A framework for identifying the falsified financial statements using network textual analysis: a general model and the Greek example.

This study aims at introducing a social network analysiss based methodology able to predict the financial fraud of companies. For this purpose, data obtained from financial statements and auditors reports for all Greek companies listed on the Athens Exchange during the period 2008–2015 has been used. Nineteen cases of possible fraud were found. These cases comprise the primary research sample with falsified financial statements (FFS). Furthermore, a control sample (non-FFS) consisting of companies with no fraud possibilities was constructed respectively. Various comparisons and several tests were applied, in order to find a network textual analysis model that could serve as a predictor of 'red flags' in the auditing process of financial statements for fraud. The results of this study could be useful to the banking sector, to internal and external auditors, to the tax or other state authorities, and to potential investors or business consultants. [ABSTRACT FROM AUTHOR]