Contrastive Unsupervised Graph Neural Network in Financial Industry

Imran Babayaro

doi:10.70393/6a6574626d.323437

OPEN ACCESS |Research Article ||16 December 2024

Contrastive Unsupervised Graph Neural Network in Financial Industry

Imran Babayaro

Financial System Research Centre

imran.babayaro@outlook.com

Financial System Research Centre, Canada.

* Corresponding Author¹: Imran Babayaro, E-Mail: imran.babayaro@outlook.com

Publication

Accepted Unknow ; Published 2024 December 16

Journal of Economic Theory and Business Management, 2024, 1(6), 3006-4953.

Abstract

This paper explores the application of the Contrastive Unsupervised Graph Neural Network (CuGNN) framework in financial domains, leveraging its heterophily-based adaptive convolution to address critical tasks like fraud detection, risk propagation, and portfolio optimization. CuGNN's ability to identify and utilize heterophilic patterns in financial transaction graphs enables robust representation learning even in unsupervised settings, where labeled data is scarce. Specifically, we adapt CuGNN to model risk spread across trading and investment networks by dynamically capturing high-frequency and low-frequency signals through its adaptive convolution mechanism. This approach allows us to differentiate between correlated and inverse-correlated asset behaviors, providing deeper insights into systemic risks and diversification strategies. Furthermore, CuGNN’s feature-distribution embedding and latent-space contrastive learning strategies are utilized to detect anomalous interactions in high-frequency trading networks and to identify heterophilic relationships in credit scoring and supply chain finance. By applying the CuGNN framework across diverse financial datasets, this study demonstrates its potential to uncover hidden structures and optimize decision-making in critical financial applications, addressing the growing need for explainable and adaptive graph-based methods in the sector.

Keywords

Graph Neural Networks , Heterophily in Graphs , Unsupervised Learning , Financial Networks , Risk Propagation , Fraud Detection .

Metadata

DOI:

10.70393/6a6574626d.323437

ARK:

ark:/40704/JETBM.v1n6a04

Pages: 25-32

References: 51

Disciplines: Finance

Subjects: Risk Management

Cite This Article

APA Style

Babayaro, I. (2024). Contrastive unsupervised graph neural network in financial industry. Journal of Economic Theory and Business Management, 1(6), 25-32. https://doi.org/10.70393/6a6574626d.323437

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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