Fiscal And Accounting Fraud Risk Detection Using Beneish Model. A Romanian Case Study

  • Timofte Cristina Coca Ştefan cel Mare University
  • Socoliuc Marian Ştefan cel Mare University
  • Grosu Veronica Ştefan cel Mare University
  • Coca Dan-Andrei Ştefan cel Mare University
Keywords: Beneish Model, Fraud/Tax Evasion Risk, Information Manipulation, Romania


The manipulation of the accounting and fiscal information is currently a much debated reality that occurs throughout economies and societies all over the world. The main purpose of this paper is focused on shaping and obtaining a model that can detect fraud/tax evasion risk, that could be useful both to fiscal authorities as part of the risk assessment analysis regarding the taxpayer behavior, and to auditors and even to entities from the private sector in the due diligence phase, when selecting potential business partners. The study focuses on regional data from the North-Eastern part of Romania. The main finding is that such a model should include financial, fiscal and nonfinancial variables.


Artavanis N., Morse A., & Tsoutsoura M. (2015). Measuring Income Tax Evasion using Bank Credit: Evidence from Greece. Chicago Booth Research Paper No. 12-25. Fama-Miller Working Paper.

Beneish, M. D. (1999). The Detection of Earnings Manipulation. Financial Analysts Journal, 55(5), 24–36. doi:

Berry, M., & Linoff, G. (1997). Data Mining Techniques for Marketing Sales and Customer Support. New Jersey: Wiley.

Bunget, O. C., & Dumitrescu A. C. (2008). Accounting Treatment of Deferred Income Taxes According to The Requirements of The Romanian Accounting Regulations. The Annals of University of Oradea – Economic Science, XVII(3), 1039-1043. Retrieved from pdf

Cecchini, M., Aytug, H., Koehler, G. J., & Pathak, P. (2010). Making words work: Using financial text as a predictor of financial events. Decision Support Systems, 50(1), 164–175. doi:

Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting Material Accounting Misstatements. Contemporary Accounting Research, 28(1), 17–82. doi: 10.1111/j.1911-3846.2010.01041.x

Dikmen, B., & Kucukkocaoglu, G. (2009). The Detection of Earnings Manipulation: The Three-Phase Cutting Plane Algorithm Using Mathematical Programming. Journal of Forecasting, 29(5), 442-466. doi: 10.1002/for.1138

Directorate General Taxation and Customs Union (2017). Study and Reports on the VAT Gap in the EU-28 Member States: 2017 Final Report. Retrieved from taxation_customs/sites/taxation/files/study_and_reports_on_the_vat_gap_2017.pdf

Erdoğan, M., & Erdoğan, E. O. (2020). Financial Statement Manipulation: A Beneish Model Application. In S. Grima, E. Boztepe, & P. J. Baldacchino (Eds.), Contemporary Issues in Audit Management and Forensic Accounting (pp. 173–188). Bingley: Emerald Publishing Limited.

Fekete, S., Cuzdriorean D. D., Sucală L., & Matiş D. (2009). An attempt at measuring the Fiscal Influence Over Accounting of Romanian Listed Companies. SSRN e-Library.

Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th Eds.). Sage Publication Ltd.

Fischthal, S. (1998). Neural network/conceptual clustering fraud detection architecture (US Patent 5822741). U.S. Patent and Trademark Office.

Gupta, M., & Nagadevara, V., (2007). Audit Selection Strategy for Improving Tax Compliance: Application of Data Mining Techniques. In A. Agarwal, & V. Ramana (Ed.) Foundations of E-Government, Proceedings of the eleventh International Conference on e-Governance (pp. 28-30). Hyderabad, India.

Halilbegovic, S., Celebic, N., Cero, E., Buljubasic, E., & Mekic, A. (2020). Application of Beneish M-score model on small and medium enterprises in Federation of Bosnia and Herzegovina. Eastern Journal of European Studies, 11(1), 146-163.

Hashimzade, N., Myles, G., Page, F., & Rablen, M. (2015). The use of agent-based modelling to investigate tax compliance. Economics of Governance, 16(2), 143–164. doi: 10.1007/s10101-014-0151-8

Lee, M., & Colbert, J. L. (1997). Analytical procedures: management tools for monitoring controls. Management Decision, 35(9), 682-688.

