Identifying Fraud Financial Reports Based on Signs of Income Management Using Machine Learning Technology: The Case of Listed Companies in Vietnam

Nguyen Anh Phong, Phan Huy Tam and Nguyen Thanh Tung
Additional contact information
Nguyen Anh Phong: University of Economics and Law, Ho Chi Minh City, Vietnam
Phan Huy Tam: ��Vietnam National University, Ho Chi Minh City, Vietnam
Nguyen Thanh Tung: ��Vietnam National University, Ho Chi Minh City, Vietnam

Journal of International Commerce, Economics and Policy (JICEP), 2024, vol. 15, issue 02, 1-16

Abstract: This study aims to use a measure of earnings management to predict companies whose financial statements have problems. This is an identification measure other than common measures to predict financial statement fraud such as measuring by the M-Score or the Z-score model that many previous studies have applied. In the income management measure, the author uses a measure of abnormal cash flow and abnormal expense flow to consider whether the corporate financial statements have problems or not. To do this, the author uses data from listed non-financial enterprises in the period from 2018 to 2022, with machine learning and deep learning algorithms, of which we focus on three main algorithms: ANN, SVM and RF. The results show that identifying problematic financial statements based on abnormal cash flows is quite effective with an accuracy of over 78% for the SVM method, while if using the RF method, the accuracy reaches over 82% but it is required to accept an increased processing time.

Keywords: Fraud financial statement; machine learning and deep learning methods; listed companies (search for similar items in EconPapers)
JEL-codes: C11 C53 E37 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S1793993324500133
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:jicepx:v:15:y:2024:i:02:n:s1793993324500133

Ordering information: This journal article can be ordered from

DOI: 10.1142/S1793993324500133

Access Statistics for this article

Journal of International Commerce, Economics and Policy (JICEP) is currently edited by Ramkishen S. Rajan

More articles in Journal of International Commerce, Economics and Policy (JICEP) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().