Detecting Falsified Financial Statements Using Multicriteria Analysis: The Case of Greece

Abstract

This paper develops a model for detecting factors associated with falsified financial statements (FFS). A sample of 76 firms described over ten financial ratios is used for detecting factors associated with FFS. The identification of such factors is performed using a multicriteria decision aid classification method (UTADIS–UTilites Additives DIScriminantes). The developed model is accurate in classifying the total sample correctly. The results therefore demonstrate that the model is effective in detecting FFS and could be of assistance to auditors, to taxation, to Stock Exchange officials, to state authorities and regulators and to the banking system.