A Review on Graph Neural Network Methods in Financial Applications

Volume 20, Issue 2 (2022), pp. 111–134

Pub. online: 2 May 2022
Type: Data Science Reviews
Open Access

Received

3 November 2021

3 November 2021

Accepted

10 April 2022

10 April 2022

Published

2 May 2022

2 May 2022

#### Abstract

With multiple components and relations, financial data are often presented as graph data, since it could represent both the individual features and the complicated relations. Due to the complexity and volatility of the financial market, the graph constructed on the financial data is often heterogeneous or time-varying, which imposes challenges on modeling technology. Among the graph modeling technologies, graph neural network (GNN) models are able to handle the complex graph structure and achieve great performance and thus could be used to solve financial tasks. In this work, we provide a comprehensive review of GNN models in recent financial context. We first categorize the commonly-used financial graphs and summarize the feature processing step for each node. Then we summarize the GNN methodology for each graph type, application in each area, and propose some potential research areas.

#### Supplementary material

Supplementary MaterialIn the supplementary materials, we present materials that are not covered in the main text. The supplementary materials contain the summary table for each financial application, figures categorizing major GNN methodologies for each graph type and acronyms used in the text.

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