Feature extraction of companies from business relationship graphs by GCN

Abstract

Financial institutions have a large amount of data on money transfer among companies. With these data, they can construct graphs that represent the business relationships of the companies. If they can predict a rating of a company's repayment capacity from the graphs, they can make more appropriate loan decisions and find new customers. In this study, we propose a method applying extended Graph Convolutional Networks to business relationship graphs representing the continuity of transactions to predict the ratings. As a result of applying this method to actual data, it was indicated that this method automatically extracted features necessary for the prediction of the rating.