Risk-Aware Generative AI and Machine Learning Frameworks for Privacy-Preserving Banking and Trade Analytics over Cloud and 5G Networks
DOI:
https://doi.org/10.15680/IJCTECE.2025.0804011Keywords:
Risk-Aware AI, Generative AI, Machine Learning, Banking Analytics, Trade Analytics, Privacy-Preserving, Cloud Computing, 5G Networks, Fraud Detection, Real-Time AnalyticsAbstract
The digital transformation of banking and trade platforms has accelerated the need for intelligent, privacy-preserving, and risk-aware analytical systems. Modern financial operations involve high-frequency transactions, multi-channel customer interactions, and cross-border trade, all of which generate massive volumes of structured and unstructured data. Conventional risk detection methods are increasingly insufficient to detect sophisticated fraud, cyber threats, and operational anomalies in real time. This study proposes a Risk-Aware Generative AI and Machine Learning Framework that operates over cloud and 5G networks to provide real-time, scalable, and privacy-conscious banking and trade analytics. The framework integrates predictive machine learning models for anomaly detection, generative AI models to simulate rare and high-impact fraud scenarios, and risk-aware scoring mechanisms for adaptive prioritization of alerts. Privacy-preserving mechanisms, including differential privacy and secure multi-party computation, ensure compliance with regulatory standards such as GDPR and PCI DSS. Experimental evaluation on synthetic and real-world datasets demonstrates detection accuracies exceeding 95%, reductions in false positives by up to 40%, and improved operational efficiency. The framework enables proactive risk management, interpretable analytics, and secure real-time insights, providing financial institutions with a robust, scalable solution for high-speed, cloud-based banking and trade analytics in the era of 5G connectivity.
References
1. Dwork, C. (2006). Differential privacy. Journal of Privacy and Confidentiality, 2(1), 1–12.
2. 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.
3. McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS).
4. Kumar, R. (2024). Real-Time GenAI Neural LDDR Optimization on Secure Apache–SAP HANA Cloud for Clinical and Risk Intelligence. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(5), 8737-8743.
5. Vasugi, T. (2023). An Intelligent AI-Based Predictive Cybersecurity Architecture for Financial Workflows and Wastewater Analytics. International Journal of Computer Technology and Electronics Communication, 6(5), 7595-7602.
6. Cherukuri, B. R. (2025). Enhanced trimodal emotion recognition using multibranch fusion attention with epistemic neural networks and Fire Hawk optimization. Journal of Machine and Computer, 58, Article 202505005. https://doi.org/10.53759/7669/jmc202505005
7. Md Manarat Uddin, M., Sakhawat Hussain, T., & Rahanuma, T. (2025). Developing AI-Powered Credit Scoring Models Leveraging Alternative Data for Financially Underserved US Small Businesses. International Journal of Informatics and Data Science Research, 2(10), 58-86.
8. Kasireddy, J.R. (2025). Quantifying the Causal Effect of FMCSA Enforcement Interventions on Truck Crash Reduction: A Quasi-Experimental Approach Using Carrier-Level Safety Data. International journal of humanities and information technology, 7(2), 25-32
9. Akter Tohfa, N., Alim, M. A., Arif, M. H., Rahman, M. R., Rahman, M., Rasul, I., & Hossen, M. S. (2025). Machine learning–enabled anomaly detection for environmental risk management in banking. World Journal of Advanced Research and Reviews, 28(3), 1674–1682. https://doi.org/10.30574/wjarr.2025.28.3.4259
10. 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.
11. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
12. Sugumar, R. (2024). Next-Generation Security Operations Center (SOC) Resilience: Autonomous Detection and Adaptive Incident Response Using Cognitive AI Agents. International Journal of Technology, Management and Humanities, 10(02), 62-76.
13. Natta, P. K. (2024). Autonomous cloud optimization leveraging AI-augmented decision frameworks. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7817–7829. https://doi.org/10.15662/IJEETR.2024.0602005
14. Kusumba, S. (2024). Accelerating AI and Data Strategy Transformation: Integrating Systems, Simplifying Financial Operations Integrating Company Systems to Accelerate Data Flow and Facilitate Real-Time Decision-Making. The Eastasouth Journal of Information System and Computer Science, 2(02), 189-208.
15. Kairouz, P., McMahan, H. B., Avent, B., et al. (2019). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2), 1–210.
16. Shokri, R., & Shmatikov, V. (2015). Privacy preserving deep learning. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security.
17. Madathala, H., Thumala, S. R., Barmavat, B., & Prakash, K. K. S. (2024). Functional consideration in cloud migration. International Peer Reviewed/Refereed Multidisciplinary Journal (EIPRMJ), 13(2).
18. Goodfellow, I., Pouget Abadie, J., Mirza, M., et al. (2014). Generative adversarial networks. Communications of the ACM, 63(11), 139–144.
19. Panda, M. R., & Kumar, R. (2023). Explainable AI for Credit Risk Modeling Using SHAP and LIME. American Journal of Cognitive Computing and AI Systems, 7, 90-122.
20. Madabathula, L. (2023). Scalable risk-aware ETL pipelines for enterprise subledger analytics. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(6), 9737–9745. https://doi.org/10.15662/IJRPETM.2023.0606015
21. Singh, A. (2025). AI-driven autonomous network control planes for large-scale infrastructure networks. International Journal of Computer Technology and Electronics Communication (IJCTEC), 8(6), 11705–11715. https://doi.org/10.15680/IJCTECE.2025.0806015
22. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., et al. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security.
23. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 1–19.
24. Chakraborty, S., Chaudhuri, K., & Y. L. (2018). Adversarial machine learning: A review of attacks and defenses. IEEE Access, 6, 18317–18339.
25. Usha, G., Babu, M. R., & Kumar, S. S. (2017). Dynamic anomaly detection using cross layer security in MANET. Computers & Electrical Engineering, 59, 231-241.
26. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.
27. Gopinathan, V. R. (2024). AI-Driven Customer Support Automation: A Hybrid Human–Machine Collaboration Model for Real-Time Service Delivery. International Journal of Technology, Management and Humanities, 10(01), 67-83.
28. Adari, V. K. (2024). How Cloud Computing is Facilitating Interoperability in Banking and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465-11471.
29. Chivukula, V. (2024). The Role of Adstock and Saturation Curves in Marketing Mix Models: Implications for Accuracy and Decision-Making.. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(2), 10002–10007.
30. Hossain, A., ataur Rahman, K., Zerine, I., Islam, M. M., Hasan, S., & Doha, Z. (2023). Predictive Business Analytics For Reducing Healthcare Costs And Enhancing Patient Outcomes Across US Public Health Systems. Journal of Medical and Health Studies, 4(1), 97-111.
31. Nagarajan, G. (2024). Cloud-Integrated AI Models for Enhanced Financial Compliance and Audit Automation in SAP with Secure Firewall Protection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(1), 9692-9699.
32. Kubam, C. S. (2025). Agentic AI for Autonomous, Explainable, and Real-Time Credit Risk Decision-Making. arXiv preprint arXiv:2601.00818.
33. Kumar, S. S. (2024). Cybersecure Cloud AI Banking Platform for Financial Forecasting and Analytics in Healthcare Systems. International Journal of Humanities and Information Technology, 6(04), 54-59.
34. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.
