Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China

Abstract The current research aims to launch effective accounting fraud detection models using imbalanced ensemble learning algorithms for China A‐Share listed firms. Based on a sample of 33,544 Chinese firm‐year instances from 1998 2017, this respectively established one logistic regression and four classifiers (AdaBoost, XGBoost, CUSBoost, RUSBoost) by 12 financial ratios 28 raw data. Additionally, we divided the into train test observations evaluate classifiers' out‐of‐sample performance. In detail, applied two metrics, namely, Area under ROC (receiver operating characteristic) curve (AUC) Precision‐Recall (AUPR), discriminability. supplement test, study put forward an algebraic fused model basis introduced sliding window technique. empirical results showed that can detect A‐listed firms far more effectively than model. Moreover, (CUSBoost performed better common (AdaBoost XGBoost) in average. also obtained highest average AUC AUPR among all employed algorithms. Our offer firm support potential role Machine Learning (ML)‐based Artificial Intelligence (AI) approaches reliably predicting with high accuracy. Similarly, settings, our ML‐based AI offers utmost advantage forecasting fraud. Finally, paper fills gap applications