AI-Enabled Interoperable Enterprise Systems Using a Cloud-Native Predictive Analytics Framework for Unified Healthcare and Financial and Insurance Data
DOI:
https://doi.org/10.15662/IJARCST.2024.0701813Keywords:
Cloud-Native Architecture, Predictive Analytics, AI/ML Pipelines, Interoperability, Healthcare Data, Financial Analytics, Insurance Enterprise SystemsAbstract
Enterprises in healthcare, finance, and insurance generate massive volumes of heterogeneous and sensitive data, creating significant challenges for interoperability, predictive analytics, and real-time decision-making. Traditional monolithic architectures often fail to address scalability, data privacy, and cross-domain analytics requirements. This paper proposes a cloud-native predictive analytics framework that enables AI-driven interoperability across healthcare, financial, and insurance enterprise systems. The framework leverages microservices, containerization, serverless computing, and federated learning to ensure secure data sharing and collaborative machine learning while maintaining compliance with regulatory standards such as HIPAA, GDPR, and PCI DSS. It integrates end-to-end AI/ML pipelines for predictive modeling, anomaly detection, and performance optimization, supported by automated monitoring, continuous integration, and observability tools. The framework facilitates unified data ingestion, preprocessing, feature engineering, model deployment, and inference across distributed systems, ensuring operational efficiency and accuracy. Case studies demonstrate enhanced predictive capabilities, improved resource utilization, and real-time insights for patient care, financial risk assessment, and insurance claim prediction. The findings highlight that cloud-native AI platforms not only accelerate innovation and operational efficiency but also ensure data security, interoperability, and compliance across multiple enterprise domains. This study contributes a holistic architecture for building next-generation intelligent enterprise systems capable of unified cross-domain analytics.
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