Application of selected data mining techniques in unintentional accounting error detection

Authors

DOI:

https://doi.org/10.24136/eq.2021.007

Keywords:

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

Abstract

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).

Downloads

Download data is not yet available.

References

Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589?609.

DOI: https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
View in Google Scholar

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).
View in Google Scholar

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.

DOI: https://doi.org/10.2469/faj.v55.n5.2296
View in Google Scholar

Bowen, R., Dutta, S., & Zhu, P. (2017). Financial constraints and accounting restatements. University of Ottawa Working Paper.
View in Google Scholar

Breiman, L., Friedman J., Olshen R., & Stone C. (1984). Classification and regression trees. Wadsworth Int. Group.
View in Google Scholar

Breiman, L. (2001). Random forests. Machine Learning, 45(5), 5?32. doi: 10.1023 /A:1010933404324.

DOI: https://doi.org/10.1023/A:1010933404324
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.accinf.2017.06.004
View in Google Scholar

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.

DOI: https://doi.org/10.1111/j.1911-3846.2010.01041.x
View in Google Scholar

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.

DOI: https://doi.org/10.11118/actaun201563061859
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.eswa.2017.08.030
View in Google Scholar

EDGAR Online (2019). List of companies: 10-K.
View in Google Scholar

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).
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.knosys.2017.05.001
View in Google Scholar

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.

DOI: https://doi.org/10.24136/eq.2020.026
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.dss.2010.08.009
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.eswa.2006.02.016
View in Google Scholar

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.
View in Google Scholar

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.

DOI: https://doi.org/10.17576/AJAG-2019-12-05
View in Google Scholar

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.

DOI: https://doi.org/10.3390/su10020513
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.eswa.2016.06.016
View in Google Scholar

Li, O. Z., & Zhang, Y. (2006). Financial restatement announcements and insider trading. SSRN Electronic Journal. doi: 10.2139/ssrn.929539.

DOI: https://doi.org/10.2139/ssrn.929539
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.knosys.2015.08.011
View in Google Scholar

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.

DOI: https://doi.org/10.5539/ijef.v7n7p178
View in Google Scholar

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.
View in Google Scholar

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.
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.knosys.2010.10.003
View in Google Scholar

Palmrose, Z. V., Richardson, V., & Scholz, S. (2004). Determinants of market reactions to restatement announcements. Journal of Accounting and Economics, 37(1). 59?89.

DOI: https://doi.org/10.1016/j.jacceco.2003.06.003
View in Google Scholar

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.

DOI: https://doi.org/10.3846/jbem.2019.10179
View in Google Scholar

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.

DOI: https://doi.org/10.24136/oc.2019.010
View in Google Scholar

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.

DOI: https://doi.org/10.1556/032.2019.69.2.4
View in Google Scholar

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).
View in Google Scholar

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).
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.dss.2010.11.006
View in Google Scholar

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.

DOI: https://doi.org/10.1016/S1045-2354(03)00072-8
View in Google Scholar

Saxunová, D. (2012). Investigation of suspected fraud. International Journal of Business and Management, 1(2), 347?364.
View in Google Scholar

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.

DOI: https://doi.org/10.24136/eq.v12i4.36
View in Google Scholar

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.

DOI: https://doi.org/10.24136/eq.2018.033
View in Google Scholar

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.

DOI: https://doi.org/10.3390/su11061579
View in Google Scholar

Quinlan, J. R. (1986). Introduction of decision trees. Machine Learning, 1, 81?106. doi: 10.1007/BF00116251.

DOI: https://doi.org/10.1007/BF00116251
View in Google Scholar

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.

DOI: https://doi.org/10.1201/b17320
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.dss.2015.04.006
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.jclepro.2020.122266
View in Google Scholar

Downloads

Published

2021-03-31

How to Cite

Papík, M., & Papíková, L. (2021). Application of selected data mining techniques in unintentional accounting error detection . Equilibrium. Quarterly Journal of Economics and Economic Policy, 16(1), 185–201. https://doi.org/10.24136/eq.2021.007

Issue

Section

Articles

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.