Secure LLM-Powered Banking Risk Analytics on SAP Cloud Platforms Supporting Digital Privacy in 5G Web Ecosystems
DOI:
https://doi.org/10.15662/IJEETR.2024.0605011Keywords:
Generative AI, Large Language Models (LLMs), Cloud Security, Banking Risk Analytics, Digital Privacy, 5G Web Platforms, Privacy Preservation, Cybersecurity, Federated LearningAbstract
The advent of generative Artificial Intelligence (AI) and Large Language Models (LLMs) has catalyzed transformational innovation across multiple sectors, particularly in banking risk analytics. When integrated with cloud computing and 5G‑enabled web platforms, these advanced technologies promise exponential improvements in predictive risk modeling, fraud detection, and customer behavior insights. However, this fusion also introduces complex challenges relating to data privacy, cybersecurity, regulatory compliance, and system integrity, particularly within the financial context where sensitive personal and transactional data are processed at scale. This paper presents a comprehensive investigation into the secure deployment of generative AI and LLM‑enabled cloud systems tailored for banking risk analytics over 5G web platforms, examining the interplay between performance capability and digital privacy safeguards. Through a critical literature review, methodological framework, and comprehensive discussion, we explore how cryptographic protections, federated learning, privacy‑by‑design principles, and federated differential privacy can be leveraged to balance analytic performance with user data protection. The findings highlight inherent advantages such as real‑time risk detection and scalability while elucidating disadvantages like attack surface expansion and algorithmic opacity. The paper concludes with strategic recommendations for future research to enhance secure, privacy‑preserving, and compliant generative AI frameworks for the financial services industry.References
1. Gupta, A., & Rao, P. (2017). Differential privacy in financial systems. Journal of Data Security.
2. Nagarajan, G. (2024). A Cybersecurity-First Deep Learning Architecture for Healthcare Cost Optimization and Real-Time Predictive Analytics in SAP-Based Digital Banking Systems. International Journal of Humanities and Information Technology, 6(01), 36-43.
3. 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.
4. 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.
5. Madabathula, L. (2024). Reusable streaming pipeline frameworks for enterprise lakehouse analytics. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8444–8451. https://doi.org/10.15662/IJEETR.2024.0604007
6. Panda, M. R., Selvaraj, A., & Muthusamy, P. (2023). FinTech Trading Surveillance Using LLM-Powered Anomaly Detection with Isolation Forests. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 530-564.
7. Karnam, A. (2023). SAP Beyond Uptime: Engineering Intelligent AMS with High Availability & DR through Pacemaker Automation. International Journal of Research Publications in Engineering, Technology and Management, 6(5), 9351–9361. https://doi.org/10.15662/IJRPETM.2023.0605011
8. Ramakrishna, S. (2024). Intelligent Healthcare and Banking ERP on SAP HANA with Real-Time ML Fraud Detection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(Special Issue 1), 1-7.
9. 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.
10. Kumar, S. N. P. (2022). Machine Learning Regression Techniques for Modeling Complex Industrial Systems: A Comprehensive Summary. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 67–79. https://ijhit.info/index.php/ijhit/article/view/140/136.
11. Kusumba, S. (2023). A Unified Data Strategy and Architecture for Financial Mastery: AI, Cloud, and Business Intelligence in Healthcare. International Journal of Computer Technology and Electronics Communication, 6(3), 6974-6981.
12. Usha, G., Babu, M. R., & Kumar, S. S. (2017). Dynamic anomaly detection using cross layer security in MANET. Computers & Electrical Engineering, 59, 231-241.
13. 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.
14. 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.
15. 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.
16. Lee, Y., & Chen, R. (2015). Cloud deployment security in finance. International Security.
17. Chivukula, V. (2022). Improvement in Minimum Detectable Effects in Randomized Control Trials: Comparing User-Based and Geo-Based Randomization. International Journal of Computer Technology and Electronics Communication (IJCTEC), 5(4), 5442–5446.
18. 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
19. Rajendran, S. (2023). Privacy preserving data mining using hiding maximum utility item first algorithm by means of grey wolf optimisation algorithm.
20. 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.
21. Singh, A. (2023). Network slicing and its testing in 5G networks. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(6), 8005–8013. https://doi.org/10.15680/IJCTECE.2023.0606020
22. Natta, P. K. (2023). Robust supply chain systems in cloud-distributed environments: Design patterns and insights. International Journal of Research and Applied Innovations (IJRAI), 6(4), 9222–9231. https://doi.org/10.15662/IJRAI.2023.0604006.
23. Vengathattil, Sunish. 2021. "Interoperability in Healthcare Information Technology – An Ethics Perspective." International Journal For Multidisciplinary Research 3(3). doi: 10.36948/ijfmr.2021.v03i03.37457.
24. Kumar, S. S. (2024). SAP-Based Digital Banking Architecture Using Azure AI and Deep Learning for Real-Time Healthcare Predictive Analytics. International Journal of Technology, Management and Humanities, 10(02), 77-88.
25. Rao, T., & Singh, R. (2021). Secure federated learning approaches. AI Security Journal.
