Research on the Application Effect of Artificial Intelligence Algorithms in the Detection of Financial Fraud in Listed Companies
Main Article Content
Abstract
Accurate detection of financial fraud remains a critical challenge due to information asymmetry, high-dimensional data complexity, and evolving fraudulent behaviors. This study develops an artificial-intelligence-based financial fraud identification framework for listed companies by integrating multiple machine-learning algorithms. A financial indicator database covering profitability, solvency, cash flow, and governance characteristics is first constructed. Principal Component Analysis is employed to reduce dimensionality and eliminate multicollinearity, followed by the training of Random Forest, Support Vector Machine, and Neural Network models. A fusion-learning strategy with cross-validation optimization is further introduced to enhance model robustness and classification performance. Experimental results demonstrate that the fusion model achieves an accuracy of 94.7%, outperforming individual RF (83.2%), SVM (85.6%), and NN (89.1%) models while maintaining a false-positive rate below 5%. The proposed framework provides effective support for intelligent risk warning and financial supervision and offers methodological references for pattern recognition, data fusion, and intelligent decision-making systems.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
Q. Ye, F. Ye, and S. Huang, “Construction of a framework for identifying financial fraud-based on accounting information system theory and big data perspective,” Accounting Research, no. 3, pp. 3-16, 2022, doi: 10.3969/j.issn.1003-2886.2022.03.001.
S. Tao, H. Zhong, F. Liu, et al., “Research on intelligent financial fraud identification based on three-dimensional integrated index system and residual neural network,” Operations Research and Management, vol. 33, no. 11, pp. 145-151, 2024, doi: 10.12005/orms.2024.0366.
C. Liang, M. Zhang, and S. Gu, “Machine learning-driven financial fraud identification model,” Statistics and Applications, vol. 14, no. 11, pp. 236-248, 2025, doi: 10.12677/sa.2025.1411326.
X. Zhang and Y. Shi, “Research on Financial Fraud Detection Based on Meta-learning,” Journal of Management Science, vol. 26, no. 10, pp. 95-113, 2023, doi: 10.19920/j.cnki.jmsc.2023.10.006.
F. Ye, Q. Ye, and S. Huang, “Identification and Response to Financial Fraud in the Environmental Governance Industry-A Case Study Based on Botian Environment,” Accounting Monthly, vol. 46, no. 20, pp. 96-101, 2025, doi: 10.19641/j.cnki.42-1290/f.2025.20.015.
L. Hernandez Aros, L. X. Bustamante Molano, F. Gutierrez-Portela, et al., “Financial fraud detection through the application of machine learning techniques: a literature review,” Humanities and Social Sciences Communications, vol. 11, no. 1, pp. 1-22, 2024, doi: 10.1057/s41599-024-03606-0.
P. Kamuangu, “A review on financial fraud detection using AI and machine learning,” Journal of Economics, Finance and Accounting Studies, vol. 6, no. 1, pp. 67-77, 2024, doi: 10.32996/jefas.2024.6.1.7.
R. Udayakumar, A. Joshi, S. S. Boomiga, et al., “Deep fraud Net: A deep learning approach for cyber security and financial fraud detection and classification,” Journal of Internet Services and Information Security, vol. 13, no. 3, pp. 138-157, 2023, doi: 10.58346/JISIS.2023.14.010.
Y. Alghofaili, A. Albattah, and M. A. Rassam, “A financial fraud detection model based on LSTM deep learning technique,” Journal of Applied Security Research, vol. 15, no. 4, pp. 498-516, 2020, doi: 10.1080/19361610.2020.1815491.
R. Li, Z. Liu, Y. Ma, et al., “Internet financial fraud detection based on graph learning,” IEEE Transactions on Computational Social Systems, vol. 10, no. 3, pp. 1394-1401, 2022, doi: 10.1109/TCSS.2022.3189368.