THE NEXUS BETWEEN FRAUDULENT FINANCIAL REPORTING AND COMPANY FAILURE – THE CASE OF BANKRUPT COMPANIES IN SERBIA

Dejan V. Spasić, Jovana Stojanović

DOI Number
https://doi.org/10.22190/TEME260131028S
First page
437
Last page
460

Abstract


Under conditions of financial crisis, companies may resort to manipulative practices in order to present their financial position and earning capacity more favourably than they actually are. Following the disclosure of financial statement manipulations, a significant number of companies enter bankruptcy, and even when they survive, recovery is often prolonged and challenging. A particular challenge in less developed economies is the limited transparency regarding the perpetrators of fraud, which hampers the development of reliable indicators that could serve as early warning signals (red flags). This paper examines the applicability of the Beneish M-score model with eight variables on a sample of 48 companies for which bankruptcy proceedings were initiated in the Republic of Serbia during the 2017–2024 period. The research objective is to identify potential red flags by isolating financial statement items that exhibited unusual movements in the years preceding the initiation of bankruptcy proceedings. The results indicate that 20.83% of the companies were classified as potential manipulators during the crisis period. The observed risk of fraudulent financial reporting is particularly associated with distortions in the relationship between receivables and sales, with an indicator pointing to problematic patterns related to intangible assets, as well as with faster growth of administrative expenses relative to the growth of sales revenue. The findings further confirm the need for enhanced analysis of accrual-based items that reflect mismatches in the recognition of revenues and expenses relative to the corresponding cash inflows and outflows. The results may be useful to investors, financial institutions, regulators and agencies involved in fraud prevention and bankruptcy proceedings, as well as to academic researchers in the further development of models tailored to the specific characteristics of national economies.


Keywords

forensic accounting, financial statements, fraud indicators, M-score, bankruptcy.

Full Text:

PDF

References


ACFE (2024). Occupational Fraud 2024: A Report to the Nations. Available at: https://legacy.acfe.com/report-to-the-nations/2024/

Amiram, D., Bozanic, Z., Cox, J. D., Dupont, Q., Karpoff, J. M., & Sloan, R. (2018). Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature. Review of Accounting Studies, 23(2), 732-783. https://doi.org/10.1007/s11142-017-9435-x

Arman, M., & Sharmin, S. (2019). Likelihood of a company’s manipulation of its financial statement: An empirical analysis using Beneish M-score model. In International Conference on Management and Information Systems September 2019 (pp. 117-125).

Bansal, M. (2024). Earnings management: a three-decade analysis and future prospects. Journal of Accounting Literature, 46(4), 630-670. https://doi.org/10.1108/JAL-10-2022-0107

Beasley, M. S., Hermanson, D. R., Carcello, J. V., & Neal, T. L. (2010). Fraudulent financial reporting: 1998-2007: аn analysis of U.S. public companies. Association Sections, Divisions, Boards, Teams. 453. https://egrove.olemiss.edu/aicpa_assoc/453

Bell, T. B., & Carcello, J. V. (2000). A decision aid for assessing the likelihood of fraudulent financial reporting. Auditing: A Journal of Practice & Theory, 19(1), 169-184. https://doi.org/10.2308/aud.2000.19.1.169

Bello, O. A., Ogundipe, A., Mohammed, D., Adebola, F., & Alonge, O. A. (2023). AI-driven approaches for real-time fraud detection in US financial transactions: Challenges and opportunities. European Journal of Computer Science and Information Technology, 11(6), 84-102. Available at: https://eajournals.org/ejcsit/wp-content/uploads/sites/21/2024/06/AI-Driven-Approaches.pdf

Beneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24-36. https://doi.org/10.2469/faj.v55.n5.2296

Beneish, M. D. (2001). Earnings management: A perspective. Managerial Finance, 27(12), 3-17. https://doi.org/10.1108/03074350110767411

Beneish, M. D., Lee, C. M., & Nichols, D. C. (2013). Earnings manipulation and expected returns. Financial Analysts Journal, 69(2), 57-82. https://doi.org/10.2469/faj.v69.n2.1

Beneish, M. D., & Press, E. (1993). Costs of technical violation of accounting-based debt covenants. Accounting Review, 68(2), 233-257.

