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.
翻译:金融机构获取了大量关于用户交易和资金转移的数据,这些数据可被视为一个巨大的图表,在时间上动态变化。在这项工作中,我们侧重于预测银行客户网络中的新互动,并将它作为联系预测问题处理。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表层结构,而且使用用于图形节点和边缘的丰富时间序列数据。我们使用欧洲大银行提供的数据评估了多年来开发的方法。拟议的模型优于现有方法,包括其他神经网络模型,在拉美哥伦比亚石油公司在连接预测问题的得分方面存在巨大差距,还能够提高信用评分的质量。