Predicting financial statement fraud using fuzzy neural networks

Document Type: Research Paper


1 Department of Mathematics, Faculty of Science, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Management and Economics, Science And Research Branch, Islamic Azad University ,Tehran,Iran

3 Department of Management and accounting, Allameh Tabataba’i University, Tehran, Iran



Fraud is a common phenomenon in
business, and according to Section 24 of
the Iranian Auditing Standards, it is the
fraudulent act of one or more managers,
employees, or third parties to derive
unfair advantage and any intentional or
unlawful conduct. Financial statements
are a means of transmitting confidential
management information about the
financial position of a company to
shareholders and other stakeholders. In
this paper, by reviewing the literature, 6
indicators of current ratio, debt ratio,
inventory turnover ratio, sales growth
index, total asset turnover ratio, and
capital return ratio as input and detection
of financial fraud as output are
considered for the fuzzy neural network.
The database was compiled for 10
companies in the period from 2010 to
2018 after clearing and normalizing
qualitatively between 1 to 5 discrete
numbers with very low or very high
meanings, respectively. The fuzzy neural
network model with 161 nodes, 448
linear parameters, 36 nonlinear
parameters, and 64 fuzzy laws with two
methods of accuracy approximation of
mean squared error and root mean squared error has been set to zero and
0.0000001 respectively. This neural
network can be used for prediction.


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