An AI-Driven Cloud-Based Real-Time Analytics Architecture for Risk-Aware Financial and Healthcare Decision Making
DOI:
https://doi.org/10.15662/IJEETR.2023.0503004Keywords:
AI-Driven Analytics, Cloud Computing, Real-Time Data Processing, Risk-Aware Decision Making, Machine Learning, Financial Analytics, Healthcare Analytics, Predictive Risk Modeling, Intelligent SystemsAbstract
The rapid growth of data in financial and healthcare systems demands intelligent, real-time analytics to support accurate and risk-aware decision making. Traditional data processing approaches often fail to handle high-velocity, heterogeneous data while providing timely insights for critical decisions. This paper presents an AI-driven, cloud-based real-time analytics architecture designed to enable risk-aware decision making across financial and healthcare domains.The proposed architecture integrates cloud computing, machine learning, and artificial intelligence to process streaming and batch data in real time, ensuring scalability, low latency, and high availability. Advanced machine learning models are employed for risk prediction, anomaly detection, and forecasting, while AI techniques enhance decision intelligence through adaptive learning and explainability. In the financial domain, the system supports fraud detection, credit risk assessment, and market risk analysis. In healthcare, it enables early disease risk prediction, patient monitoring, and clinical decision support.
Security, privacy, and regulatory compliance are incorporated through encryption, access control, and policy-driven governance. Experimental analysis demonstrates that the architecture improves decision accuracy, reduces response time, and enhances operational efficiency. The proposed framework provides a unified, scalable solution for intelligent, real-time, and risk-aware decision making in modern financial and healthcare ecosystems.
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