Fiscal And Accounting Fraud Risk Detection Using Beneish Model. A Romanian Case Study
Abstract
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.
References
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: https://doi.org/10.2469/faj.v55.n5.2296
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 http://steconomiceuoradea.ro/anale/volume/2008/v3-finances-banks-accountancy/190. 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: https://doi.org/10.1016/j.dss.2010.07.012
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 https://ec.europa.eu/ 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. https://patentimages.storage. googleapis.com/2b/14/3e/eeefb00edc9489/US5822741.pdf
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 http://portal.just.ro/SitePages/acasa.aspx
Romanian Ministry of Finance (n.d.). Fiscal Information and Financial Statements. Retrieved June 21, 2017, from http://www.mfinante.gov.ro/rol.html?pagina=acasa
Romanian National Fiscal Council (2013). Annual Report 2013. Retrieved from http://www.consiliulfiscal.ro/
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.
Copyright Transfer Statement for Journal
1) In signing this statement, the author(s) grant UNIMAS Publisher an exclusive license to publish their original research papers. The author(s) also grant UNIMAS Publisher permission to reproduce, recreate, translate, extract or summarize, and to distribute and display in any forms, formats, and media. The author(s) can reuse their papers in their future printed work without first requiring permission from UNIMAS Publisher, provided that the author(s) acknowledge and reference publication in the Journal.
2) For open access articles, the author(s) agree that their articles published under UNIMAS Publisher are distributed under the terms of the CC-BY-NC-SA (Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License) which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work of the author(s) is properly cited.
3) For subscription articles, the author(s) agree that UNIMAS Publisher holds copyright, or an exclusive license to publish. Readers or users may view, download, print, and copy the content, for academic purposes, subject to the following conditions of use: (a) any reuse of materials is subject to permission from UNIMAS Publisher; (b) archived materials may only be used for academic research; (c) archived materials may not be used for commercial purposes, which include but not limited to monetary compensation by means of sale, resale, license, transfer of copyright, loan, etc.; and (d) archived materials may not be re-published in any part, either in print or online.
4) The author(s) is/are responsible to ensure his or her or their submitted work is original and does not infringe any existing copyright, trademark, patent, statutory right, or propriety right of others. Corresponding author(s) has (have) obtained permission from all co-authors prior to submission to the journal. Upon submission of the manuscript, the author(s) agree that no similar work has been or will be submitted or published elsewhere in any language. If submitted manuscript includes materials from others, the authors have obtained the permission from the copyright owners.
5) In signing this statement, the author(s) declare(s) that the researches in which they have conducted are in compliance with the current laws of the respective country and UNIMAS Journal Publication Ethics Policy. Any experimentation or research involving human or the use of animal samples must obtain approval from Human or Animal Ethics Committee in their respective institutions. The author(s) agree and understand that UNIMAS Publisher is not responsible for any compensational claims or failure caused by the author(s) in fulfilling the above-mentioned requirements. The author(s) must accept the responsibility for releasing their materials upon request by Chief Editor or UNIMAS Publisher.
6) The author(s) should have participated sufficiently in the work and ensured the appropriateness of the content of the article. The author(s) should also agree that he or she has no commercial attachments (e.g. patent or license arrangement, equity interest, consultancies, etc.) that might pose any conflict of interest with the submitted manuscript. The author(s) also agree to make any relevant materials and data available upon request by the editor or UNIMAS Publisher.
