Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques

Abstract

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.

Text mining to detect indications of fraud in annual reports worldwide

“…Despite the guidelines, detecting indications of fraud during an audit is challenging owing to the lack of adequate knowledge about the characteristics of fraud, a lack of experienced auditors due to the infrequency of occurrence of fraud and the efforts of a company to hide the fraud (Spathis et al, 2002). The KPMG fraud survey of 2008 (Forensic, 2008) shows that none of the large frauds was detected by the external audit and only 4% by the internal audit.…”

“…The ASB SAS standard requires auditors to assess the risk of fraud in each audit. The three main categories of 'red flags' are: management's characteristics and the attitude of the management toward the internal control system; industry conditions; operating characteristics and financial stability (Spathis et al, 2002). Detecting irregularities that have a material effect on the companies financial statements is considered a responsibility of the auditor (Persons, 1995).…”

“…These activities can be summarized in fraud schemes. Most of the fraud schemes affecting financial reports include overstating revenues and assets and misstating expenses (Dechow et al, 2011;Spathis et al, 2002). The characteristics of regularly encountered and well known fraud schemes are used to develop procedures and methods to detect indications of fraud.…”

“…The application of statistical tests reveals the financial ratios that may be indicative of the presence of financial fraud in a company (Persons, 1995;Spathis et al, 2002). In the subsequent years, the financial ratios formed the input for machine learning techniques, such as Decision Trees, Neural Networks and Bayesian Belief Networks, K-nearest neighbors and Support Vector Machines (SVM) (Kotsiantis et al, 2006;Kirkos et al, 2007).…”

“…The fraud detection research began with a focus on the quantitative information and predefined risk variables. The researchers use various financial ratios developed to measure a company's financial condition (Persons, 1995;Spathis et al, 2002;Kaminski et al, 2004;Kirkos et al, 2007). Examples of risk variables include the number of subsidiaries of a company, whether the company changed its CEO, and wether the company has a poor reputation (Fanning and Cogger, 1998;Bell and Carcello, 2000).…”