AI- and Cloud-Driven Real-Time Architectures for Secure Smart Healthcare Finance and Mission-Critical Enterprise Platforms
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Abstract
This paper explores the design and implementation of AI- and cloud-driven real-time architectures that support secure, scalable, and resilient operations across smart healthcare finance and mission-critical enterprise environments. The rapid digital transformation of healthcare and financial ecosystems has led to increased data generation, complex compliance requirements, and heightened cybersecurity risks. To address these challenges, organizations are adopting intelligent cloud-native architectures that integrate artificial intelligence, real-time analytics, and zero-trust security frameworks.
The proposed architecture leverages distributed cloud platforms, federated data pipelines, and deep learning models to enable predictive healthcare analytics, fraud detection, and financial risk management in real time. Stream processing frameworks and microservices enable continuous data ingestion and processing from electronic health records, payment systems, IoT devices, and enterprise applications. Security mechanisms such as encryption, identity-based access control, and compliance automation ensure the protection of sensitive financial and patient data.
This study evaluates the effectiveness of AI-enabled real-time platforms in improving operational efficiency, enhancing decision-making, and ensuring regulatory compliance. Results demonstrate that cloud-native architectures with integrated AI analytics significantly improve scalability, threat detection accuracy, and service reliability. The paper concludes by highlighting the importance of resilient design, governance automation, and cross-domain integration in building future-ready enterprise systems.
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