AI-Driven Healthcare and Banking Framework Cloud-Native ML Intelligence with Oracle Integration and Secure IAM
DOI:
https://doi.org/10.15662/IJEETR.2024.0606006Keywords:
AI-driven framework, healthcare analytics, banking intelligence, cloud-native architecture, machine learning (ML), Oracle integration, identity and access management (IAM), anomaly detection, predictive analytics, data governance, enterprise automation, cross-domain complianceAbstract
This paper presents an AI-driven, cloud-native framework that unifies healthcare and banking ecosystems through advanced machine learning intelligence, secure identity access management (IAM), and seamless Oracle integration. The proposed architecture leverages real-time data pipelines, predictive analytics, and automated anomaly detection to enhance operational accuracy, reduce manual intervention, and ensure regulatory compliance across both domains. Within healthcare workflows, the system supports clinical decision-making, medical imaging analysis, and patient data validation, while in banking environments it strengthens transaction verification, fraud detection, and financial data governance. Oracle-based interoperability ensures consistent audit trails, traceability, and secure enterprise resource planning, enabling cross-platform data harmonization. Robust IAM mechanisms enforce zero-trust security, role-based access control, and continuous authentication, safeguarding sensitive medical and financial assets in multi-cloud environments. Experimental results demonstrate improvements in detection accuracy, latency reduction, throughput, and end-to-end workflow reliability. The integrated AI–ML–Oracle–Cloud framework establishes a scalable and compliant foundation for next-generation intelligent enterprises.
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