Advancing financial risk management: A transparent framework for effective fraud detection

Wenjuan Li; Xinghua Liu; Junqi Su; Tianxiang Cui

doi:10.1016/j.frl.2025.106865

Author(s)

Wenjuan Li
Xinghua Liu
Junqi Su
Tianxiang Cui

Related Research Unit(s)

Department of Information Systems

Detail(s)

Original language	English
Article number	106865
Journal / Publication	Finance Research Letters
Volume	75
Online published	1 Feb 2025
Publication status	Published - Apr 2025

Link(s)

DOI	DOI https://doi.org/10.1016/j.frl.2025.106865 Final Published version
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Attachment(s)	Documents 267753537.pdf Final Published version, 980 KB, PDF Publisher's Copyright Statement This full text is made available under CC-BY-NC 4.0. https://creativecommons.org/licenses/by-nc/4.0/
Link to Scopus	https://www.scopus.com/record/display.uri?eid=2-s2.0-85216651765&origin=recordpage
Permanent Link	https://scholars.cityu.edu.hk/en/publications/publication(768e3512-7bd9-4c67-9102-0334145e019f).html

Abstract

Robust financial fraud detection is crucial for protecting assets and maintaining financial system integrity. Traditional models lack flexibility, while machine learning models are often complex and difficult to interpret. We propose an XGB-GP framework that combines Extreme Gradient Boosting (XGB) and Genetic Programming (GP) to create interpretable models, enhancing fraud detection. Our framework highlights the effectiveness of the financial indicator “Total Liabilities/Operating Costs” and outperforms traditional and machine learning models in detecting fraud, as demonstrated through analysis of data from the CSMAR database of Chinese publicly listed companies.
© 2025 The Authors.