Transaction Graph

Transaction graphs represent financial transactions as networks, enabling the analysis of complex relationships between accounts and the detection of fraudulent activities like money laundering and phishing scams. Current research focuses on developing sophisticated graph-based machine learning models, including graph neural networks (like Graph Convolutional Networks and Graph Attention Networks), and incorporating temporal information to capture evolving transaction patterns. These advancements significantly improve the accuracy and efficiency of detecting illicit financial activities, offering valuable tools for financial forensics and anti-money laundering efforts. The resulting insights have practical implications for enhancing financial security and regulatory compliance.