Economic Analysis: Theory and Practice
 

Logit models to assess the risk of fraudulent misstatements in financial statements of Russian banks

Vol. 16, Iss. 8, AUGUST 2017

Received: 6 June 2017

Received in revised form: 13 July 2017

Accepted: 26 July 2017

Available online: 29 August 2017

Subject Heading: MATHEMATICAL METHODS AND MODELS

JEL Classification: C23, C53, G21, M42

Pages: 1567–1582

https://doi.org/10.24891/ea.16.8.1567

Arzhenovskii S.V. Rostov State University of Economics (RSUE), Rostov-on-Don, Russian Federation sarzhenov@gmail.com

Bakhteev A.V. Rostov State University of Economics (RSUE), Rostov-on-Don, Russian Federation a_bakhteev@mail.ru

Slobodyan A.S. Southern Federal University, Rostov-on-Don, Russian Federation a.slobodyan2011@yandex.ru

Importance The article investigates the possibilities to apply regression models when performing the audit procedures to assess the risk of material misstatement in financial statement due to fraud.
Objectives The aim is to develop mathematical models enabling to assess the risk of material misstatement arising from fraud during the financial audit of Russian banks.
Methods We overview current studies dedicated to model-building to assess the risk of fraud in financial statements, perform a praxeological analysis of information on reasons for financial organizations’ license revocation published by the Bank of Russia. The paper employs econometric modeling using panel data in Stata.
Results We reviewed the existing regression models that help identify and assess the risk of material misstatement in financial statements, prepared a list of reasons for license withdrawal of Russian banks associated with financial statement fraud, offered a five-factor logit model to assess the said risk in financial statements of Russian commercial banks.
Conclusions and Relevance If used, the model will increase the efficiency of audit procedures for assessing the risk of material misstatement due to fraud in the course of financial audit of Russian banks.

Keywords: auditing, fraud, risk of material misstatement, regression model, discrete choice

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