A GRA-Enhanced Cloud AI Framework for Petabyte-Scale Multi-Tenant Environments: Multivariate Classification for Credit Card Fraud Detection and Adaptive Risk Analytics
DOI:
https://doi.org/10.15662/IJEETR.2024.0606008Keywords:
Grey Relational Analysis (GRA), Multivariate Classification, Cloud AI Framework, Credit Card Fraud Detection, Adaptive Risk Analytics, Petabyte-Scale Data Processing, Multi-Tenant Cloud Environments, Distributed Machine Learning, Streaming Risk Scoring, Big Data Analytics, Feature Relevance Modeling, Financial Threat IntelligenceAbstract
Petabyte-scale, multi-tenant cloud ecosystems generate massive volumes of heterogeneous data that demand scalable, intelligent, and adaptive risk analytics frameworks. This paper presents a GRA-enhanced Cloud AI framework that integrates multivariate classification models with credit card fraud detection and dynamic risk scoring to support high-throughput, real-time financial security operations. Grey Relational Analysis (GRA) is utilized as a feature relevance mechanism to identify influential behavioral, transactional, and contextual variables across large, multi-tenant datasets, improving both model interpretability and predictive stability.
The proposed architecture leverages distributed cloud services and big data engines to support parallelized ingestion, preprocessing, and analytics over petabyte-scale workloads. Multivariate machine learning classifiers—such as gradient-boosted trees, deep neural networks, stacked ensembles, and hybrid GRA-weighted models—enable robust detection of fraudulent patterns while reducing false alarms in high-dimensional environments. Adaptive risk analytics modules continuously update fraud scores using streaming data, ensuring real-time responsiveness to evolving threat behaviors.
Experimental evaluations demonstrate substantial improvements in fraud detection accuracy, recall, and processing efficiency compared to conventional rule-based and univariate ML systems. The GRA-driven feature optimization enhances model transparency and reduces computational overhead, making the framework suitable for multi-tenant cloud platforms with varied data distributions and performance constraints. This research contributes a scalable, interpretable, and cloud-ready solution for next-generation financial fraud intelligence and risk monitoring at petabyte scale.
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