Application of selected data mining techniques in unintentional accounting error detection

• Authors: Mário Papík , Lenka Papíková

Abstracts

EN

Research background: Even though unintentional accounting errors leading to financial restatements look like less serious distortion of publicly available information, it has been shown that financial restatements impacts on financial markets are similar to intentional fraudulent activities. Unintentional accounting errors leading to financial restatements then affect value of company shares in the short run which negatively impacts all shareholders. Purpose of the article: The aim of this manuscript is to predict unintentional accounting errors leading to financial restatements based on information from financial statements of companies. The manuscript analysis if financial statements include sufficient information which would allow detection of unintentional accounting errors. Methods: Method of classification and regression trees (decision tree) and random forest have been used in this manuscript to fulfill the aim of this manuscript. Data sample has consisted of 400 items from financial statements of 80 selected international companies. The results of developed prediction models have been compared and explained based on their accuracy, sensitivity, specificity, precision and F1 score. Statistical relationship among variables has been tested by correlation analysis. Differences between the group of companies with and without unintentional accounting error have been tested by means of Kruskal-Wallis test. Differences among the models have been tested by Levene and T-tests. Findings & value added: The results of the analysis have provided evidence that it is possible to detect unintentional accounting errors with high levels of accuracy based on financial ratios (rather than the Beneish variables) and by application of random forest method (rather than classification and regression tree method).

Keywords

EN

financial fraud; unintentional accounting errors; financial restatements; decision tree; classification and regression tree; random forest

• Publisher: Instytut Badań Gospodarczych
• Journal: Equilibrium. Quarterly Journal of Economics and Economic Policy
• Year: 2021
• Volume: 16
• Issue: 1
• Pages: 185-201

