Secure Healthcare Data Exchange and Financial Fraud Detection Using AI-Driven Cloud Data Integration

Abstract

The rapid digitization of healthcare systems and the migration of sensitive clinical and financial data to cloud environments have significantly improved interoperability, scalability, and operational efficiency. However, this transformation has simultaneously increased exposure to cyber threats, data breaches, and financial fraud, including insurance fraud, billing manipulation, and unauthorized access to protected health information (PHI). Traditional security mechanisms and rule-based fraud detection systems are increasingly inadequate in addressing the scale, complexity, and evolving nature of these threats. This paper proposes an integrated framework for secure healthcare data exchange and financial fraud detection using artificial intelligence (AI)-driven cloud data integration. The framework combines machine learning-based fraud analytics, secure cloud architectures, encryption mechanisms, and network-level monitoring to enable real-time detection and prevention of anomalous activities. We examine how AI models leverage heterogeneous healthcare data sources—such as electronic health records (EHRs), insurance claims, payment transactions, and access logs—to identify fraudulent patterns while maintaining compliance with data protection regulations. A comprehensive methodology covering data ingestion, feature engineering, model training, explainability, and deployment is presented. Experimental results and literature-backed evaluations demonstrate that AI-driven approaches significantly outperform traditional methods in detection accuracy and response time. The study concludes with insights into current limitations and outlines future research directions, including federated learning and privacy-preserving analytics for secure healthcare ecosystems.