Application of Machine Learning Methods to Risk Assessment of Financial Statement Fraud: Evidence from China |
| |
Authors: | Xin‐Ping Song Zhi‐Hua Hu Jian‐Guo Du Zhao‐Han Sheng |
| |
Institution: | 1. College of Business and Management, Jiangsu University, Zhenjiang, China;2. College of Engineering and Management, Nanjing University, Nanjing, China;3. Logistics Research Center, Shanghai Maritime University, Shanghai, China |
| |
Abstract: | 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. Copyright © 2014 John Wiley & Sons, Ltd. |
| |
Keywords: | financial statement fraud fraud risk assessment fraud risk factors machine learning rule‐based system |
|
|