AI-Powered Cyber-Secure Federated Learning on AWS for Next-Generation Digital Banking Analytics
DOI:
https://doi.org/10.15662/IJARCST.2024.0703009Keywords:
Federated learning, Digital banking, Cybersecurity, AWS cloud, Predictive analytics, Privacy-preserving AI, Financial risk analyticsAbstract
The rapid digital transformation of banking systems has increased the demand for secure, scalable, and privacy-preserving analytics capable of operating across distributed financial environments. Traditional centralized machine learning approaches face significant challenges related to data privacy, regulatory compliance, and cybersecurity risks. To address these limitations, this paper proposes an AI-powered, cyber-secure federated learning framework deployed on Amazon Web Services (AWS) for next-generation digital banking analytics. The proposed framework enables multiple banking entities to collaboratively train predictive models without sharing raw sensitive data, thereby preserving data confidentiality while improving analytical accuracy. Advanced security mechanisms, including end-to-end encryption, role-based access control, and continuous threat monitoring, are integrated to ensure cyber resilience. The framework supports real-time predictive analytics for use cases such as fraud detection, credit risk assessment, and transaction anomaly detection. Experimental evaluation demonstrates low-latency model updates, robust predictive performance, and strong resistance to simulated cyber threats. The results highlight the framework’s effectiveness in delivering scalable, compliant, and secure AI-driven analytics for modern digital banking ecosystems.
References
1. Bagdasaryan, E., et al. (2020). How to backdoor federated learning. Proceedings of Machine Learning and Systems.
2. Bonawitz, K., et al. (2017). Practical Secure Aggregation for Federated Learning. NIPS.
3. Chen, M., & Zhang, Y. (2017). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences.
4. Davenport, T., & Harris, J. (2017). Competing on Analytics. Harvard Business Review Press.
5. 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.
6. Geyer, R. C., et al. (2017). Differentially Private Federated Learning: A Client Level Perspective. NIPS.
7. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
8. Pichaimani, T., Inampudi, R. K., & Ratnala, A. K. (2021). Generative AI for Optimizing Enterprise Search: Leveraging Deep Learning Models to Automate Knowledge Discovery and Employee Onboarding Processes. Journal of Artificial Intelligence Research, 1(2), 109-148.
9. Krawczuk, P., Papadimitriou, G., Tanaka, R., Do, T. M. A., Subramanya, S., Nagarkar, S., ... & Deelman, E. (2021, November). A performance characterization of scientific machine learning workflows. In 2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS) (pp. 58-65). IEEE.
10. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
11. Praveen Kumar Reddy Gujjala. (2023). Advancing Artificial Intelligence and Data Science: A Comprehensive Framework for Computational Efficiency and Scalability. IJRCAIT, 6(1), 155-166.
12. Mahajan, N. (2023). A predictive framework for adaptive resources allocation and risk-adjusted performance in engineering programs. Int. J. Intell. Syst. Appl. Eng, 11(11s), 866.
13. Nagarajan, G. (2022). Advanced AI–Cloud Neural Network Systems with Intelligent Caching for Predictive Analytics and Risk Mitigation in Project Management. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(6), 7774-7781.
14. Hadden, J., et al. (2007). Customer churn prediction in telecommunications. Expert Systems with Applications.
15. Althati, C., Perumalsamy, J., & Konidena, B. K. (2023). Enhancing life insurance risk models with ai: predictive analytics, data integration, and real-world applications. J Artif Intell Res Appli, 3, 448-86.
16. Meka, S. (2023). Empowering Members: Launching Risk-Aware Overdraft Systems to Enhance Financial Resilience. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7517-7525.
17. Das, D., Vijayaboopathy, V., & Rao, S. B. S. (2018). Causal Trace Miner: Root-Cause Analysis via Temporal Contrastive Learning. American Journal of Cognitive Computing and AI Systems, 2, 134-167.
18. Kumar, R. K. (2023). AI‑integrated cloud‑native management model for security‑focused banking and network transformation projects. International Journal of Research Publications in Engineering, Technology and Management, 6(5), 9321–9329. https://doi.org/10.15662/IJRPETM.2023.0605006
19. Sudhakara Reddy Peram, Praveen Kumar Kanumarlapudi, Sridhar Reddy Kakulavaram. (2023). Cypress Performance Insights: Predicting UI Test Execution Time Using Complexity Metrics. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 6(1), 167-190.
20. Mohile, A. (2022). Enhancing Cloud Access Security: An Adaptive CASB Framework for Multi-Tenant Environments. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7134-7141.
21. Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations (IJRAI), 4(2), 4913–4920. https://doi.org/10.15662/IJRAI.2021.0402004
22. Kusumba, S. (2024). Delivering the Power of Data-Driven Decisions: An AI-Enabled Data Strategy Framework for Healthcare Financial Systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7799-7806.
23. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.
24. Md Al Rafi. (2022). Intelligent Customer Segmentation: A Data- Driven Framework for Targeted Advertising and Digital Marketing Analytics. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7417–7428.
25. Paul, D., Sudharsanam, S. R., & Surampudi, Y. (2021). Implementing Continuous Integration and Continuous Deployment Pipelines in Hybrid Cloud Environments: Challenges and Solutions. Journal of Science & Technology, 2(1), 275-318.
26. Navandar, P. (2022). The Evolution from Physical Protection to Cyber Defense. International Journal of Computer Technology and Electronics Communication, 5(5), 5730-5752.
27. Chandra Sekhar Oleti, " Real-Time Feature Engineering and Model Serving Architecture using Databricks Delta Live Tables" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 6, pp.746-758, November-December-2023. Available at doi : https://doi.org/10.32628/CSEIT23906203
28. Rajurkar, P. (2024). Integrating AI in Air Quality Control Systems in Petrochemical and Chemical Manufacturing Facilities. International Journal of Innovative Research of Science, Engineering and Technology, 13(10), 17869 - 17873.
29. Udayakumar, R., Chowdary, P. B. K., Devi, T., & Sugumar, R. (2023). Integrated SVM-FFNN for fraud detection in banking financial transactions. Journal of Internet Services and Information Security, 13(3), 12-25.
30. Kshetri, N., & Voas, J. (2019). Blockchain in financial services. Computer.
31. Christadoss, J., Yakkanti, B., & Kunju, S. S. (2023). Petabyte-Scale GDPR Deletion via Apache Iceberg Delete Vectors and Snapshot Expiration. European Journal of Quantum Computing and Intelligent Agents, 7, 66-100.
32. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
33. Abdul Azeem, M., Tanvir Rahman, A., Ismoth, Z., KM, Z., & Md Mainul, I. (2022). BUSINESS RULES AUTOMATION THROUGH ARTIFICIAL INTELLIGENCE: IMPLICATIONS ANALYSIS AND DESIGN. International Journal of Economy and Innovation, 29, 381-404.
