Enhancing corporate governance via machine learning and statistical tools for fraud detection
Advances in Corporate Governance
| Field | Value | |
| Title | Enhancing corporate governance via machine learning and statistical tools for fraud detection | |
| Creator | Salmanov, Tural | |
| Description | Background: Recent trends in machine learning and statistical techniques have revolutionised traditional auditing practices and unveiled new horizons to enhance corporate governance practices.Objectives: This article proposes a hybrid model by combining statistical techniques, such as Benford’s Law and the Beneish M-Score, with machine learning algorithms to detect fraud. Integration of all the methodologies results in a broad, flexible framework for the identification of irregularities and possible fraudulent activities within financial datasets.Method: The research addresses how these advanced tools meet the gaps in traditional auditing practices, thus enabling a more refined approach towards fraud detection.Results: Empirical findings show that this integrated model will improve detection rates, thus strengthening governance structures and promoting transparency within organisations.Conclusion: Major findings suggest that while machine learning algorithms are effective in improving the identification of complex fraud patterns, statistical methods prove to be effective in preliminary screening.Contribution: The article ends with a discussion on implications for auditors and corporate governance structures along with future research recommendations and applications by the industry. | |
| Publisher | AOSIS | |
| Date | 2025-05-15 | |
| Identifier | 10.4102/acg.v2i1.6 | |
| Source | Advances in Corporate Governance; Vol 2, No 1 (2025); 6 pages 3078-2252 | |
| Language | eng | |
| Relation |
The following web links (URLs) may trigger a file download or direct you to an alternative webpage to gain access to a publication file format of the published article:
https://goodgovernancejournal.org/index.php/acg/article/view/6/28
https://goodgovernancejournal.org/index.php/acg/article/view/6/29
https://goodgovernancejournal.org/index.php/acg/article/view/6/30
https://goodgovernancejournal.org/index.php/acg/article/view/6/31
|
|
ADVERTISEMENT
