Graph AI for Fraud Detection: Improving Risk Management and Compliance in Digital Finance

The increasing sophistication of financial fraud, combined with the rapid expansion of digital financial services, poses significant challenges for conventional fraud detection systems. Traditional machine learning models, which rely on transaction-level features, often fail to capture the intricate relational structures underlying fraudulent behaviors in digital finance ecosystems. This study proposes a Graph Neural Network (GNN)-based fraud detection framework, leveraging network science, deep learning, and Explainable AI (XAI) to improve fraud prevention strategies in fintech platforms, payment systems, and digital lending markets. By modeling financial transactions as structured graphs, GNNs uncover hidden dependencies, collusive fraud rings, and synthetic identity fraud patterns more effectively than conventional fraud detection approaches. A comparative analysis against industry-standard models, including Random Forest and XGBoost, demonstrates that GNNs achieve superior recall and predictive accuracy, particularly in detecting fraudulent behaviors embedded within decentralized financial transactions and peer-to-peer lending networks. Additionally, we assess the computational efficiency and real-time feasibility of GNNs, addressing scalability concerns for high-frequency financial environments such as real-time payment fraud detection and blockchain-based financial transactions. Our findings highlight the potential of integrating GNN-based fraud detection into digital banking, fintech applications, and financial risk management frameworks, enhancing fraud prevention, risk assessment, and regulatory compliance in an increasingly interconnected financial landscape. This research provides actionable insights for financial institutions, fintech innovators, and policymakers, positioning graph-based AI as a foundational technology for secure, scalable, and transparent financial crime prevention in the digital economy.