Detecting accounting fraud in family firms: Evidence from machine learning approaches

Abstract

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.