AN ANTI-FRAUD SCHEME BASED ON GRAPH CONVOLUTIONAL NETWORK FOR NETWORK MICRO-FINANCE
Aiming at the phenomenon of network micro-finance gang fraud,this paper proposes an anti-fraud scheme based on graph convolutional network for network micro-finance.According to the knowledge of credit field,the knowledge graph was defined by the top-down method,and the user network was built by the user call logs.The data was preprocessed to obtain the features of fraud risk.The adjacency matrix and features of the relational network were used as the input of graph convolutional network,and the feature propagation of the second-order neighbors was aggregated to calculate the default probability of the unlabeled node.Experimental results show that compared with the DeepWalk model combined with logistic regression,XGBoost,GBDT classifier,the KS value of our method is improved by 0.214,0.168,0.076 respectively,which distinguishes between positive and negative samples better,and effectively identifies fraud gang.
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