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Identification of financial violations of Russian organizations: Special aspects and applicability of foreign models

Vol. 18, Iss. 2, FEBRUARY 2019

Received: 13 June 2018

Received in revised form: 4 September 2018

Accepted: 27 November 2018

Available online: 28 February 2019

Subject Heading: FINANCIAL STABILITY AND SOLVENCY

JEL Classification: G17, G32, G34

Pages: 339–352

https://doi.org/10.24891/ea.18.2.339

Fedorova E.A. Financial University under Government of Russian Federation, Moscow, Russian Federation
ecolena@mail.ru

https://orcid.org/0000-0002-3381-6116

Gudova M.R. Financial University under Government of Russian Federation, Moscow, Russian Federation
GudovaMR@gmail.com

ORCID id: not available

Subject The article addresses the financial irregularities of organizations in modern Russian economic realities.
Objectives The aim is to reveal financial irregularities committed by Russian organizations based on the assessment of foreign models applicability.
Methods Calculation of optimal values of financial indicators of the model that detects financial violations through the algorithm of constructing a binary classification tree for a sampling of about seven hundred Russian organizations with almost half of them found guilty of committing financial irregularities.
Results We tested the Beneish model being the most popular one for financial irregularities identification. Due to low predictive accuracy of the basic Beneish model, we adjusted the values of the model indices and the consolidated index, considering the specific aspects of Russian organizations performance with financial irregularities. The adjustments increased the projected accuracy of the model, however, the share of incorrect classifications of organizations with financial irregularities remained high. Therefore, we recommend refining the indicators of foreign models for Russian economic realities.
Conclusions Unfavorable economic conditions in Russia and the significance of damage from financial violations require adequate tools to identify them. Foreign models do not focuse on identification of financial violations that are specific to Russia. Low accuracy of testing results is a signal of the need to develop tools taking into account the Russian economy specificity.

Keywords: financial irregularity, Beneish model, transparency, corporate sector, risks

References:

  1. Gudova M.R. [Financial irregularities of companies: The essence of the concept, building a unified approach to the phenomenon]. Vestnik AKSOR = AKSOR Bulletin, 2017, no. 2, pp. 218–223. (In Russ.)
  2. Beneish M.D. The Detection of Earnings Manipulation. Financial Analysts Journal, 1999, vol. 55, iss. 5, pp. 24–36. URL: Link
  3. 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. URL: Link
  4. Dechow P., Weili Ge, Larson C.R., Sloan R.G. Predicting Material Accounting Misstatements. Contemporary Accounting Research, 2011, vol. 28, iss. 1, pp. 17–82. URL: Link
  5. Kirkos E., Spathis C., 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
  6. Yuh-Jen Chen, Chun-Han Wu, Yuh-Min Chen et al. Enhancement of Fraud Detection for Narratives in Annual Reports. International Journal of Accounting Information Systems, 2017, vol. 26, pp. 32–45. URL: Link
  7. Feruleva N.V., Shtefan M.A. [Detecting the Financial Statements Fraud in Russian Companies: Analysis of Beneish and Roxas Models Applicability]. Rossiiskii zhurnal menedzhmenta = Russian Management Journal, 2016, vol. 14, no. 3, pp. 49–70. URL: Link (In Russ.)
  8. 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
  9. 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. URL: Link
  10. Fedina V.V. [Accounting under IFRS and RAS]. Sovremennaya ekonomika: problemy, tendentsii, perspektivy =Modern Economy: Problems, Trends, Prospects, 2009, no. 2, pp. 93–116. URL: Link (In Russ.)
  11. Abbasi A., Chen H. A Comparison of Fraud Cues and Classification Methods for Fake Escrow Website Detection. Information Technology and Management, 2009, vol. 10, iss. 2-3, pp. 83–101. URL: Link
  12. Albrecht W.S., Albrecht C.O., Albrecht C.C. Current Trends in Fraud and Its Detection. Information Security Journal: A Global Perspective, 2008, vol. 17, iss. 1, pp. 2–12. URL: Link
  13. Barth M., Landsman W., Lang M. International Accounting Standards and Accounting Quality. Journal of Accounting Research, 2008, vol. 46, iss. 3, pp. 467–498. URL: Link
  14. 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
  15. Cecchini M., Aytug H., Koehler G., Pathak P. Detecting Management Fraud in Public Companies. Management Science, 2010, vol. 56, iss. 7, pp. 1146–1160. URL: Link
  16. 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
  17. Ravisankar P., Ravi V., Raghava Rao G., Bose I. Detection of Financial Statement Fraud and Feature Selection Using Data Mining Techniques. Decision Support Systems, 2011, vol. 50, iss. 2, pp. 491–500. URL: Link
  18. Tarjo Herawati N. Application of Beneish M-Score Models and Data Mining to Detect Financial Fraud. Procedia – Social and Behavioral Sciences, 2015, no. 211, pp. 924–930. URL: Link
  19. Spathis C. Detecting False Financial Statements Using Published Data: Some Evidence from Greece. Managerial Auditing Journal, 2002, vol. 17, iss. 4, pp. 179–191. URL: Link
  20. Watson H.J., Wixom B.H. The Current State of Business Intelligence. IEEE Computer, 2007, vol. 40, iss. 9, pp. 96–99. URL: Link

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