Boateng, D. A. (2011). A theoretical construct for explaining the impact of financial distress on unethical earnings management decisions. The Journal of American Academy of Business, 16(2), 89-95. Avalilable at: http://www.jaabc.com/Jaabc16-2March2011Boateng2.html

Burgstahler, D., & Dichev, I. (1997). Earnings management to avoid earnings decreases and losses. Journal of Accounting and Economics, 24(1), 99-126. https://doi.org/10.1016/S0165-4101(97)00017-7

Campa, D., & Camacho-Miñano, M. del M. (2014). Earnings management among bankrupt non-listed firms: evidence from Spain. Spanish Journal of Finance and Accounting / Revista Española de Financiación y Contabilidad, 43(1), 3-20. https://doi.org/10.1080/02102412.2014.890820

Chan, C., & Landry, S. P. (2019). Financial Statements Too Good to be True? An Instructional Case Assessing that Question Using Analytical Procedures and Beneish’s M-Score. Journal of Forensic and Investigative Accounting, 11(2), 380-394. Available at: https://www.nacva.com/content.asp?contentid=711#10

Chen, S. (2016). Detection of fraudulent financial statements using the hybrid data mining approach. SpringerPlus, 5, 89. https://doi.org/10.1186/s40064-016-1707-6

Christensen, T. E., Huffman, A., Lewis‐Western, M. F., & Scott, R. (2022). Accruals earnings management proxies: Prudent business decisions or earnings manipulation?. Journal of Business Finance & Accounting, 49(3-4), 536-587. https://doi.org/10.1111/jbfa.12585

Compagnino, A. A., Maruccia, Y., Cavuoti, S., Riccio, G., Tutone, A., Crupi, R., & Pagliaro, A. (2025). An Introduction to Machine Learning Methods for Fraud Detection. Applied Sciences, 15(21), 11787. https://doi.org/10.3390/app152111787

Corsi, C., Di Berardino, D., & Di Cimbrini, T. (2015). Beneish M-score and detection of earnings managements in Italian SMEs. Ratio Mathematica, 28, 65-83. https://doi.org/10.23755/rm.v28i1.28

Craja, P., Kim, A., & Lessmann, S. (2020). Deep learning for detecting financial statement fraud. Decision Support Systems, 139, 113421. https://doi.org/10.1016/j.dss.2020.113421

Criminal Procedure Code (2011). Official Gazette of the Republic of Serbia, Nos. 72/11 of 28 September 2011, 101/11 of 30 December 2011, 121/12 of 24 December 2012, 32/13 of 8 April 2013, 45/13 of 22 May 2013, 55/14 of 23 May 2014, 35/19 of 21 May 2019 and 27/21 of 24 March 2021 (CC). Available at: https://propisi.pravno-informacioni-sistem.rs/article?productId=1657&articleId=54814

Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting material accounting misstatements. Contemporary accounting research, 28(1), 17-82. https://doi.org/10.1111/j.1911-3846.2010.01041.x

Dichev, I., Graham, J., Harvey, C., & Rajgopal, S. (2013). Earnings quality: Evidence from the field. Journal of Accounting and Economics, 56(2-3), Supplement 1, 1–33. https://doi.org/10.1016/j.jacceco.2013.05.004

Dimitrijević, D. S., Obradović, V. M., & Milutinović, S. (2018). Indicators of fraud in financial reporting in the Republic of Serbia. Teme, 42(4), 1319-1338. https://doi.org/10.22190/TEME1804319D

Dimitrijević, D., Milutinović, S., & Stanković, P. (2024). Determinants of the occurrence of financial distress in medium-sized and big public jointstock companies. Economic Horizons/Ekonomski Horizonti, 26(3), 301-319. https://doi.org/10.5937/ekonhor2403301D

Dimitrijević, D., Stanković, P., & Vržina, S. (2024). Warnings of financial fraud in travel agencies in the Republic of Serbia during the COVID-19 pandemic. Menadžment u hotelijerstvu i turizmu, 12(2), 75-88. https://doi.org/10.5937/menhottur2400003D

Dyck, A., Morse, A., & Zingales, L. (2024). How pervasive is corporate fraud?. Review of Accounting Studies, 29(1), 736-769. https://doi.org/10.1007/s11142-022-09738-5

Frank, J. J., Jansen, D., & Carey, M. (2011). Financial Statement Fraud: Revenue and Receivables. Chapter 22, In: Golden, T. W., Skalak, S. L., Clayton, M. M., & Pill, J. S. (Eds). A Guide to Forensic Accounting Investigation, 2nd Edition (pp. 433-466). John Wiley & Sons, Inc., Hoboken, New Jersey.

