Abstract: The asset management industry,characterized by high-frequency and flexible financial operations,has become a primary target for money laundering activities.However,the sparsity of transaction structures,the complexity of implicit fund transfer paths between accounts,and the non-uniform characteristics of transaction behaviors pose significant challenges for traditional graph modeling methods based on explicit relationships.To address these issues,this paper proposes an Anti-money Laundering Detection Framework for Asset Management based on Temporal Graph Neural Networks(AM-GAML).By integrating temporal models with graph neural networks,the proposed framework constructs a joint temporal-structural embedding representation and designs a graph generation mechanism based on implicit interaction relationships.This enables the framework to effectively capture weakly correlated features in transaction records and uncover complex transaction behavior patterns between users.Experimental results on a real-world transaction dataset demonstrate that AM-GAML significantly outperforms several advanced approaches in terms of accuracy,recall,F1-score,and AUPRC.The framework excels particularly in minority class detection and generalization ability.The proposed method provides an efficient and reliable solution for anti-money laundering detection in the asset management industry and offers valuable support for risk prevention and control research in complex financial scenarios.

Key words: Asset management,Anti-money laundering,Temporal model,Graph neural network,Transaction behavior analysis