The Efficacy of Predictive Methods in Financial Statement Fraud

The existence and persistence of financial statement fraud (FSF) are detrimental to the financial health of global capital markets. A number of detective and predictive methods have been used to prevent, detect, and correct FSF, but their practicability has always been a big challenge for researcher...

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Bibliographic Details
Published in:Discrete dynamics in nature and society 2019-01, Vol.2019 (2019), p.1-12
Main Authors: Piri, Muhammad, Moradinaftchali, Vahab, Min, Qingfei, Omidi, Mahdi
Format: Article
Language:English
Subjects:
Accounting
Algorithms
Annual reports
Artificial intelligence
Audit risk
Auditors
Clustering
Data mining
Datasets
Discriminant analysis
Empirical analysis
Finance
Financial misrepresentations
Financial statement analysis
Financial statements
Fraud
Hypothesis testing
Information technology
Investments
Knowledge discovery
Mathematical models
Multilayers
Nanotechnology
Neural networks
Securities markets
Stock markets
Studies
Support vector machines
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Summary:The existence and persistence of financial statement fraud (FSF) are detrimental to the financial health of global capital markets. A number of detective and predictive methods have been used to prevent, detect, and correct FSF, but their practicability has always been a big challenge for researchers and auditors, as they do not address real-world problems. In this paper, both supervised and unsupervised approaches are employed for analysing the financial data obtained from China’s stock market in detecting FSF. The variables used in this paper are 18 financial datasets, representing a fraud triangle. Additionally, this study examined the properties of five widely used supervised approaches, namely, multi-layer feed forward neural network (MFFNN), probabilistic neural network (PNN), support vector machine (SVM), multinomial log-linear model (MLM), and discriminant analysis (DA), applied in different real-life situations. The empirical results show that MFFNN yields the best classification results in detection of fraudulent data presented in financial statement. The outcomes of this study can be applied to different types of financial statement datasets, as they present a practical way for constructing predictive models using a combination of supervised and unsupervised approaches.
ISSN:1026-0226
1607-887X
DOI:10.1155/2019/4989140