Secure Financial Cloud Framework for API-Enabled Real-Time AI Analytics Using Java-Based Deep Learning in Healthcare Systems
DOI:
https://doi.org/10.15680/IJCTECE.2023.0604006Keywords:
Secure API architecture, Healthcare real-time analytics, Financial services, Machine learning, Risk mitigation, Cybersecurity, Cloud-native platformAbstract
The rapid digital transformation of healthcare and financial services has enabled real-time data processing and machine learning analytics, but it has also increased exposure to cybersecurity and operational risks. This paper presents a secure API architecture specifically designed to support healthcare real-time machine learning analytics and risk mitigation in financial services. The proposed framework integrates API-driven communication, cloud-native design, and microservices to enable scalable, low-latency data ingestion and processing. Embedded machine learning models provide predictive analytics, anomaly detection, and automated decision support for both healthcare and financial applications. Security is implemented by design, incorporating encryption, access controls, continuous monitoring, and compliance with regulatory standards such as HIPAA and financial industry regulations. The platform uses real-time APIs to ensure interoperability across heterogeneous systems while maintaining data integrity and confidentiality. Experimental evaluation shows that the architecture delivers high throughput, accurate threat detection, and reduced latency compared to traditional batch-based systems. By embedding security and analytics directly into the system architecture, the framework enhances trust, resilience, and operational efficiency. The study demonstrates the practical applicability of secure, API-driven cloud architectures for mission-critical healthcare and financial services. Findings indicate that organizations can adopt similar frameworks to strengthen real-time analytics, risk management, and regulatory compliance.References
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