Using machine learning to predict corporate fraud: evidence based on the GONE framework
This study focuses on a traditional business ethics question and aims to use advanced techniques to improve the performance of corporate fraud prediction. Based on the GONE framework, we adopt the machine learning model to predict the occurrence of corporate fraud in China. We first identify a compr...
| Authors: | ; ; |
|---|---|
| Format: | Electronic Article |
| Language: | English |
| Check availability: | HBZ Gateway |
| Journals Online & Print: | Drawer...
|
| Fernleihe: | Fernleihe für die Fachinformationsdienste |
| Published: |
Springer Science + Business Media B. V
2023
|
| In: |
Journal of business ethics
Year: 2023, Volume: 186, Issue: 1, Pages: 137-158 |
| Further subjects: | B
Corporate Fraud
B Aufsatz in Zeitschrift B Machine Learning B GONE |
| Online Access: |
Volltext (lizenzpflichtig) Volltext (lizenzpflichtig) |
| Summary: | This study focuses on a traditional business ethics question and aims to use advanced techniques to improve the performance of corporate fraud prediction. Based on the GONE framework, we adopt the machine learning model to predict the occurrence of corporate fraud in China. We first identify a comprehensive set of fraud-related variables and organize them into each category (i.e., Greed, Opportunity, Need, and Exposure) of the GONE framework. Among the six machine learning models tested, the Random Forest (RF) model outperforms the other five models in corporate fraud prediction. Based on the RF model, we show that Exposure variables play a more important role in predicting corporate fraud than other input variables. These results highlight the importance of Exposure variables in corporate fraud prediction and promote the practical use of the machine learning model in solving business ethics questions. |
|---|---|
| ISSN: | 1573-0697 |
| Contains: | Enthalten in: Journal of business ethics
|
| Persistent identifiers: | DOI: 10.1007/s10551-022-05120-2 |