Next-Generation Banking and Healthcare Cloud Infrastructure: Real-Time Autonomous Detection via Neural Networks, NLP, and Data Mining Integrated with Oracle EBS and GitHub DevOps Automation

Abstract

In the rapidly evolving domains of banking and healthcare, organisations must adopt cloud-native infrastructures that support real-time autonomous detection of anomalies, fraud, operational risk and clinical events. This paper proposes a unified architecture that integrates neural networks, natural-language processing (NLP) and data-mining techniques within a cloud infrastructure, bridging both banking and healthcare verticals, and anchored by enterprise systems such as Oracle E‑Business Suite (EBS) and DevOps pipelines using GitHub for automation. The approach addresses the challenges of heterogeneous data sources (transactional banking data, clinical records, unstructured text, logs), high-velocity ingestion, regulatory compliance, auditability and continuous deployment. Neural networks facilitate pattern recognition and predictive detection (e.g., fraud, medical emergencies), NLP enables extraction of insights from unstructured text (customer complaints, clinical notes), and data mining uncovers latent correlations across domains (e.g., risk factors in both banking & health portfolios). The proposed system leverages cloud elasticity for scalability and fault-tolerance, and automates software delivery and configuration via GitHub-based DevOps pipelines integrated with Oracle EBS modules. A pilot simulation demonstrates improved detection latency, higher true-positive rates, and streamlined release cycles. Key benefits include faster incident detection, integrated cross-domain insights and operational agility; drawbacks include initial complexity, data governance overhead and potential model drift. The paper concludes with implementation guidelines, results of the simulation, and future work directions including federated learning, cross-organisational data sharing and self-healing infrastructure.