Fülbier, R. U., & Sellhorn, T. (2023). Understanding and improving the language of business: how accounting and corporate reporting research can better serve business and society. Journal of Business Economics, 93, 1089-1124. https://doi.org/10.1007/s11573-023-01158-4

Healy, P. M., & Wahlen, J. M. (1999). A review of the earnings management literature and its implications for standard setting. Accounting Horizons, 13(4), 365-383. https://doi.org/10.2308/acch.1999.13.4.365

Henry, E., Elizabeth, A. G., Brad, R., & Timothy, L. (2012). The Role of Related Party Transactions in Fraudulent Financial Reporting. Journal of Forensic & Investigative Accounting, 4(1), 186-213. Available at: https://www.nacva.com/content.asp?admin=Y&contentid=469#7

Jackson, A. B. (2018). Discretionary accruals: earnings management. or not?. Abacus, 54(2), 136-153. https://doi.org/10.1111/abac.12117

Janger, E. J. (2022). Aggregation and Abuse: Mass Torts in Bankruptcy. Fordham Law Review, 91(2), 361-383. Available at: https://ir.lawnet.fordham.edu/flr/vol91/iss2/3

Kanapickienė, R., & Grundienė, Ž. (2015). The model of fraud detection in financial statements by means of financial ratios. Procedia-Social and Behavioral Sciences, 213, 321-327. https://doi.org/10.1016/j.sbspro.2015.11.545

Knežević, S., Špiler, M., Milašinović, M., Mitrović, A., Milojević, S., & Travica, J. (2021). Using Beneish M-Score and Altman Z-Score models to detect financial fraud and company failure. Tekstilna industrija, 69(4), 20-29. https://doi.org/10.5937/tekstind2104020K

Leach, R. (2007). Do firms manage their earnings prior to filing for bankruptcy?. Academy of Accounting and Financial Studies Journal, 11(3), 125-137. Available at: https://www.abacademies.org/articles/aafsjvol1132007.pdf

Lev, B., & Thiagarajan, S. R. (1993). Fundamental information analysis. Journal of Accounting Research, 31(2), 190-215. https://doi.org/10.2307/2491270

Lo, A. W., Wong, R. M., & Firth, M. (2010). Can corporate governance deter management from manipulating earnings? Evidence from related-party sales transactions in China. Journal of Corporate Finance, 16(2), 225-235. https://doi.org/10.1016/j.jcorpfin.2009.11.002

Malinić, D., & Ribić, M. (2025). Identifying indicators of fraudulent financial reporting under conditions of institutional opacity: the relevance of the Beneish model. In Malinić, D., & Vučković Milutinović, S. (Eds.), Conference Proceedings of the Second Conference on Forensic Accounting “Contemporary Forensic Accounting Trends in Combating Financial Fraud: Global and National Perspectives”, Belgrade, 3-4 October (pp. 1-22). Belgrade: Faculty of Economics and Business & Centre for Forensic Accounting.

Marchini, P. L., Mazza, T., & Medioli, A. (2018). The impact of related party transactions on earnings management: Some insights from the Italian context. Journal of Management and Governance, 22(4), 981-1014. https://doi.org/10.1007/s10997-018-9415-y

Mavengere, K. (2015). Predicting Corporate Bankruptcy and Earnings Manipulation Using the Altman Z-Score and Beneish M Score. The Case of Z Manufacturing Firm in Zimbabwe. International Journal of Management Sciences and Business Research, 4(10), 8-14. https://ssrn.com/abstract=2739676

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.

Özari, Ç., Can, E. N., & Demirkale, Ö. (2025). Financial Fraud Detection with Altman Z-Score and Beneish M-Score via Random Forest: Verified by Borsa Istanbul Fines (2018–2022). Sage Open, 15(4). https://doi.org/10.1177/21582440251386174

Perols, J. L., & Lougee, B. A. (2010). The relation between earnings management and financial statement fraud. Advances in Accounting, 27(1), 39-53. https://doi.org/10.1016/j.adiac.2010.10.004

Ramírez-Orrelliana, A., Martínez-Romero, M. J., & Marino-Garrido, T. (2017). Measuring fraud and earnings management by a case of study: Evidence from an international family business. European Journal of Family Business, 7(1-2), 41-53. https://doi.org/10.1016/j.ejfb.2017.10.001

