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.
@article{arxiv.2001.08427,
title = {Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data},
author = {Valentina Shumovskaia and Kirill Fedyanin and Ivan Sukharev and Dmitry Berestnev and Maxim Panov},
journal= {arXiv preprint arXiv:2001.08427},
year = {2020}
}