InfoResearchPeer-reviewed
AI-Driven Financial Fraud Detection: An Integrated Framework for Explainability, Privacy, and Resilience
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Summary
This paper presents an integrated framework for financial fraud detection that combines ensemble models such as XGBoost, LightGBM, and CatBoost with recurrent and graph neural networks, plus behavioral biometrics, SHAP and LIME explainability, and federated learning. It also examines adversarial attacks on fraud systems and regulatory considerations including the EU AI Act. The paper was published in the International Journal for Research in Applied Science and Engineering Technology (IJRASET) on 2026-09-30.