A Secure and Compliant AI-Driven Financial Infrastructure Enabled by Fiber Broadband–5G Integration and Cloud-Based Fraud Prevention

Authors

  • Darragh Lorcan O’Shea Independent Researcher, Ireland Author

DOI:

https://doi.org/10.15662/IJEETR.2024.0604006

Keywords:

Artificial Intelligence, Fiber Broadband, 5G Networks, Financial Infrastructure, Cloud Computing, Fraud Prevention, Cybersecurity, Regulatory Compliance, Automation

Abstract

The financial sector increasingly relies on high-speed connectivity, intelligent automation, and robust security mechanisms to support digital transactions and regulatory compliance. The integration of Fiber Broadband and Fifth-Generation (5G) networks provides a resilient and scalable communication backbone for next-generation financial infrastructures. This paper presents an AI-driven financial architecture that leverages fiber–5G convergence and cloud-based fraud prevention to deliver secure, compliant, and low-latency financial services. Artificial intelligence techniques are applied to transaction monitoring, anomaly detection, and compliance enforcement, while cloud platforms enable elastic scalability and real-time analytics. The proposed approach enhances operational efficiency, strengthens security posture, and ensures adherence to financial regulations. The findings indicate that fiber–5G-enabled AI infrastructures significantly improve fraud detection accuracy, network reliability, and compliance readiness in modern financial systems.

References

1. Baker, T., & Dellaert, B. (2019). Regulating robo advice across the financial services industry. Iowa Law Review, 103(2), 713–750. https://doi.org/10.2139/ssrn.2932189

2. Bussu, V. R. R. (2024). End-to-End Architecture and Implementation of a Unified Lakehouse Platform for Multi-ERP Data Integration using Azure Data Lake and the Databricks Lakehouse Governance Framework. International Journal of Computer Technology and Electronics Communication, 7(4), 9128-9136.

3. Singh, A. (2023). Integrating Fiber Broadband and 5G Network: Synergies and Challenges. International Journal of Scientific Research in Engineering and Management (IJSREM), 7(3), 1–12. https://doi.org/10.55041/IJSREM18134 file:///C:/Users/Admin/Downloads/Integrating-Fiber-Broadband-and-5G-Network-Synergies-and-Challenges.pdf

4. Cloud Security Alliance. (2023). Security guidance for critical areas of focus in cloud computing v5.0. https://cloudsecurityalliance.org

5. Usha, G., Babu, M. R., & Kumar, S. S. (2017). Dynamic anomaly detection using cross layer security in MANET. Computers & Electrical Engineering, 59, 231-241.

6. Karnam, A. (2024). Next-Gen Observability for SAP: How Azure Monitor Enables Predictive and Autonomous Operations. International Journal of Computer Technology and Electronics Communication, 7(2), 8515–8524. https://doi.org/10.15680/IJCTECE.2024.0702006

7. GSMA. (2022). 5G security: Protecting the future mobile network. https://www.gsma.com/security

8. Chivukula, V. (2023). Calibrating Marketing Mix Models (MMMs) with Incrementality Tests. International Journal of Research and Applied Innovations (IJRAI), 6(5), 9534–9538.

9. International Telecommunication Union. (2021). Framework for secure and trusted broadband networks (ITU-T X.805). https://www.itu.int

10. Navandar, P. (2023). Guarding Networks: Understanding the Intrusion Detection System (IDS). Journal of biosensors and bioelectronics research. https://d1wqtxts1xzle7.cloudfront.net/125806939/20231119-libre.pdf?1766259308=&response-content-disposition=inline%3B+filename%3DGuarding_Networks_Understanding_the_Intr.pdf&Expires=1767147182&Signature=H9aJ73csgfALZ~2B89oBRyYgz57iuooJUU0zKPdjpmQjunvziuvJjd~r8gYT52Ah6RozX-LUpFB14VO8yjXrVD73j1HN9DAMi1PSGKaRbcI8gBbrnFQQGOhTO7VYkGcz3ylDLZJatGabbl5ASNiqe0kINjsw6op5mJzXUoWLZkmret8YBzR1b6Ai8j4SCuZ2kc75dAfryQSZDKuv9ISFi9oHyMxEwWKkyNDnnDP~0EW3dBp7qmwPJVbnm7wSQFFU9AUx5o3T742k80q8ZxvS8M-63TZkyb5I3oq6zBUOCVgK471hm2K9gYtYPrwePdoeEP5P4WmIBxeygrqYViN9nw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA

11. National Institute of Standards and Technology. (2023). AI risk management framework (NIST AI RMF 1.0). https://www.nist.gov

12. Anand, L., Tyagi, R., Mehta, V. (2024). Food Recognition Using Deep Learning for Recipe and Restaurant Recommendation. In: Bhateja, V., Lin, H., Simic, M., Attique Khan, M., Garg, H. (eds) Cyber Security and Intelligent Systems. ISDIA 2024. Lecture Notes in Networks and Systems, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-97-4892-1_23

13. Rajurkar, P. (2020). Predictive Analytics for Reducing Title V Deviations in Chemical Manufacturing. International Journal of Technology, Management and Humanities, 6(01-02), 7-18.

14. Sivaraju, P. S. (2022). Enterprise-Scale Data Center Migration and Consolidation: Private Bank's Strategic Transition to HP Infrastructure. International Journal of Computer Technology and Electronics Communication, 5(6), 6123-6134.

15. Kalyanasundaram, P. D., & Paul, D. (2023). Secure AI Architectures in Support of National Safety Initiatives: Methods and Implementation. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 322-355.

16. Ramakrishna, S. (2023). Cloud-Native AI Platform for Real-Time Resource Optimization in Governance-Driven Project and Network Operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6282-6291.

17. Meka, S. (2022). Engineering Insurance Portals of the Future: Modernizing Core Systems for Performance and Scalability. International Journal of Computer Science and Information Technology Research, 3(1), 180-198.

18. Hollis, M., Omisola, J. O., Patterson, J., Vengathattil, S., & Papadopoulos, G. A. (2020). Dynamic Resilience Scoring in Supply Chain Management using Predictive Analytics. The Artificial Intelligence Journal, 1(3).

19. Nagarajan, G. (2023). AI-Integrated Cloud Security and Privacy Framework for Protecting Healthcare Network Information and Cross-Team Collaborative Processes. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6292-6297.

20. Sugumar, R. (2023, September). A Novel Approach to Diabetes Risk Assessment Using Advanced Deep Neural Networks and LSTM Networks. In 2023 International Conference on Network, Multimedia and Information Technology (NMITCON) (pp. 1-7). IEEE.

21. Nguyen, Q. K. (2021). Artificial intelligence in banking: A systematic review of applications and risks. Journal of Economics and Business, 116, 106038. https://doi.org/10.1016/j.jeconbus.2021.106038

22. Thambireddy, S. (2021). Enhancing Warehouse Productivity through SAP Integration with Multi-Model RF Guns. International Journal of Computer Technology and Electronics Communication, 4(6), 4297-4303.

23. Zhang, Y., Chen, M., & Li, S. (2020). 5G-enabled financial technology: Architecture, security, and regulatory challenges. IEEE Network, 34(6), 56–63. https://doi.org/10.1109/MNET.001.2000176

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Published

2024-07-07

How to Cite

A Secure and Compliant AI-Driven Financial Infrastructure Enabled by Fiber Broadband–5G Integration and Cloud-Based Fraud Prevention. (2024). International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8437-8443. https://doi.org/10.15662/IJEETR.2024.0604006