Dates

published

2021

Contributors

author

Mário Papík

• Comenius University in Bratislava

• https://orcid.org/0000000325637799

author

Lenka Papíková

• Comenius University in Bratislava

• https://orcid.org/0000000165256769

References

• Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589?609.
• Association of Certified Fraud Examiners. (2018). Global study on occupational fraud and abuse. Retrieved from https://s3-us-west-2.amazonaws.com/acfepubl ic/2018-report-to-the-nations.pdf / (16.02.2020).
• Beneish, M. D, Lee, C., Press, E., Whaley, B., Zmijewski, M., & Cisilino, P. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24?36. doi: 10.2469/faj.v55.n5.2296.
• Bowen, R., Dutta, S., & Zhu, P. (2017). Financial constraints and accounting restatements. University of Ottawa Working Paper.
• Breiman, L., Friedman J., Olshen R., & Stone C. (1984). Classification and regression trees. Wadsworth Int. Group.
• Breiman, L. (2001). Random forests. Machine Learning, 45(5), 5?32. doi: 10.1023 /A:1010933404324.
• Chen, Y-J., Wu, Ch-H., Chen, Y-M., Li, H-Y., & Chen, H-K. (2017). Enhancement of fraud detection for narratives in annual reports. International Journal of Accounting Information Systems, 26. 32?45. doi: 10.1016/j.accinf.2017.06.004.
• Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R.G. (2011). Predicting material accounting misstatements: Predicting material accounting misstatements. Contemporary Accounting Research. 28, 17?82. doi: 10.1111/j.1911-3846.2010.01 041.x.
• Drábková, Z. (2015). Analysis of possibilities of detecting the manipulation of financial statements in terms of the IFRS and Czech Accounting Standards. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 63, 1859?1866. doi: 10.11118/actaun201563061859.
• Dutta, I., Dutta, S., & Raahemi, B. (2017). Detecting financial restatements using data mining techniques. Expert Systems with Applications 90, 374?393. doi: 10.1016/j.eswa.2017.08.030.
• EDGAR Online (2019). List of companies: 10-K.
• Gepp, A. (2015). Financial statement fraud detection using supervised learning methods. Retrieved from http://epublications.bond.edu.au/cgi/viewcontent.cgi? article=1227&context=theses (16.02.2020).
• Hajek, P., & Henriques, R. (2017). Mining corporate annual reports for intelligent detection of financial statement fraud ? a comparative study of machine learning methods. Knowledge-Based Systems, 128(5), 139?152. doi: 10.1016/j.knosys. 2017.05.001.
• Homola, D., & Paseková, M. (2020). Factors influencing true and fair view when preparing financial statements under IFRS: evidence from the Czech. Republic. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(3), 595?611. doi: 10.24136/eq.2020.026.
• Humpherys, S. L., Moffitt, K. C., Burns, M. B., Burgoon, J. K., & Felix, W. F. (2011). Identification of fraudulent financial statements using linguistic credibility analysis. Decision Support Systems, 50(3), 585?594. doi: 10.1016/j.dss.201 0.08.009.
• Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications. 32, 995?1003. doi: 10.1016/j.eswa.2006.02.016.
• Kotsiantis, S., Koumanakos, E., Tzelepis, D., & Tampakas, V. (2006). Forecasting fraudulent financial statements using data mining. International Journal of Computational Intelligence, 3(2), 104-110.
• Ibadin, P. O., & Ehigie, A. H. (2019). Beneish model, corporate governance and financial statements manipulation. Asian Journal of Accounting and Governance, 12. 51?64. doi: 10.17576/AJAG-2019-12-05.
• Jan, Ch.-I. (2018). An effective financial statements fraud detection model for the sustainable development of financial markets: evidence from Taiwan. Sustainability, 10(2). 513. doi: 10.3390/su10020513.
• Kim, Y. J., Baik, B., & Cho, S. (2016). Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning. Expert Systems with Applications, 62, 32?43. doi: 10.1016/j.eswa.2016.06.016.
• Li, O. Z., & Zhang, Y. (2006). Financial restatement announcements and insider trading. SSRN Electronic Journal. doi: 10.2139/ssrn.929539.
• Lin, C.-C., Chiu, A.-A., Huang, S. Y., & Yen, D. C. (2015). Detecting the financial statement fraud: the analysis of the differences between data mining techniques and experts? judgments. Knowledge-Based Systems, 89, 459?470. doi: 10.1016/ j.knosys.2015.08.011.
• Liu, Ch., Chan, Y., Kazmi, S. H. A., & Fu, H. (2015). Financial fraud detection model: based on random forest. International Journal of Economics and Finance, 7(7), 178?188. doi: 10.5539/ijef.v7n7p178.
• Mariak, V., & Mitková, Ľ. (2016). Long-term sustainability of portfolio investments ? gender perspective: an overview study. Oxford Journal of Finance and Risk Perspectives, 5(1), 219?226.
• MacCarthy, J. (2017). Using Altman Z-score and Beneish M-score models to detect financial fraud and corporate failure: a case study of Enron Corporation. International Journal of Finance and Accounting 6, 159?166. doi: 10.5923/j.ijfa. 20170606.01.
• Pai, P.-F., Hsu, M.-F., & Wang, M.-Ch. (2011). A support vector machine-based model for detecting top management fraud. Knowledge-Based Systems, 24(2). 314?321. doi: 10.1016/j.knosys.2010.10.003.
• Palmrose, Z. V., Richardson, V., & Scholz, S. (2004). Determinants of market reactions to restatement announcements. Journal of Accounting and Economics, 37(1). 59?89.
• Papík, M., & Papíková, L. 2020. Detection models for unintentional financial restatements. Journal of Business Economics and Management, 21(1), 64?86. doi: 10.3846/jbem.2019.10179.
• Paseková, M., Kramná, E., Svitáková, B., & Dolejšová, M. (2019). Relationship be-tween legislation and accounting errors from the point of view of business representatives in the Czech Republic. Oeconomia Copernicana, 10(1), 193?210. doi: 10.24136/oc.2019.010.
• Pavlovič, V., Kneževič, G., Joksimovič, M., & Joksimovič D. (2019). Fraud detection in financial statements applying Benford?s law with Monte Carlo simulation. Acta Oeconomica, 69(2), 217?239. doi: 10.1556/032.2019.69.2.4.
• Price Waterhouse Coopers (2014). Global Economic Crime Survey 2014. Retrieved from https://www.pwc.at/publikationen/global-economic-crime-survey-2014.pdf (3.01.2020).
• Price Waterhouse Coopers (2016). Global Economic Crime Survey 2016. Retrieved from https://www.pwc.com/gx/en/economic-crime-survey/pdf/GlobalE conomicCrimeSurvey2016.pdf (3.01.2020).
• Ravisankar, P., Ravi, V., Rao, G. R., & Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision Support Systems, 50, 491?500. doi: 10.1016/j.dss.2010.11.006.
• Rezaee, Z. (2005). Causes, consequences and deterrence of financial statement fraud. Critical Perspectives on Accounting, 16(3), 277?298. doi: 10.1016/S104 5-2354(03)00072-8.
• Saxunová, D. (2012). Investigation of suspected fraud. International Journal of Business and Management, 1(2), 347?364.
• Sosnowski, T. (2017). Earnings management and the floatation structure: empirical evidence from Polish IPOs. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(4), 693?709. doi: 10.24136/eq.v12i4.36.
• Sosnowski, T. (2018). Earnings management in the private equity divestment process on Warsaw Stock Exchange. Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(4), 689?705. doi: 10.24136/eq.2018.033.
• Yao, J., Pan, Y., Yang, S., Chen, Y., & Li, Y. (2019). Detecting fraudulent financial statements for the sustainable development of the socio-economy in China: A multi-analytic approach. Sustainability, 11(6), 1?17. doi: 10.3390/su110615 79.
• Quinlan, J. R. (1986). Introduction of decision trees. Machine Learning, 1, 81?106. doi: 10.1007/BF00116251.
• Tang, J., Alelyani, S., & Liu, H. (2014). Feature selection for classification: a review. In Data classification: algorithms and applications. CRC Press, 37?64. doi: 10.1201/b17320.
• Throckmorton, Ch. S., Mayew, W. J., Venkatachalam, M., & Collins, L. M. (2015). Financial fraud detection using vocal, linguistic and financial cues. Decision Support Systems, 74. 78?87. doi: 10.1016/j.dss.2015.04.006.
• Wang, R., Asghari V., Hsu, Sh.-H., Lee Ch.-J., & Chen, J.-H. (2020). Detecting corporate misconduct through random forest in China?s construction industry, Journal of Cleaner Production, 268, doi: 10.1016/j.jclepro.2020.122266.

Identifiers

DOI

10.24136/eq.2021.007

Biblioteka Nauki

22444352