AI-Driven Financial Fraud Detection: An Integrated Framework for Explainability, Privacy, and Resilience

This paper presents a comprehensive framework for modern financial fraud detection that integrates machine learning, deep learning, graph-based learning, behavioral biometrics, explainable artificial intelligence, federated learning, adversarial defenses, and agentic AI. The study examines how ensemble models such as XGBoost, LightGBM, CatBoost, and stacking architectures support high-volume transaction analysis, while recurrent and graph neural networks capture temporal and relational patterns associated with sophisticated fraud schemes. Behavioral biometrics is considered for continuous authentication, whereas agentic AI is discussed for automated triage, investigation, suspicious activity reporting, and incidentresponse workflows. The paper also addresses asset tracing and recovery, victim notification, model explainability using SHAP and LIME, privacy-preserving federated learning, and adversarial attacks against fraud detection systems. Finally, regulatory considerations, including the EU AI Act, and future directions such as neurosymbolic AI are examined. The framework emphasizes the need for accurate, transparent, privacy-aware, resilient, and accountable fraud detection systems capable of supporting continuous financial crime prevention and compliance