Real-Time AI-Driven Healthcare and Banking Cloud Framework: Integrating Artificial Neural Networks with Oracle EBS, Azure DevOps, and Autonomous Error Detection
DOI:
https://doi.org/10.15662/IJRAI.2025.0806808Keywords:
Artificial Neural Networks, Real-time AI, Healthcare IT, Banking IT, Cloud Framework, Oracle EBS, Azure DevOps, Autonomous Error Detection, Hybrid Cloud, Continuous Integration, Predictive AnalyticsAbstract
In today’s data-rich era, both healthcare and banking sectors face tremendous pressures to deliver fault-tolerant, ultra-responsive services while managing large volumes of sensitive data and adhering to strict regulatory regimes. This paper proposes a novel cloud-based framework that integrates artificial neural networks (ANNs) for real-time predictive decision-making, with enterprise resource planning via Oracle E‑Business Suite (EBS), and continuous integration/continuous deployment (CI/CD) via Azure DevOps, augmented by an autonomous error-detection module for self-healing operations. In healthcare, the framework enables continuous patient‐monitoring, anomaly detection (e.g., adverse events, deterioration) and automated decision-support, while in banking it supports fraud detection, risk scoring, real-time customer service and compliance workflows. The architecture uses a hybrid/multicloud deployment, decoupling the ANN inferencing layer from the ERP core, and embedding a stream-processing module that flags anomalies and triggers automated remediation (via DevOps pipelines) before transaction or clinical workflows fail. The integration with Oracle EBS ensures that AI insights seamlessly influence core operations (e.g., billing, claim processing in healthcare; loan origination, account management in banking) without manual hand-offs. The autonomous error detection component leverages both supervised and unsupervised ANN models to identify deviations from expected behaviour and automatically route remediation tasks into Azure DevOps pipelines for rapid resolution. The paper describes the conceptual architecture, design considerations (data governance, latency, security, model drift), implementation methodology, and potential advantages/disadvantages of the proposed framework. Keywords and introduction follow.
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