An Intelligent AI/ML Framework for Secure Healthcare–Finance Data Integration and Fraud Prevention in Clouds
DOI:
https://doi.org/10.15662/IJRAI.2023.0606020Keywords:
AI/ML, Fraud Prevention, Healthcare–Finance Integration, Cloud Computing, Secure Data Integration, Anomaly Detection, CybersecurityAbstract
The rapid adoption of cloud computing in healthcare and financial ecosystems has significantly improved data accessibility and interoperability, while simultaneously increasing exposure to fraud, data breaches, and compliance risks. This paper proposes an intelligent AI/ML-based framework designed to enable secure data integration and proactive fraud prevention across interconnected healthcare and finance cloud environments. The proposed framework leverages advanced machine learning algorithms for anomaly detection, predictive risk analysis, and real-time fraud identification, while incorporating encryption, access control, and secure data exchange mechanisms to ensure confidentiality and regulatory compliance. By integrating heterogeneous healthcare and financial datasets through interoperable cloud services, the framework enhances transparency, reduces fraudulent activities, and improves decision-making efficiency. Experimental analysis demonstrates improved fraud detection accuracy, reduced false positives, and scalable performance across distributed cloud infrastructures. The proposed approach offers a robust, secure, and intelligent solution for next-generation healthcare–finance cloud systems.
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