论文标题
使用图形神经网络链接银行客户端，由丰富的交易数据供电
Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data
论文作者
论文摘要
金融机构获得了有关用户交易和汇款转移的大量数据，这些数据可以被视为大型图表在时间上动态变化。在这项工作中，我们专注于预测银行客户网络中新交互的任务，并将其视为链接预测问题。我们提出了一个新的图形神经网络模型，该模型不仅使用网络的拓扑结构，还使用可用于图节点和边缘的丰富时间序列数据。我们使用大型欧洲银行提供的数据评估开发的方法已有数年。提出的模型优于现有方法，包括其他神经网络模型，在链接预测问题上，ROC AUC得分的差距很大，还可以提高信用评分的质量。
Financial institutions obtain enormous amounts of data about user transactions and money transfers, which can be considered as a large graph dynamically changing in time. In this work, we focus on the task of predicting new interactions in the network of bank clients and treat it as a link prediction problem. We propose a new graph neural network model, which uses not only the topological structure of the network but rich time-series data available for the graph nodes and edges. We evaluate the developed method using the data provided by a large European bank for several years. The proposed model outperforms the existing approaches, including other neural network models, with a significant gap in ROC AUC score on link prediction problem and also allows to improve the quality of credit scoring.
