Intelligent Scalable Cloud Framework Leveraging AI, Oracle, and SAP for Data-Driven Banking ETL Processes
DOI:
https://doi.org/10.15662/IJARCST.2025.0806009Keywords:
AI-driven optimization, Oracle Database, SAP Financial Cloud, Banking Infrastructure, Reinforcement Learning, Predictive Analytics, Cloud Workflow, Database Performance, Financial Technology (FinTech)Abstract
The increasing digitalization of banking operations has necessitated efficient and intelligent data management strategies within cloud-based enterprise systems such as SAP Financial Cloud. Traditional database optimization techniques, while effective in static environments, often struggle with the dynamic, high-volume, and latency-sensitive nature of modern financial transactions. This paper explores an AI-driven optimization framework for Oracle databases underpinning SAP Financial Cloud workflows in banking infrastructure. The approach integrates predictive analytics, self-learning algorithms, and adaptive indexing techniques to enhance database query performance, minimize downtime, and optimize storage allocation. Using a hybrid model combining reinforcement learning and heuristic-driven database tuning, this study demonstrates how AI can dynamically adjust indexing, caching, and partitioning strategies based on real-time transaction workloads. The proposed system achieves measurable performance improvements, including up to 40% faster query execution and 25% reduction in resource utilization. Furthermore, this research examines the security implications, scalability potential, and cost-effectiveness of AI-driven optimization in comparison with conventional Oracle tuning practices. The integration of AI-based automation not only streamlines data operations but also aligns with financial compliance and auditing standards by ensuring consistency and traceability of data flows. Ultimately, the findings indicate that AI-enhanced Oracle optimization serves as a critical enabler for the next generation of intelligent, resilient, and cloud-native banking infrastructures.
References
1. Chen, L., & Liu, Y. (2023). Reinforcement learning for adaptive database optimization in cloud environments. Journal of Cloud Computing, 12(3), 144–158.
2. Sampath Kumar Konda, “Distributed AI Infrastructure Orchestration: A Hyperscale Multi-Cloud Framework for Geographic Load Balancing with Renewable Energy Optimization”, Int J Sci Res Sci Eng Technol, vol. 11, no. 4, pp. 522–533, Aug. 2024, doi: 10.32628/IJSRSET242438.
3. Gupta, R., & Mehta, A. (2022). Integrating AI with SAP Financial Cloud for digital banking transformation. International Journal of Information Systems, 19(4), 311–329.
4. Dave, B. L. (2025). ENHANCING TRANSPARENCY AND AGILITY IN SOCIAL WORK SERVICES VIA THE
SWAN PLATFORM. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(2), 11778-11783.
5. Vegineni, G. C. (2025). Automated Eligibility and Enrollment Workflows a Convergence of AI and Cybersecurity. International Journal of Emerging Trends in Computer Science and Information Technology, 245-251.
6. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(6), 4305-4311.
7. Chiranjeevi, Y., Sugumar, R., & Tahir, S. (2024, November). Effective Classification of Ocular Disease Using Resnet-50 in Comparison with Squeezenet. In 2024 IEEE 9th International Conference on Engineering Technologies and Applied Sciences (ICETAS) (pp. 1-6). IEEE.
8. Jain, P., & Rao, K. (2024). AI-based fraud detection in financial database systems. Financial Technology Review, 11(1), 65–78.
9. Batchu, K. C. (2025). Metadata-Driven ETL Framework for Automated Schema Evolution and Impact Analysis. Journal of Computer Science and Technology Studies, 7(7), 846-852.
10. Kumar, S., & Patel, D. (2021). Automated database tuning through machine learning: A comparative study. IEEE Access, 9, 102345–102358.
11. Kumar, L. M. S. (2025). Security Across Services in Microservice Architecture. International Journal of Computer Science and Engineering Research and Development (IJCSERD), 15(3), 89-101.
12. Nair, V., Thomas, L., & Singh, R. (2023). Performance tuning of SAP HANA and Oracle databases in hybrid cloud systems. Cloud Computing Letters, 4(2), 121–136.
13. Venkata Ramana Reddy Bussu,, Sankar, Thambireddy, & Balamuralikrishnan Anbalagan. (2023). EVALUATING THE FINANCIAL VALUE OF RISE WITH SAP: TCO OPTIMIZATION AND ROI REALIZATION IN CLOUD
ERP MIGRATION. International Journal of Engineering Technology Research & Management (IJETRM), 07(12), 446–457. https://doi.org/10.5281/zenodo.15725423
14. Reddy, B. V. S., & Sugumar, R. (2025, April). Improving dice-coefficient during COVID 19 lesion extraction in lung CT slice with watershed segmentation compared to active contour. In AIP Conference Proceedings (Vol. 3270, No. 1, p. 020094). AIP Publishing LLC.
15. Sanepalli, Uttama Reddy. (2023). Distributed Multi-Cloud Data Lake Architecture for Enterprise-Scale Workplace Benefits Analytics: A Federated Approach to Heterogeneous Financial Data Integration. International Journal of Computer Engineering and Technology (IJCET), 14(1), 268-282.
16. Smith, J., & Johnson, E. (2020). Oracle database performance tuning for enterprise financial systems. Journal of Database Management, 31(1), 45–62.
17. Manda, P. (2025). Optimizing ERP resilience with online patching: A deep dive into Oracle EBS 12.2. x ADOP architecture. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(1), 11786-11797.
18. Ahmed, M., & Lee, D. (2021). Database self-tuning using reinforcement learning: A survey. Expert Systems with Applications, 168, 114347.
19. Arjunan, T., Arjunan, G., & Kumar, N. J. (2025, May). Optimizing Quantum Support Vector Machine (QSVM) Circuits Using Hybrid Quantum Natural Gradient Descent (QNGD) and Whale Optimization Algorithm (WOA). In 2025 6th International Conference for Emerging Technology (INCET) (pp. 1-7). IEEE
20. Kotte, K. R. (2025). Lean Manufacturing and Financial Performance a Study on Cost Reduction Strategies. International Journal of Emerging Research in Engineering and Technology, 41-49.
21. Tan, C., & Ho, W. (2023). SAP S/4HANA and AI convergence in cloud-native banking systems. Journal of FinTech Transformation, 5(1), 33–49.
22. Niture, N. (2025). AI-Augmented Infrastructure Governance: Intelligent Risk Detection in Identity-Centric Cloud Platforms. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(2), 11802-11814.
23. 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.
24. Park, J., & Sun, H. (2022). Adaptive workload management in financial databases. Data Engineering Journal, 16(4), 201–217.
25. Ireddy, R. K. (2024). Deep learning architecture for banking risk management: Cloud and AI-driven predictive analytics solution. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/CSEIT24113395
26. Gosangi, S. R. (2023). Reimagining Government Financial Systems: A Scalable ERP Upgrade Strategy for Modern Public Sector Needs. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8001-8005.
27. 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.
28. Rahman, F., & Osei, K. (2024). AI-enhanced Oracle database automation for financial cloud infrastructures. Cloud and Data Systems Review, 9(2), 140–159.
29. Nair, S. G. (2025). Cloud Reliability Engineering for Design Collaboration Platforms: Building 99.99% Availability with Multi-Region Failover. International Journal of Communication Networks and Information Security, 17(8), 66-72.
30. Li, X., & Zhang, H. (2023). Predictive indexing for AI-driven data systems. Proceedings of the IEEE International Conference on Big Data, 112–124.
