Each day bank clients conduct numerous operations, such as purchasing goods or transferring money to other clients. These interactions can be interpreted as a graph dynamically changing over time. This work focuses on the task of predicting new interactions in the network of bank clients and treats it as a link prediction problem. We propose an architecture for the graph convolutional network to efficiently solve the link prediction problem for this type of data. Our model uses recurrent neural networks to leverage the time-series data in both nodes and edges and effectively scales to the graphs with millions of nodes. We evaluate the model on the data provided for several years by a large European bank. The obtained results show that the model outperforms the existing approaches. The current paper is an extended abstract for the work [5].

In this paper, we discuss how modern deep learning approaches can be applied to the credit scoring of bank clients. We show that information about connections between clients based on money transfers between them allows us to significantly improve the quality of credit scoring compared to the approaches using information about the target client solely. As a final solution, we develop a new graph neural network model EWS-GCN that combines ideas of graph convolutional and recurrent neural networks via attention mechanism. The resulting model allows for robust training and efficient processing of large-scale data. We also demonstrate that our model outperforms the state-of-the-art graph neural networks achieving excellent results.