Contrastive Unsupervised Graph Neural Network in Financial Industry

Imran Babayaro

doi:10.70393/6a6574626d.323437

Authors

Imran Babayaro Financial System Research Centre

DOI:

https://doi.org/10.70393/6a6574626d.323437

ARK:

https://n2t.net/ark:/40704/JETBM.v1n6a04

Disciplines:

Finance

Subjects:

Risk Management

References:

51

Keywords:

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

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.

Author Biography

Imran Babayaro, Financial System Research Centre

Financial System Research Centre, Canada.

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