Graph Representation Learning of Banking Transaction Network with Edge Weight-Enhanced Attention and Textual Information

Abstract

In this paper, we propose a novel approach to capture inter-company relationships from banking transaction data using graph neural networks with a special attention mechanism and textual industry or sector information. Transaction data owned by financial institutions can be an alternative source of information to comprehend real-time corporate activities. Such transaction data can be applied to predict stock price and miscellaneous macroeconomic indicators as well as to sophisticate credit and customer relationship management. Although the inter-company relationship is important, traditional methods for extracting information have not captured that enough. With the recent advances in deep learning on graphs, we can expect better extraction of inter-company information from banking transaction data. Especially, we analyze common issues that arise when we represent banking transactions as a network and propose an efficient solution to such problems by introducing a novel edge weight-enhanced attention mechanism, using textual information, and designing an efficient combination of existing graph neural networks.