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.
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