AI-Driven Risk-Adaptive Cloud Intelligence for Large-Scale Fraud Detection
DOI:
https://doi.org/10.15662/IJEETR.2024.0602004Keywords:
Cloud Computing, Artificial Intelligence, Fraud Detection, Gray Relational Analysis, Big Data Analytics, Software Engineering, Risk-Adaptive Systems, Multi-Tenant Architecture, Apache Frameworks, Intelligent Cloud SystemsAbstract
The rapid growth of cloud-based, multi-tenant enterprise systems has significantly increased the complexity and scale of fraud detection, especially when dealing with petabyte-scale data. Traditional fraud detection approaches often struggle to adapt to dynamic risk patterns, heterogeneous data sources, and large-scale processing requirements. This paper proposes a Risk-Adapted AI-Driven Cloud Intelligence System that integrates Gray Relational Analysis (GRA) with scalable Apache-based big data processing frameworks to enhance fraud detection accuracy and efficiency.The proposed system dynamically evaluates risk factors across multi-tenant environments by analyzing behavioral, transactional, and system-level metrics. Gray Relational Analysis is employed to identify and rank the most influential features contributing to fraudulent activities, enabling adaptive risk-aware decision-making. Leveraging distributed cloud infrastructure, the system supports petabyte-scale data ingestion and parallel processing while maintaining tenant isolation and performance efficiency. Experimental analysis demonstrates improved detection accuracy, reduced false positives, and scalable execution performance compared to conventional rule-based and static machine learning approaches. The results highlight the effectiveness of combining AI-driven analytical techniques with cloud-native software engineering principles for large-scale enterprise fraud detection.
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