Abstract: Financial fraud has brought many negative impacts to society,and a variety of AI and financial anti-fraud algorithms have been applied to practical anti-fraud business scenarios and have achieved good results.These anti-fraud algorithms either perform fraud detection from the perspective of individual users,or perform fraud detection from the perspective of topological relationship between nodes and network,or perform fraud detection by learning the graph embedded representation of nodes,which are limited in their starting perspectives and cannot perform a complete fraud detection analysis.To address the above problems,this paper designs a Kcore graph convolutional neural network anti-fraud algorithm based on the fusion of multi-source graph features.The innovation of this algorithm lies in the fact that it can efficiently mine the topological relationships at the node level in the network and the topological relationships at the global network level to build a wide-field feature system,and complete the propagation and aggregation of deep-level graph structure features through the graph convolutional neural network based on the Kcore algorithm The final result is the detection of fraud risk.Experimental results show that the method has a large improvement in the evaluation indexes compared with related machine learning algorithms and graph neural network algorithms,including a 12% improvement in the AUC value compared with LightGBM algorithm and a 6% improvement in the AUC value compared with GCN algorithm.

Key words: Machine learning, Graph representation learning, Graph neural network, Financial fraud detection