AI-Powered Fraud Detection in Financial Services: GNN, Compliance Challenges, and Risk Mitigation

Abstract

The rapid evolution of financial fraud, coupled with increasing regulatory scrutiny, challenges conventional fraud detection approaches. Traditional machine learning models struggle to capture complex fraud networks and evolving transactional patterns. This study proposes a Graph Neural Network (GNN)-based fraud detection framework, integrating network science, deep learning, and Explainable AI (XAI) to enhance fraud prevention in financial systems while ensuring compliance with anti-money laundering (AML) and knowyour-customer (KYC) regulations. By structuring financial transactions as graphs, GNNs identify hidden dependencies, collusive fraud, and synthetic identity fraud more effectively than traditional models. We benchmark GNNs against Random Forest and XGBoost, demonstrating superior recall and detection accuracy. Furthermore, we evaluate computational efficiency and real-time feasibility, addressing the scalability challenges of AI-driven fraud detection in highfrequency financial environments. Our findings suggest that integrating GNNs into financial information systems and fintech platforms significantly enhances fraud detection accuracy, risk assessment, and regulatory transparency. This research offers actionable insights for financial institutions, regulators, and AI practitioners, positioning graph-based AI methodologies as a critical innovation for ethical, scalable, and compliant financial crime prevention.

Keywords: Financial Fraud Detection, Graph Neural Networks, Anomaly Detection, AI Ethics, Compliance, Risk Management