A Multi-Variate Classification Approach with AI-Augmented Gray Relational Analysis and Skew Variation Modeling for Fraud and Risk Intelligence in Cloud Environments
DOI:
https://doi.org/10.15662/IJARCST.2024.0704008Keywords:
AI-Augmented Gray Relational Analysis, Multi-Variate Classification, Skew Variation Modeling,, Fraud Detection, Adaptive Risk Intelligence, Multi-Tenant Cloud Environments, Cloud Security Analytics, Real-Time Risk MonitoringAbstract
In the era of cloud computing and multi-tenant platforms, organizations face increasing challenges in detecting fraudulent activities and managing adaptive risk. This study proposes a novel framework that integrates AI-augmented Gray Relational Analysis (GRA) with skew variation modeling and multi-variate classification techniques to enhance fraud detection and risk intelligence in cloud environments. By leveraging GRA, the framework effectively quantifies relationships among heterogeneous features, while skew variation modeling addresses data distribution asymmetries, improving model sensitivity to anomalous patterns. The multi-variate classification component enables accurate identification and categorization of potential threats across multiple dimensions of risk. Experimental evaluations on simulated and real-world cloud datasets demonstrate that the proposed approach outperforms traditional methods in both detection accuracy and computational efficiency. This research provides a scalable and adaptive solution for real-time risk intelligence in multi-tenant cloud platforms, offering significant implications for cloud security, financial fraud prevention, and enterprise risk management.
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