Acta Univ. Agric. Silvic. Mendelianae Brun. 2018, 66(5), 1261-1266 | DOI: 10.11118/actaun201866051261

Fraud Risk Management from the Perspective of CFEBT Risk Triangle of Accounting Errors and Frauds

Zita Drábková
Department of Accounting and Finances, Faculty of Economics, University of South Bohemia in České Budějovice, Studentská 13, 370 05 České Budějovice, Czech Republic

The objective of the present contribution is to evaluate the risk of the impact of accounting errors and frauds on reported accounting records on the basis of the CFEBT risk triangle of accounting errors and frauds. The analysis is conducted in the framework of a case study that examines a selected accounting unit predominantly operating in trade, with respect to financial statements reported during the years 2011-2015. The evaluation of the risk of impacts of accounting errors and frauds forms a part of one of the three vertices of the CFEBT risk triangle. The contribution presents results of the CFEBT approach at three levels of the M-score and analyses significant discrepancies between the generation of earnings and increase in cash flow during the observed periods. The CFEBT risk triangle was designed as a tool for detection, evaluation and management of the risk of accounting errors and frauds in circumstances of the Czech accounting standards and International Financial Reporting Standards (IFRS). The essential aim of the triangle is to reduce information asymmetry between authors and users of accounting records, or, in other words, to increase the quality of available information with respect to decision-making on the basis of available accounting information.

Keywords: CFEBT risk triangle of accounting errors and frauds, fraud risk management, creative accounting
Grants and funding:

This paper was supported by the University of South Bohemia, Faculty of Economics [no. IGS05C1].

Published: October 29, 2018  Show citation

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Drábková, Z. (2018). Fraud Risk Management from the Perspective of CFEBT Risk Triangle of Accounting Errors and Frauds. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis66(5), 1261-1266. doi: 10.11118/actaun201866051261
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References

  1. BALACIU, D. E., BOGDAN, V., FELEAGA, L. et al. 2014. Accounting Outside the Box. An Introspective Study on the "Colorful" Mind of Managers Reflectec in Creative Accounting. Journal of Accounting and Management Information Systems, 13(4): 643-664.
  2. BLOOMFIELD, R. 1995. Strategic Dependence and Inherent Risk Assessments. The Accounting Review, 70(1): 71-90.
  3. CAPLAN, D. 1994. The Expectations Gap: Understanding Auditors' Efforts to Detect Fraud. Ph.D. Dissertation. USA: University of California at Berkeley.
  4. DAWSON, S. 2015. Internal Control/Anti-Fraud Program Design for The Small Business. UK: John Wiley and Sons Ltd. Go to original source...
  5. DRÁBKOVÁ, Z. 2013. The potential to reduce the risk of manipulation of financial statements using the identification models of creative accounting. Acta universitatis agriculturae et silviculturae Mendelianae Brunensis, 61(7): 2055-2063. DOI: 10.11118/actaun201361072055 Go to original source...
  6. 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(6): 1859-1866. DOI: 10.11118/actaun201563061859 Go to original source...
  7. DRÁBKOVÁ, Z. 2017. Kreativní účetnictví a účetní podvody - Řízení rizika účetních chyb a podvodů. Praha, Česko: Wolters Kluwer.
  8. GOODE, S. and LACEY, D. 2011. Detecting complex account fraud in the enterprise: The role of technical and non-technical controls. Decision Support Systems, 50(4): 702-714. DOI: 10.1016/j.dss.2010.08.018 Go to original source...
  9. HORVAT, T. and LIPICNIK, M. 2016. Internal Audits of Frauds in Accounting Statements of a Construction Company. Strategic Management, 21(4): 29-36.
  10. PAULA, E. L., LADEIRA, M., CARVALHO, R. N. et al. 2016. Deep Learning Anomaly Detection as Support Fraud Investigation in Brazilian Exports and Anti-Money Laundering. In: 15th Ieee International Conference on Machine Learning and Applications. IEEE, 18-20 December. California, USA, pp. 954-960. Go to original source...
  11. PURDA, L. and SKILLICORN, D. 2015. Accounting Variables, Deception, and a Bag of Words: Assessing the Tools of Fraud Detection. Contemporary Accounting Research, 32(3): 1193-1223. DOI: 10.1111/1911-3846.12089 Go to original source...
  12. WUERGES, A. F. E. and BORBA, J. A. 2014. Accounting Fraud: an estimation of detection probability. Rbgn-Revista Brasileira De Gestao De Negocios, 16(52): 466-483. Go to original source...

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