检测家族企业的会计欺诈:来自机器学习方法的证据

IF 1.2 Q3 BUSINESS, FINANCE
Md Jahidur Rahman , Hongtao Zhu
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引用次数: 0

摘要

本研究的主要目的是利用不平衡集合学习算法检测中国家族企业的会计欺诈行为。该研究首次尝试利用机器学习算法预测家族企业中的舞弊行为,从而填补了家族企业研究中机器学习建模的空白。研究结果表明,与逻辑回归方法相比,集合学习模型在识别会计欺诈方面表现出更高的有效性。此外,不平衡集合学习分类器的表现优于传统模型。值得注意的是,在所有研究的欺诈分类器中,CUSBoost 分类器的整体性能一直是最好的。这项研究将重点从传统的因果推理方法(如回归)转移到基于机器学习的预测技术,从而为家族企业的会计欺诈检测领域做出了贡献。此外,它还扩展了现有的会计欺诈检测文献,强调了欺诈数据集中的数据不平衡问题,并证明了不平衡机器学习算法在检测会计欺诈方面优于传统方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting accounting fraud in family firms: Evidence from machine learning approaches

The primary objective of this research is to detect accounting fraud in Chinese family firms through the utilization of imbalanced ensemble learning algorithms. It serves as the first endeavor to predict fraud in family firms using machine learning algorithms, thus addressing the gap in machine-learning modeling for family business research. The findings of this study demonstrate that the ensemble learning models exhibit superior effectiveness in identifying accounting fraud compared to the logistic regression approach. Moreover, the imbalanced ensemble learning classifiers outperform the conventional models. Significantly, among all the studied fraud classifiers, the CUSBoost classifier consistently attains the best overall performance. This research contributes to the field of accounting fraud detection in family firms by shifting the focus from conventional causal inference methods (such as regression) to machine-learning-based predictive techniques. Additionally, it extends existing literature on accounting fraud detection by emphasizing the issue of data imbalance in fraud datasets and demonstrating the superiority of imbalanced machine learning algorithms over conventional approaches in detecting accounting fraud.

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来源期刊
Advances in Accounting
Advances in Accounting BUSINESS, FINANCE-
CiteScore
2.50
自引率
6.20%
发文量
29
期刊介绍: Advances in Accounting, incorporating Advances in International Accounting continues to provide an important international forum for discourse among and between academic and practicing accountants on the issues of significance. Emphasis continues to be placed on original commentary, critical analysis and creative research.
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