Secure Serverless AI Platforms for Federated Learning and Predictive Analytics in Healthcare Financial and Insurance Enterprise Systems
DOI:
https://doi.org/10.15662/IJRAI.2024.0702007Keywords:
Serverless Computing, Federated Learning, Predictive Analytics, Cloud-Native Architecture, Healthcare Systems, Financial Systems, Insurance Analytics, Data Privacy, Artificial Intelligence, Machine LearningAbstract
The rapid adoption of cloud computing and artificial intelligence has transformed enterprise systems across healthcare, financial, and insurance domains. However, these sectors face persistent challenges related to data privacy, regulatory compliance, scalability, and secure collaboration across organizational boundaries. Traditional centralized machine learning approaches often require data aggregation, which increases security risks and violates domain-specific regulations. To address these challenges, this paper proposes a secure serverless AI platform that integrates federated learning with predictive analytics in a cloud-native environment. The proposed framework leverages serverless computing for elastic scalability, federated learning for privacy-preserving model training, and AI-driven analytics for predictive decision support. The architecture ensures secure data isolation, regulatory compliance, and real-time performance optimization while enabling cross-domain intelligence. Experimental evaluation and qualitative analysis demonstrate improved scalability, reduced latency, and enhanced data privacy compared to centralized AI approaches. The proposed platform provides a robust foundation for next-generation intelligent enterprise systems and supports secure, scalable, and predictive analytics across healthcare, financial, and insurance ecosystems.
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