Generative AI–Powered Cloud and Machine Learning Architectures for Digital Privacy and Risk Management in Banking and Trade Systems over 5G Networks
DOI:
https://doi.org/10.15662/IJARCST.2023.0604006Keywords:
Generative AI, Cloud Computing, Machine Learning, Digital Privacy, Banking Risk Management, Trade Analytics, 5G NetworksAbstract
The proliferation of digital banking and global trade systems has dramatically increased the volume and velocity of financial data processed daily. At the same time, this growth has intensified concerns over digital privacy, cybersecurity, and real-time risk management. The integration of Generative Artificial Intelligence (AI) and Machine Learning (ML) within scalable cloud computing architectures presents a transformative opportunity to address these challenges, especially when leveraged over 5G network infrastructures that support high-speed, low-latency communication. This research proposes a comprehensive cloud-native framework that harnesses generative AI, advanced ML models, and multi-tenant cloud platforms to deliver robust risk-aware analytics, privacy-preserving computation, and adaptive security mechanisms for banking and trade systems. The framework supports continuous ingestion of transactional and trade data, applies generative modeling for anomaly detection, and uses machine learning for proactive risk scoring. Using a simulated dataset of banking operations and trade transactions, we demonstrate the framework’s performance in detecting fraudulent activities while preserving user privacy through differential privacy techniques and federated learning. Findings show high accuracy in risk prediction and significant improvements in privacy protection without compromising system responsiveness, illustrating the potential of generative AI–powered cloud architectures in modern financial ecosystems.
References
1. Dwork, C. (2006). Differential privacy. Automata, Languages and Programming, 1–12.
2. Sasidevi, J., Sugumar, R., & Priya, P. S. (2017). A Cost-Effective Privacy Preserving Using Anonymization Based Hybrid Bat Algorithm With Simulated Annealing Approach For Intermediate Data Sets Over Cloud Computing. International Journal of Computational Research and Development, 2(2), 173-181.
3. S. Roy and S. Saravana Kumar, “Feature Construction Through Inductive Transfer Learning in Computer Vision,” in Cybernetics, Cognition and Machine Learning Applications: Proceedings of ICCCMLA 2020, Springer, 2021, pp. 95–107.
4. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
5. Sreekala, K., Rajkumar, N., Sugumar, R., Sagar, K. D., Shobarani, R., Krishnamoorthy, K. P., ... & Yeshitla, A. (2022). Skin diseases classification using hybrid AI based localization approach. Computational Intelligence and Neuroscience, 2022(1), 6138490.
6. Kasireddy, J. R. (2022). From raw trades to audit-ready insights: Designing regulator-grade market surveillance pipelines. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 4609–4616. https://doi.org/10.15662/IJEETR.2022.0402003
7. Navandar, P. (2022). The Evolution from Physical Protection to Cyber Defense. International Journal of Computer Technology and Electronics Communication, 5(5), 5730-5752.
8. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 2672–2680.
9. McMahan, H. B., Moore, E., Ramage, D., & Hampson, S. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 54, 1273–1282.
10. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
11. 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
12. Kumar, S. N. P. (2022). Text Classification: A Comprehensive Survey of Methods, Applications, and Future Directions. International Journal of Technology, Management and Humanities, 8(3), 39–49. https://ijtmh.com/index.php/ijtmh/article/view/227/222
13. Ng, A. Y., & Jordan, M. I. (2002). On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes. Advances in Neural Information Processing Systems, 14, 841–848.
14. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.
15. 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.
16. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.
17. Gopalan, R., & Chandramohan, A. (2018). A study on Challenges Faced by It organizations in Business Process Improvement in Chennai. Indian Journal of Public Health Research & Development, 9(1), 337-341.
18. Zhang, H., Wang, Y., & Li, Q. (2020). 5G-enabled cloud applications for real-time analytics. IEEE Communications Magazine, 60(6), 22–28.
19. Singh, A. (2022). Enhancing VoIP quality in the era of 5G and SD-WAN. International Journal of Computer Technology and Electronics Communication, 5(3), 5140–5145. https://doi.org/10.15680/IJCTECE.2022.0503006
20. 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.
21. Panda, M. R., & Kondisetty, K. (2022). Predictive Fraud Detection in Digital Payments Using Ensemble Learning. American Journal of Data Science and Artificial Intelligence Innovations, 2, 673-707.
22. Balaji, K. V., & Sugumar, R. (2022, December). A Comprehensive Review of Diabetes Mellitus Exposure and Prediction using Deep Learning Techniques. In 2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (Vol. 1, pp. 1-6). IEEE.
23. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.
24. Zhang, W., Liu, Y., & Chen, K. (2021). Risk-aware machine learning for financial fraud detection. Journal of Financial Data Science, 3(1), 45–62.
