Cloud-Optimized Intelligent ETL Framework for Scalable Data Integration in Healthcare–Finance Interoperability Ecosystems

Abstract

The heterogeneous data has been growing exponentially in the healthcare and financial systems, and this fact has created more pressure to have scalable, intelligent and cloud-optimised data integration structures. In the provided framework, the suggested solution is a cloud-optimised intelligent ETL framework, which is a single platform of AI-based ingestion, semantic normalisation, cloud-native orchestration, and analytics based on machine learning to provide an interoperable interface of the healthcare and finance ecosystem. The methodology has three pillars: (1) AI-based ingestion pipelines through automated cleaning, anomaly detection and semantic alignment of multi-domain, structured and unstructured data; (2) a cloud-native ETL/ELT system of infrastructure comprising distributed data lakes, metadata controls and serverless orchestration; and (3) machine learning and business intelligence layers that provide predictive analytics, asset-liability forecasting, claims processing, fraud detection and pattern of payment analysis.

Large-scale clinical databases, insurance payments, accounting books, assets-liabilities books and running transaction journals were analysed. In summary, the article supports the claim that AI automation, cloud scalability, and metadata-motivated governance may be an efficient and smart ETL system that could reconfigure the data ecosystem of companies and make them interoperable faster, generate their insights quicker, and utilise strategic choices in the healthcare-finance sector.