Application of Machine Learning Methods to Risk Assessment of Financial Statement Fraud: Evidence from China

Application of Machine Learning Methods to Risk Assessment of Financial Statement Fraud: Evidence from China

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Article ID: iaor201523654
Volume: 33
Issue: 8
Start Page Number: 611
End Page Number: 626
Publication Date: Dec 2014
Journal: Journal of Forecasting
Authors: , , ,
Keywords: risk, neural networks, statistics: regression
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.

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