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

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. 1 Introduction It is important for the financial institutions to know their client well in order to mitigate credit risks [ Siddiqi, 2012 ], deal with fraud [ Phua et al., 2010 ] and recommend relevant services [ Bruss et al., 2019 ] . One of the defining properties of a particular bank client is his or her social and financial interactions with other people. It motivates to look on the bank clients as on the network of interconnected agents [ Tran et al., 2019; Bruss et al., 2019; Weber et al., 2018 ] .