Using Machine Learning to Predict Corporate Fraud: Evidence Based on the GONE Framework

Xin Xu (), Feng Xiong () and Zhe An ()
Additional contact information
Xin Xu: Xiamen University
Feng Xiong: Xiamen University
Zhe An: Monash University

Journal of Business Ethics, 2023, vol. 186, issue 1, No 7, 137-158

Abstract: Abstract 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.

Keywords: Corporate Fraud; Machine Learning; GONE (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10551-022-05120-2

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