  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
