Robust AI for Financial Fraud Detection in the GCC: A Hybrid Framework for Imbalance, Drift, and Adversarial Threats

The rising complexity of financial fraud in highly digitalized regions such as the Gulf Cooperation Council (GCC) poses challenging issues owing to class imbalance, adversarial attacks, concept drift, and explainability requirements. This paper suggests a hybrid machine-learning framework (HMLF) that incorporates SMOTEBoost and cost-sensitive learning to address imbalances, adversarial training and FraudGAN to ensure robustness, DDM and ADWIN to achieve adaptive learning, and SHAP, LIME, and human-in-the-loop (HITL) analysis to ensure explainability. Employing real transaction data from the GCC banks, the framework is tested through a design science research approach. Experiments illustrate significant gains in fraud recall (from 35% to 85%), adversarial robustness (attack success rate decreased from 35% to 5%), and drift recovery (within 24 h), while retaining operational latency below 150 milliseconds. This paper substantiates that incorporating technical resilience with institutional constraints offers an auditable, scalable, and regulation-compliant solution for detecting fraud in high-risk financial contexts.

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