Publication Type

Journal Article

Version

acceptedVersion

Publication Date

10-2025

Abstract

Knowledge Graphs (KGs) are increasingly used in finance to manage complex, interconnected data and support advanced analytics. This survey provides an overview of how KGs are applied across various financial areas, such as fraud detection, credit risk assessment, anti-money laundering, and regulatory compliance. We examine key techniques for building and using KGs in finance, including graph construction, embedding methods, and machine learning models. The survey also discusses challenges specific to finance, like handling private data, ensuring interpretability, and managing real-time data. Additionally, we explore the emerging combination of KGs with large language models and generative AI, which offers new possibilities for financial analysis and decision-making. By summarizing the latest developments, this paper aims to offer a clear view of how KGs are transforming finance and to highlight opportunities for future directions for KG research.

Keywords

Financial knowledge graphsLLMtemporal knowledge graphsfraud detectionmarket trend analysisenterprise risk managementdynamic knowledge graphs

Discipline

Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

World Scientific Annual Review of Artificial Intelligence

Volume

3

First Page

1

Last Page

14

ISSN

2811-0323

Identifier

10.1142/S2811032325300014

Publisher

World Scientific Publishing Co. Pte. Ltd.

Additional URL

https://doi.org/10.1142/S2811032325300014

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