Counterfactual Forecasting and Cloud Intelligence Framework using Grey Relational Analysis for Credit Card Fraud Detection and Risk-Adaptive Threat Prediction in Azure Kubernetes Environments
DOI:
https://doi.org/10.15662/IJARCST.2024.0705010Keywords:
Counterfactual Forecasting, Gray/Grey Relational Analysis (GRA), Cloud Intelligence, Credit Card Fraud Detection, Risk-Adaptive Threat Prediction, ; Azure Kubernetes Service (AKS), Causal Inference, Multivariate Classification, Cost-Sensitive Learning, Cloud-Native Security, Scalable Threat AnalyticsAbstract
This paper presents a cloud-native security and analytics framework that integrates Counterfactual Forecasting, Grey Relational Analysis (GRA), and scalable cloud intelligence to enhance credit card fraud detection and risk-adaptive threat prediction in Azure Kubernetes Environments (AKE). As modern financial systems generate high-velocity, high-dimensional transactional data, conventional fraud detection models struggle to capture causal relationships, quantify uncertain outcomes, and adapt to evolving adversarial behaviors. To address these challenges, the proposed framework employs GRA to compute relational strengths between transaction features and fraud indicators, producing interpretable feature-weight profiles that guide both multivariate classifiers and counterfactual inference models. Counterfactual forecasting enables the system to estimate what-if scenarios—such as predicted fraud risk under alternative transaction patterns—thereby improving sensitivity to emerging threats and latent behavioral anomalies
The cloud intelligence layer is deployed using Azure Kubernetes Service (AKS), containerizing preprocessing pipelines, GRA computation engines, forecasting modules, and model inference services within a scalable, secure, autoscaling architecture. The system incorporates risk-adaptive threat prediction by dynamically adjusting model thresholds, cost-sensitive loss functions, and causal feature contributions based on real-time telemetry and drift detection. Experimental results using large-scale credit card transaction datasets demonstrate that combining GRA with counterfactual forecasting improves early-stage fraud detection accuracy, enhances precision-recall performance under extreme class imbalance, and reduces false positive rates by up to 25–35% compared to baseline models. The results indicate that the proposed hybrid framework provides an interpretable, Kubernetes-native, and operationally resilient solution for proactive fraud detection and adaptive threat intelligence in modern cloud-scale financial ecosystems.
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