Financial Fraud Detection of Listed Companies in China: A Machine Learning Approach

Yasheng Chen and Zhuojun Wu ()
Additional contact information
Yasheng Chen: Department of Accounting, School of Management, Xiamen University, Xiamen 361005, China
Zhuojun Wu: Department of Accounting, School of Management, Xiamen University, Xiamen 361005, China

Sustainability, 2022, vol. 15, issue 1, 1-15

Abstract: As the focus of capital market supervision, financial report fraud has shown a development trend of enormous numbers, complex transactions, and hidden means in recent years. To improve audit efficiency and reduce the dependence on non-financial data, the study only uses the structured original data in the financial report to constructs a new fraud identification model, which can quickly detect fraud in China. This study takes the listed companies in China from 1998 to 2016 as research samples and selects 28 sets of raw data from financial reports. Then, this study compares the detection effectiveness of two single classification machine learning algorithms and five ensemble learning algorithms on fraud detection. Compared with single classification machine learning algorithms, the results show that ensemble learning algorithms are generally better at detecting fraud for Chinese listed companies, and the stacking algorithm performs the best. The study results provide direct evidence for rapid fraud detection using financial report raw data and ensemble learning algorithms. The study first proposes a stacking algorithm-based financial reporting fraud identification model for listed companies in China, which provides a simple and effective approach for investors, regulators, and management. It can also provide a reference for the detection of other fraud scenarios.

Keywords: artificial intelligence; machine learning; fraud detection; financial report; financial disclosures; China; securities fraud (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/1/105/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/1/105/ (text/html)

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:gam:jsusta:v:15:y:2022:i:1:p:105-:d:1010523

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().