Presentation information

International Session

International Session » ES-2 Machine learning

[4S1-IS-2f] Machine learning

Fri. Jun 17, 2022 10:00 AM - 11:20 AM Room S (Online S)

Chair: Hisashi Kashima (Kyoto University)

10:00 AM - 10:20 AM

[4S1-IS-2f-01] Transaction prediction by using graph neural network and textual industry information

〇Naoto Minakawa1, Kiyoshi Izumi1, Hiroki Sakaji1, Hitomi Sano1 (1. The University of Tokyo)


Keywords:Graph Neural Network, Natural Language Processing

Transaction data owned by financial institutions can be alternative source of information to comprehend real-time corporate activities.
Such transaction data can be applied to predict stock price and macroeconomic indicator as well as to sophisticate credit management, customer relationship management, and etc. However, it needs attention when a financial institution uses transaction data for aforementioned applications since occurrence of transactions depends on miscellaneous factors including customer loyalty, implying missing potential transactions. To solve this issue, we can predict occurrence of transactions by formulating the problem as a link prediction task in a transaction network with bank accounts as nodes and transaction volume as edges. With the recent advances in deep learning on graphs, we can expect better link prediction. We introduce an approach to predict transaction occurrence by using graph neural network with a special attention mechanism and textual industry information, analyzing the effectiveness of the proposed attention mechanism.

Authentication for paper PDF access

A password is required to view paper PDFs. If you are a registered participant, please log on the site from Participant Log In.
You could view the PDF with entering the PDF viewing password bellow.