Repousis, S. (2016). Using Beneish model to detect corporate financial statement fraud in Greece. Journal of Financial Crime, 23(4), 1063–1073. https://doi.org/10.1108/JFC-11-2014-0055

Rosner, R. L. (2003). Earnings Manipulation in Failing Firms. Contemporary Accounting Research, 20(2), 361–408. https://doi.org/10.1506/8EVN-9KRB-3AE4-EE81

Sabău, A.-I., Mare, C., & Safta, I. L. (2021). A Statistical Model of Fraud Risk in Financial Statements. Case for Romania Companies. Risks, 9(6), 116. https://doi.org/10.3390/risks9060116

Schilit, H. M., Perler, J., & Engelhart, Y. (2018). Financial Shenanigans. How to Detect Accounting Gimmicks and Fraud in Financial Reports. New York: McGraw-Hill.

Škarić-Jovanović, K., & Spasić, D. (2022). Specijalni bilansi [Special Purpose Financial Statements]. Beograd: CID Ekonomskog fakulteta Beograd.

Steingen, L., & Löw, E. (2025). Using Machine Learning to Detect Financial Statement Fraud: A Cross-Country Analysis Applied to Wirecard AG. Journal of Risk and Financial Management, 18(11), 605. https://doi.org/10.3390/jrfm18110605

Stolowy, H., & Breton, G. (2004). Accounts Manipulation: A Literature Review and Proposed Conceptual Framework. Review of Accounting and Finance, 3(1), 5-92. https://doi.org/10.1108/eb043395

Stubben, S. (2010). Discretionary revenues as a measure of earnings management. The Accounting Review, 85(2), 695-717. https://doi.org/10.2308/accr.2010.85.2.695

Svoboda, J., Berková, I., Pražáková, J., & Vejsadová Dryjová, M. (2025). Relationships Between the Beneish M-Score and Bankruptcy Models: Insights from Financial Models in Czech Manufacturing Firms. Zbornik radova Ekonomskog fakulteta u Rijeci: časopis za ekonomsku teoriju i praksu/ Proceedings of Rijeka Faculty of Economics: Journal of Economics and Business, 43(2), 465-484. https://doi.org/10.18045/zbefri.2025.2.7

Sylwestrzak, M. (2022). Application of the Beneish Model on the Warsaw Stock Exchange. Journal of Banking and Financial Economics, 18(2), 5-16. https://doi.org/10.7172/2353-6845.jbfe.2022.2.1

Tarjo, & Herawati, N. (2015). Application of Beneish M-score models and data mining to detect financial fraud. Procedia-Social and Behavioral Sciences, 211, 924-930. https://doi.org/10.1016/j.sbspro.2015.11.122

Teixeira, J. F., & Rodrigues, L. L. (2022). Earnings management: a bibliometric analysis. International Journal of Accounting & Information Management, 30(5), 664-683. https://doi.org/10.1108/IJAIM-12-2021-0259

Velte, P. (2023). The link between corporate governance and corporate financial misconduct. A review of archival studies and implications for future research. Management Review Quarterly, 73(1), 353-411. https://doi.org/10.1007/s11301-021-00244-7

Wang, M., Zhang, X., & Han, X. (2025). AI Driven Systems for Improving Accounting Accuracy Fraud Detection and Financial Transparency. Frontiers in Artificial Intelligence Research, 2(3), 403-421. https://doi.org/10.71465/fair398

Yiannoulis, Y., Vortelinos, D., & Passas, I. (2025). Exploring Audit Opinions: A Deep Dive into Ratios and Fraud Variables in the Athens Exchange. Accounting and Auditing, 1(1), 3. https://doi.org/10.3390/accountaudit1010003

Yue, D., Wu, X., Shen, N., & Chu, C. H. (2009). Logistic regression for detecting fraudulent financial statement of listed companies in China. In 2009 International Conference on Artificial Intelligence and Computational Intelligence (Vol. 2, pp. 104-108). IEEE. https://doi.org/10.1109/AICI.2009.421




DOI: https://doi.org/10.22190/TEME260131028S

Refbacks

  • There are currently no refbacks.


© University of Niš, Serbia
Creative Commons licence CC BY-NC-ND
Print ISSN: 0353-7919
Online ISSN: 1820-7804