Detecting false financial statements using published data: some evidence from Greece

TLDR

False financial statements in Greece are often identifiable by the quantity and content of auditor qualifications in their reports. The study develops a model to detect factors associated with false financial statements using published data. The authors used a balanced sample of 76 Greek firms, selected ten financial variables, and applied logistic regression to build a predictive model. The model achieved over 84% accuracy and proved effective for detecting false statements, offering useful support to auditors, regulators, and banks.

Abstract

This paper examines published data to develop a model for detecting factors associated with false financial statements (FFS). Most false financial statements in Greece can be identified on the basis of the quantity and content of the qualifications in the reports filed by the auditors on the accounts. A sample of a total of 76 firms includes 38 with FFS and 38 non‐FFS. Ten financial variables are selected for examination as potential predictors of FFS. Univariate and multivariate statistical techniques such as logistic regression are used to develop a model to identify factors associated with FFS. The model is accurate in classifying the total sample correctly with accuracy rates exceeding 84 per cent. The results therefore demonstrate that the models function effectively in detecting FFS and could be of assistance to auditors, both internal and external, to taxation and other state authorities and to the banking system.