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This paper examines published data to develop a model for detecting factors associate d with false financia l statements (FFS). Most false financial statements in Greece can be identified on the basis of the quantity and content of the qualification s in the reports filed by the auditors on the accounts. A sample of a total of 76 firms include s 38 with FFS and 38 non-FFS. Ten financial variables are selected for examination as potential predictors of FFS. Univariate and multivariat e statistica l technique s such as logistic regression are used to develop a model to identify factors associate d with FFS. The model is accurate in classifyin g the total sample correctly with accuracy rates exceedin g 84 per cent. The results therefore demonstrat e that the models function effectivel y 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.

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The advent of the IT-led era and the increased competition have forced companies to react to the new changes in order to remain competitive. Enterprise resource planning (ERP) systems offer distinct advantages in this new business environment as they lower operating costs, reduce cycle times and (arguably) increase customer satisfaction. This study examines, via an exploratory survey of 26 companies, the underlying reasons why companies choose to convert from conventional information systems (IS) to ERP systems and the changes brought in, particularly in the accounting process. The aim is not only to understand the changes and the benefits involved in adopting ERP systems compared with conventional IS, but also to establish the best way forward in future ERP applications. The empirical evidence confirms a number of changes in the accounting process introduced with the adoption of ERP systems.

Falsifying financial statements involves the manipulation of financial accounts by overstating assets, sales and profit, or understating liabilities, expenses or losses. This paper explores the effectiveness of an innovative classification methodology in detecting firms that issue falsified financial statements (FFS) and the identification of the factors associated to FFS. The methodology is based on the concepts of multicriteria decision aid (MCDA) and the application of the UTADIS classification method (UTilités Additives DIScriminantes). A sample of 76 Greek firms (38 with FFS and 38 non-FFS) described over ten financial ratios is used for detecting factors associated with FFS. A jackknife procedure approach is employed for model validation and comparison with multivariate statistical techniques, namely discriminant and logit analysis. The results indicate that the proposed MCDA methodology outperforms traditional statistical techniques which are widely used for FFS detection purposes. Furthermore, the results indicate that the investigation of financial information can be helpful towards the identification of FFS and highlight the importance of financial ratios such as the total debt to total assets ratio, the inventories to sales ratio, the net profit to sales ratio and the sales to total assets ratio.