Lenard, M. J., & Alam, P. (2010). An Historical Perspective on Fraud Detection: From Bankruptcy Models to Most Effective Indicators of Fraud in Recent Incident. Journal of Forensic and Investigative Accounting, 1(1), 1-27.

Liou, F. M. (2008). Fraudulent financial reporting detection and business failure prediction models: a comparison. Managerial Auditing Journal, 23(7), 650-662.

MacCarthy, J. (2017). Altman Z-score, Beneish M-score, Corporate Failure, Financial Statements, Fraud. International Journal of Finance and Accounting, 6(6), 159–166.

Mahama, M. (2015). Detecting Corporate Fraud and Financial Distress Using the Altman and Beneish Models. International Journal of Economics, Commerce and Management, 3(1), 1-18.

Mantone, P. S. (2013). Using Analytics to Detect Possible Fraud: Tools and Techniques. New Jersey: Johm Wiley & Sons, Inc.

Mehta, A., & Bhavani, G. (2017). Application of Forensic Tools to Detect Fraud: The Case of Toshiba. Journal of Forensic and Investigative Accounting, 9(1), 692-710.

Mekic, A., Halilbegovic, S., & Huric, A. (2017). Forensic Accounting As A Solution To Manipulative Accounting Of Sme’s In Bosnia And Herzegovina. Ecoforum, 6(2), 1-8.

Nyakarimi, S. N., Kariuki, S. N., & Kariuki, P. W. (2020). Financial Statements Manipulations Using Beneish Model and Probit Regression Model. A Case of Banking Sector in Kenya. European Online Journal of Natural and Social Sciences, 9(1), 253-264.

Omar, N., Koya, R. K., Sanusi, Z. M., & Shafie, N. A. (2014). Financial Statement Fraud: A Case Examination Using Beneish Model and Ratio Analysis. International Journal of Trade, Economics and Finance, 5(2), 184–186. doi: 10.7763/IJTEF.2014.V5.367

Paliu-Popa L., & Ecobici N. (2007). Accounting Implications of Taxation 2007. MPRA Paper No. 12186.

Ristea, M. (2003), Bază şi alternativ în contabilitatea întreprinderii. Bucharest: Economic Forum.

Romanian Justice Department (n.d.). Romanian Courts Portal. Retrieved from

Romanian Ministry of Finance (n.d.). Fiscal Information and Financial Statements. Retrieved June 21, 2017, from

Romanian National Fiscal Council (2013). Annual Report 2013. Retrieved from

Schneider, F., Buehn A., & Montenegro, C. (2010). Shadow economies all over the World—New estimates for 162 Countries from 1999 to 2007. World Bank Policy Research Working Paper WPS5356.

Spathis, C., Doumpos, M., & Zopounidis, C. (2002). Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques. European Accounting Review, 11(3), 509-535.

Tarjoa, & Herawati, N. (2015). Application of Beneish M-Score Models and Data Mining to Detect Financial Fraud. Procedia - Social and Behavioral Sciences, 211, 924 – 930.

Vladu, A. B., Amat, O., & Cuzdriorean, D. D. (2017). Truthfulness in Accounting: How to Discriminate Accounting Manipulators from Non-manipulators. Journal of Business Ethics, 140(4), 633–648. doi: 10.1007/s10551-016-3048-3

Volkov, M. (2020). Beneish Model as a Tool for Reporting Quality Estimation: Empirical Evidence. In T. Antipova (Ed.), Integrated Science in Digital Age 2020 (pp. 60–68). NY: Springer International Publishing.

Wagner, W. E. (2015). Using IBM SPSS Statistics For Research Methods And Social Science Statistics. California: SAGE Publication.

Wu, R.-S., Ou, C.-S., Lin, H.-Y., Chang, S.-I., & Yen, D.C. (2012). Using data mining technique to enhance tax evasion detection performance. Expert System with Application, 39, 8769–8777.

How to Cite
Timofte Cristina Coca, Socoliuc Marian, Grosu Veronica, & Coca Dan-Andrei. (2021). Fiscal And Accounting Fraud Risk Detection Using Beneish Model. A Romanian Case Study. International Journal of Business and Society, 22(1), 296-312.