Detecting Financial Statements Fraud: the Evidence from Russia

Abstract

Russian stakeholders of joint stock companies, which shares are not traded on a stock exchange, and limited liability companies need the effective instruments which enable them to detect the facts of financial statement fraud quickly because the financial statement remains the main source of information about the companies’ performance for them.  Although Institute of Auditors is one of the most reliable tools which identify financial statement manipulations, the costs, connected with audit, are too high and, and as a result, stakeholders have to look for other instruments to distinguish fraudsters, which make an attempt to overestimate or underestimate net assets and financial results, from non-fraudsters. Mathematical model of the American researcher Messod Beneish can be considered as an example of such tools. The general purpose of this paper is to identify whether it is possible, basing on the Beneish model, to create a new one, which enables to distinguish fraudulent from non-fraudulent financial statements reporting in Russia, and determine the accuracy level of fraud status forecasts made by using this model.  In our research we are going to concentrate on identification of companies, which overestimate net assets and financial results. Tо obtain the information on the financial ratios included in the model we use financial reports of Russian both non-traded joint stock companies and limited liability firms. The conclusion can also be drawn that it is possible to develop the fraud detection probit model and linear model (integrated M-score index), which enabled stakeholders to identify fraud status correctly in 83% and 60 % respectively. Developing the model we include extra parameters, connected with growth rate of other income to sales ratio and an accounting policy of the company. It was found that fraud risk increases if the company chooses accounting policy according to which administrative costs are charged to core product expenses.