Open Access

REVIEW

Detecting Anomalies in FinTech: A Graph Neural Network and Feature Selection Perspective

Vinh Truong Hoang^1,*, Nghia Dinh¹, Viet-Tuan Le¹, Kiet Tran-Trung¹, Bay Nguyen Van¹, Kittikhun Meethongjan^2,*

1 AI Lab, Faculty of Information Technology, Ho Chi Minh City Open University, 35-37 Ho Hao Hon Street, Co Giang Ward, District 1, Ho Chi Minh City, 700000, Vietnam
2 Department of Apply Science, Faculty of Science and Technology, Suan Sunandha Rajabhat University, 1 U Thong Nok Rd, Dusit, Dusit District, Bangkok, 10300, Thailand

* Corresponding Authors: Vinh Truong Hoang. Email: ; Kittikhun Meethongjan. Email:

(This article belongs to the Special Issue: Advanced Algorithms for Feature Selection in Machine Learning)

Computers, Materials & Continua 2026, 86(1), 1-40. https://doi.org/10.32604/cmc.2025.068733

Received 05 June 2025; Accepted 29 September 2025; Issue published 10 November 2025

Abstract

The Financial Technology (FinTech) sector has witnessed rapid growth, resulting in increasingly complex and high-volume digital transactions. Although this expansion improves efficiency and accessibility, it also introduces significant vulnerabilities, including fraud, money laundering, and market manipulation. Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data. Graph Neural Networks (GNNs), capable of modeling intricate interdependencies among entities, have emerged as a powerful framework for detecting subtle and sophisticated anomalies. However, the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability, performance, and interpretability. This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech, with an emphasis on the synergistic role of feature selection. We examine the theoretical foundations of GNNs, review state-of-the-art feature selection techniques, analyze their integration with GNNs, and categorize prevalent anomaly types in FinTech applications. In addition, we discuss practical implementation challenges, highlight representative case studies, and propose future research directions to advance the field of graph-based anomaly detection in financial systems.

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