Grey Relational Analysis–Powered AI Cloud Architecture for Multi-Tenant Systems with ML-Based Credit Card Fraud Detection and Risk-Adaptive Multivariate Classification
DOI:
https://doi.org/10.15662/IJRAI.2024.0706023Keywords:
Grey Relational Analysis, Multi tenant Cloud, Big Data Analytics, Credit Card Fraud Detection, Machine Learning, Risk Adapted Analytics, Feature Selection, Ensemble Models, Distributed ComputingAbstract
With the proliferation of digital payments and the exponential growth in credit‑card transactions globally, modern financial institutions face ever-increasing volumes of data (on the order of petabytes) and a growing sophistication of fraud schemes. Traditional rule-based or single-model detection systems often fail to scale or adapt in real time to evolving fraud patterns, especially in multi‑tenant cloud environments shared among multiple clients. This paper proposes a novel, hybrid framework — the Grey Relational Analysis–Driven AI Cloud Framework (GRA‑AI‑Cloud) — designed for petabyte-scale, multi‑tenant infrastructures, integrating multi‑criteria decision-making, unsupervised & supervised machine learning, and dynamic risk‑adapted analytics for credit card fraud detection. The framework employs Grey Relational Analysis (GRA) to preprocess and rank feature‑sets according to their “relational closeness” to ideal fraud and non-fraud behavior profiles, thereby refining feature selection and reducing dimensionality efficiently under high data volume. Afterwards, a distributed ML pipeline running on a multi‑tenant cloud processes transactions in (near) real time, applying ensemble and graph‑based models for fraud detection and risk scoring. The system further supports per‑tenant customization and dynamic risk‑adapted alert thresholds, enabling each client to adjust sensitivity according to their risk tolerance. We evaluate the framework via simulated large-scale transaction datasets (scaled to petabyte‑volume through data generation and sampling) and benchmark detection performance against existing distributed ML fraud detection systems. Preliminary results suggest that GRA‑AI‑Cloud achieves comparable detection accuracy (precision, recall, F1‑score), but substantially improves computational efficiency (feature selection overhead reduced by ∼35%), reduces false positives by ~12%, and enables flexible, tenant‑specific risk adaptation without retraining. The proposed approach demonstrates the viability of combining grey‑system theory with cloud‑native ML architectures for scalable, adaptive fraud detection in real-world financial ecosystems.
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