A Dynamic Transaction Pattern Aggregation Neural Network for Money Laundering Detection

Money laundering is a significant problem in the financial system and provides the conditions for financing various crimes. Previous methods apply many flexible algorithms, such as machine learning, graph mining, and anomaly detection. However, most of these contemporary methods do not adequately consider the dynamic characteristics of transactions, which may contain discriminative information for money laundering detection. To address this issue, in this paper, we propose a dynamic transaction pattern aggregation neural network (DTPAN) for money laundering detection. DTPAN utilizes two feature extractors to learn the dynamic features of transaction behaviors and the evolution of transfer relationships between accounts. Furthermore, it employs a feature enhancement module to enhance the behavior dynamic features, capturing the latent dependency between behavior dynamic and relationship evolution. Experimental results obtained with a real-world dataset demonstrate the effectiveness of DTPAN. The results also reveal that DTPAN can enhance the performance of ML detection by adequately exploring the dynamic information of transactions.