Subject The article investigates the financial non-compliance in organizations, which includes instances of financial irregularity, legal offense, corporate fraud, and other destructive events of economic life. Objectives The focus is on introducing an effective model to identify instances of financial non-compliance by organizations through testing the tools being the most appropriate for Russian economic conditions. Methods I use the data on 700 Russian organizations, including those that Russian courts found guilty of gross violation of accounting (financial) requirements. I update the equations of tested models on a sample of Russian organizations by using the tools of a logit model in accordance with initial methodology for model development. Results I assessed the classification accuracy of Russian organizations by pilot basic models based on the analysis of financial non-compliance identification, and proved the low accuracy of foreign basic models in Russian economic conditions. It is reasonable to use the findings to improve the mechanism of mitigating the risk of fraudulent actions by economic entities and increasing the transparency of the corporate sector, including through reducing the unintentional misstatement and shrinking the off-the-books economy. Conclusions It is crucial to have effective tools to minimize financial and non-financial damage from financial non-compliance of companies. The paper confirms low accuracy of tested models for identifying financial non-compliance in Russian economic conditions. Updating the equations enabled to enhance the model accuracy, however, it is insufficient for significant reduction of investors' and creditors' losses.
Gudova M.R. [Financial non-compliance in companies: The essence of the concept, formation of a unified approach to this phenomenon]. Vestnik AKSOR, 2017, no. 2, pp. 218–223. (In Russ.)
Beneish M.D. The Detection of Earnings Manipulation. Financial Analysts Journal, 1999, vol. 55(5), pp. 24–36.
Persons O.S. Using Financial Statement Data to Identify Factors Associated with Fraudulent Financial Reporting. Journal of Applied Business Research, 1995, vol. 11, iss. 3, pp. 38–46.
Kirkos E., Spathis Ch., Manolopoulos Y. Data Mining Techniques for the Detection of Fraudulent Financial Statements. Expert Systems with Applications, 2007, vol. 32, iss. 4, pp. 995–1003. URL: Link
Cecchini M., Aytug H., Koehler G., Pathak P. Detecting Management Fraud in Public Companies. Management Science, 2010, vol. 56, no. 7, pp. 1146–1160.
Spathis Ch., Doumpos M., Zopounidis C. Detecting falsified financial statements: A comparative study using multicriteria analysis and multivariate statistical techniques. European Accounting Review, 2002, vol. 11, iss. 3, pp. 509–535. URL: Link
Tarjo, Herawati Nurul. Application of Beneish M-Score Models and Data Mining to Detect Financial Fraud. Procedia – Social and Behavioral Sciences, 2015, vol. 211, pp. 924–930.
Kanapickiene R., Grundiene Z. The Model of Fraud Detection in Financial Statements by Means of Financial Ratios. Procedia – Social and Behavioral Sciences, 2015, vol. 213, pp. 321–327.
Dalnial H., Kamaluddin A., Sanusi Z., Khairuddin K. Accountability in Financial Reporting: Detecting Fraudulent Firms. Procedia - Social and Behavioral Sciences, 2014, vol. 145, pp. 61–69.
Beneish M.D. Detecting GAAP Violation: Implications for Assessing Earnings Management among Firms with Extreme Financial Performance. Journal of Accounting and Public Policy, 1997, vol. 16, iss. 3, pp. 271–309. URL: Link00023-9
Beneish M.D., Marshall C.D., Yang J. Explaining CEO Retention in Misreporting Firms. Journal of Financial Economics, 2017, vol. 123(3), pp. 512–535. URL: Link
Beneish M.D., Vargus M.E. Insider Trading, Earnings Quality, and Accrual Mispricing. The Accounting Review, 2002, vol. 77, no. 4, pp. 755–791. URL: Link
Stepanyan I.K., Dubinina G.A., Nikolaev D.A. et al. Cross-Disciplinary Case-Analyses of Investment Optimization in a Foreign Language Applying Dynamic Programming. Espacios, 2017, vol. 38, no. 62, pp. 19–28. URL: Link
Dutta I., Dutta S., Raahemi B. Detecting Financial Restatements Using Data Mining Techniques. Expert Systems with Applications, 2017, vol. 90, iss. C, pp. 374–393. URL: Link
Glancy F.H., Yadav S.B. A Computational Model for Financial Reporting Fraud Detection. Decision Support Systems, 2011, vol. 50, no. 3, pp. 595–601. URL: Link
Gupta R., Gill N.S. A Data Mining Framework for Prevention and Detection of Financial Statement Fraud. International Journal of Computer Applications, 2012, vol. 50, iss. 8, pp. 7–14. URL: Link
Fanning K., Cogger K.O. Neural Network Detection of Management Fraud Using Published Financial Data. Intelligent Systems in Accounting, Finance & Management, 1998, vol. 7, iss. 1, pp. 21–41. URL: Link1099-1174(199803)7:1<21::AID-ISAF138>3.0.CO;2-K
Feroz E.H., Kwon T.M., Park K., Pastena V.S. The Efficacy of Red Flags in Predicting the SEC's Targets: An Artificial Neural Networks Approach. Intelligent Systems in Accounting, Finance & Management, 2000, vol. 9, iss. 3, pp. 145–157. URL: Link9:3%3C145::AID-ISAF185%3E3.0.CO;2-G
Kim J., Baik B., Cho S. Detecting Financial Misstatements with Fraud Intention Using Multi-Class Cost-Sensitive Learning. Expert Systems with Applications, 2016, vol. 62, iss. C, pp. 32–43. URL: Link
Throckmorton S., Mayew W., Venkatachalam M., Collins L. Financial Fraud Detection Using Vocal, Linguistic and Financial Cues. Decision Support Systems, 2015, vol. 74, pp. 78–87. URL: Link
