This invention relates to the field of intelligent auditing, and more particularly to a graph neural
network method and
system for mining hidden relationships among multiple sets of enterprise accounts. The method includes: acquiring multiple sets of accounts of a target enterprise, extracting entity and transaction information, and constructing a heterogeneous graph network containing multiple types of nodes such as enterprises, individuals,
bank accounts, and transaction vouchers, as well as multiple types of edges such as fund transfers, control, and
voucher associations; inputting the heterogeneous graph into a pre-trained GNN, aggregating neighbor features through graph convolutional
layers, and updating node embedding vectors; based on the embedding vectors, using an attention mechanism to calculate the confidence of hidden relationships between node pairs, and filtering suspicious related node pairs; performing path reasoning and
backtracking on suspicious node pairs, mining multiple paths, and generating a chain of evidence for related paths. This application, by constructing a heterogeneous graph containing multiple types of nodes and edges, comprehensively captures entities such as enterprises, individuals, accounts, and vouchers and their multidimensional relationships, solving the problem that existing methods are unable to comprehensively characterize complex relationship scenarios and capture hidden relationships.