Intelligent Real-Time Cloud Architecture for Healthcare–Banking Integration: ANN-Based Autonomous Detection and Correction with Oracle EBS, NLP, and Continuous DevOps Pipelines
DOI:
https://doi.org/10.15662/IJARCST.2025.0806806Keywords:
cloud-native architecture, real-time integration, healthcare-banking convergence, Oracle EBS, natural language processing (NLP), artificial neural network (ANN), autonomous detection, continuous DevOps pipeline, streaming data, financial-healthcare interoperabilityAbstract
In a world where healthcare and banking systems increasingly intertwine—through medical billing, insurance claims, payment platforms, patient financial tracking, fraud detection and risk-management—there is a growing imperative for architectures that support real-time, intelligent, and autonomous integration across these domains. This paper proposes an end-to-end cloud-native architecture that integrates healthcare and banking workflows using the enterprise system Oracle E‑Business Suite (EBS), natural language processing (NLP) of unstructured text (such as clinical notes, billing descriptions, customer service transcripts), and an artificial neural network (ANN)-based autonomous detection and correction engine. Continuous DevOps pipelines facilitate rapid deployment, monitoring, feedback loops and continuous improvement. The architecture supports streaming data ingestion, real-time anomaly/fraud detection and correction (for example claim mismatches, financial irregularities, patient account errors) and cross-domain workflow orchestration. We implement and evaluate a prototype with simulated healthcare billing and banking transaction streams, demonstrating that the ANN engine can detect anomalies with over 95 % accuracy and reduce manual correction time by 60 %. Further, the continuous DevOps deployment enabled micro-iteration of models, rapid rollback and seamless integration with Oracle EBS modules for accounts receivable/payable and customer accounts. The proposed approach outlines key design considerations (scalability, latency, HL7/FHIR and financial ISO 20022 compliance, security, data governance), and discusses the benefits (reduced risk, improved end-to-end visibility, faster correction of errors) and limitations (data quality, integration complexity, regulatory compliance). Keywords highlight the domains of interest, and future work addresses deeper NLP semantics, federated learning across institutions, tighter banking-healthcare regulatory coupling and deployment in production settings.
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