Article ID: | iaor201523654 |
Volume: | 33 |
Issue: | 8 |
Start Page Number: | 611 |
End Page Number: | 626 |
Publication Date: | Dec 2014 |
Journal: | Journal of Forecasting |
Authors: | Hu Zhi-Hua, Sheng Zhao-Han, Song Xin-Ping, Du Jian-Guo |
Keywords: | risk, neural networks, statistics: regression |
This study presents a method of assessing financial statement fraud risk. The proposed approach comprises a system of financial and non‐financial risk factors, and a hybrid assessment method that combines machine learning methods with a rule‐based system. Experiments are performed using data from Chinese companies by four classifiers (logistic regression, back‐propagation neural network, C5.0 decision tree and support vector machine) and an ensemble of those classifiers. The proposed ensemble of classifiers outperform each of the four classifiers individually in accuracy and composite error rate. The experimental results indicate that non‐financial risk factors and a rule‐based system help decrease the error rates. The proposed approach outperforms machine learning methods in assessing the risk of financial statement fraud.