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Financial institutions obtain enormous amounts of data about usertransactions and money transfers, which can be considered as a large graphdynamically changing in time. In this work, we focus on the task of predictingnew interactions in the network of bank clients and treat it as a linkprediction problem. We propose a new graph neural network model, which uses notonly the topological structure of the network but rich time-series dataavailable for the graph nodes and edges. We evaluate the developed method usingthe data provided by a large European bank for several years. The proposedmodel outperforms the existing approaches, including other neural networkmodels, with a significant gap in ROC AUC score on link prediction problem andalso allows to improve the quality of credit scoring.

Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data