted product model approach. Data Analytics and Artificial Intelligence, 3(5), 44–53. https://doi.org/10.46632/daai/3/5/7
22. Jain, J., Dhanasekaran, N., & Diab, M. T. (2025). From complexity to clarity: AI/NLP’s role in regulatory compliance. *Findings of the Association for Computational Linguistics: ACL 2025*. https://doi.org/10.18653/v1/2025.findings-acl.1366
