Secure AI Cloud Risk Intelligence for Healthcare ERP: SAP-Integrated Fraud and Threat Control Using Grey Relational Analysis

Pedro Henrique Gomes da Costa

doi:10.15662/IJRAI.2024.0702006

Authors

Pedro Henrique Gomes da Costa Security Architect, Brazil Author

DOI:

https://doi.org/10.15662/IJRAI.2024.0702006

Keywords:

AI cloud security, Grey Relational Analysis, Risk intelligence, Healthcare ERP, SAP integration, Fraud detection, Threat control, Cybersecurity analytics, Anomaly detection, Distributed architecture, In-memory processing, Machine learning

Abstract

This study proposes a secure AI-driven cloud risk intelligence framework designed to enhance fraud detection and adaptive threat control within healthcare Enterprise Resource Planning (ERP) systems. Leveraging Grey Relational Analysis (GRA), the platform identifies complex, nonlinear relationships among financial, clinical, operational, and user-behavior data to reveal anomalous patterns indicative of fraud or cybersecurity threats. The integration with SAP ERP enables real-time data extraction, in-memory processing, and automated risk scoring using AI and machine-learning pipelines deployed in a distributed cloud environment. To address the rising cybersecurity vulnerabilities in healthcare ecosystems, the architecture embeds multi-layered security controls—including encryption, identity and access management, continuous monitoring, and anomaly detection. Experimental evaluation demonstrates improved accuracy in fraud detection, reduced false positives, and enhanced responsiveness to emerging threat vectors. The proposed framework contributes a scalable, secure, and analytically robust approach for healthcare organizations seeking to modernize risk intelligence and safeguard critical ERP assets.

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