Cloud-Native Intelligent Data Analytics Platform for Financial Healthcare and Enterprise Decision Intelligence
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Abstract
Enterprises in financial services and healthcare sectors generate massive volumes of data that are critical for operational efficiency, risk management, and decision-making. Traditional analytics platforms often struggle to process this data in real time, resulting in delayed insights and suboptimal decisions. Cloud-native architectures, combined with intelligent data analytics, offer scalable, resilient, and flexible solutions to address these challenges.
This research proposes a cloud-native intelligent data analytics platform designed to support financial risk analysis, healthcare monitoring, and enterprise decision intelligence. The platform integrates advanced analytics, machine learning, and AI-driven predictive models to analyze structured and unstructured datasets across multiple sources. Cloud-native technologies, including microservices, containerization, and orchestration frameworks, enable dynamic scaling, high availability, and fault tolerance.
In the financial domain, the platform identifies potential risks, predicts market trends, and supports fraud detection. For healthcare monitoring, real-time analytics provide early detection of anomalies, predictive health outcomes, and operational optimization for patient care. Enterprise decision intelligence leverages aggregated insights to support strategic planning, resource allocation, and operational efficiency. This framework ensures secure data handling, compliance with regulatory requirements, and enhanced business agility. The study provides architectural design principles, implementation strategies, and evaluation metrics for deploying intelligent, cloud-native analytics platforms across complex enterprise ecosystems.
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References
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