  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.
