Detecting Anomalies in FinTech: A Graph Neural Network and Feature Selection Perspective

The Financial Technology (FinTech) sector has witnessed rapid growth, resulting in increasingly complex and high-volume digital transactions. Although this expansion improves efficiency and accessibility, it also introduces significant vulnerabilities, including fraud, money laundering, and market manipulation. Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data. Graph Neural Networks (GNNs), capable of modeling intricate interdependencies among entities, have emerged as a powerful framework for detecting subtle and sophisticated anomalies. However, the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability, performance, and interpretability. This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech, with an emphasis on the synergistic role of feature selection. We examine the theoretical foundations of GNNs, review state-of-the-art feature selection techniques, analyze their integration with GNNs, and categorize prevalent anomaly types in FinTech applications. In addition, we discuss practical implementation challenges, highlight representative case studies, and propose future research directions to advance the field of graph-based anomaly detection in financial systems.