A Cloud-Native API-Enabled Fraud Detection System: Grey Relational Insights, Machine Learning, and Generative AI for SAP ERP Threat Intelligence
DOI:
https://doi.org/10.15662/IJRAI.2024.0704005Keywords:
Cloud-native architecture, API management, SAP ERP security, Grey Relational Analysis, Machine learning, Generative AI, Fraud detection, Threat intelligence, Anomaly detection, Enterprise securityAbstract
The increasing complexity of enterprise resource planning (ERP) ecosystems has intensified the need for intelligent, adaptive, and cloud-ready security solutions—particularly for SAP-driven infrastructures that manage mission-critical business operations. This paper proposes a cloud-native API-enabled fraud detection system that integrates Grey Relational Analysis (GRA), Machine Learning (ML), and Generative AI to deliver advanced, real-time threat intelligence for SAP ERP environments. The framework leverages cloud-native APIs to ensure scalable data ingestion, cross-module correlation, and seamless interoperability with SAP’s digital core. GRA is employed to quantify multi-dimensional relationships among transactional behaviors, enabling early anomaly detection and context-aware risk scoring. ML classifiers enhance predictive accuracy by learning behavioral patterns from historical records, while Generative AI models simulate adversarial scenarios, strengthen detection thresholds, and improve system resilience against emerging attack vectors. Experimental evaluations demonstrate that the unified architecture significantly reduces false positives, enhances detection fidelity, and supports adaptive, continuous security monitoring. The proposed solution establishes a robust and explainable analytical pipeline capable of supporting real-time fraud mitigation and proactive threat intelligence across SAP ERP ecosystems.References
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