AI-Powered Banking Cloud Security Framework Integrating Gradient-Boosted Neural Networks for SQL Optimization in SAP and Oracle EBS Healthcare Systems
DOI:
https://doi.org/10.15662/IJEETR.2025.0705003Keywords:
AI-Powered Cloud Security, Gradient Boosting, Artificial Neural Networks (ANN), SQL Optimization, Cybersecurity, Banking Cloud, Healthcare Data Systems, SAP Integration, Oracle E-Business Suite (EBS), Intelligent Data Analytics, Cloud Computing, Fraud Detection, Secure Enterprise ArchitectureAbstract
The rapid evolution of cloud computing within the banking and healthcare sectors has introduced complex challenges in data security, SQL optimization, and enterprise resource integration. This research proposes an AI-powered banking cloud security framework that combines Gradient-Boosted Decision Trees (GBDT) and Artificial Neural Networks (ANNs) to enhance the cybersecurity and performance of integrated SAP and Oracle E-Business Suite (EBS) environments. The framework leverages gradient boosting for intelligent anomaly detection, access control prediction, and real-time intrusion prevention across distributed SQL databases. Concurrently, ANN-based analytics improve transaction monitoring, identify fraud patterns, and enhance query efficiency through adaptive learning models. The hybrid approach optimizes SQL query execution, strengthens data privacy, and facilitates secure interoperability between SAP modules and Oracle EBS for healthcare data management. Experimental simulations validate the model’s effectiveness in achieving higher detection accuracy, reduced latency, and improved scalability compared to traditional rule-based systems. This framework contributes to the advancement of cyber-resilient cloud ecosystems, enabling intelligent, secure, and performance-optimized enterprise infrastructures for the financial and healthcare domains.
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