【摘要】 金融欺诈行为给社会带来了许多负面影响，针对金融欺诈行为，多种人工智能与金融反欺诈算法被提出并应用于实际反欺诈业务场景，取得了不错的成绩。这些反欺诈算法或从用户个体的角度进行欺诈检测，或从节点与网络的拓扑关系的角度进行欺诈检测，或通过学习节点的图嵌入式表示进行欺诈检测，出发角度较为局限，无法进行完备的欺诈检测分析。针对上述问题，设计了一种基于融合多源图特征的Kcore图卷积神经网络反欺诈算法，该算法的创新性在于能够高效挖掘网络中节点层级的拓扑关系与全局网络层次的拓扑关系来构建宽领域的特征体系，并通过基于Kcore算法的图卷积神经网络完成深层次图结构特征的传播与聚合，最终完成欺诈风险的检测。实验效果表明，该方法相较于相关机器学习算法与图神经网络算法在相关评价指标上均有较大的提升，其中较LightGBM算法有12%的AUC值提升，较GCN算法有6%的AUC值提升。更多还原

【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.更多还原