International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020


IJSTR >> Volume 9 - Issue 1, January 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Financial Fraud Prediction Models: A Review Of Research Evidence

[Full Text]



V.K.Wadhwa, A.K.Saini, S.Sanjay Kumar



Empirical fraud prediction, Fraud Triangle/Diamond, M-Score, Z-Score, Machine Learning & Artificial Intelligence for fraud prediction.



Despite reports about significant advances in techniques for prediction of financial frauds research findings till now do not provide specific evidence or tools for predicting frauds that could be averted. Researchers have explored different methods with varied degree of success relying on financial data as well as non-financial factors for their purpose. This paper reviews reported models/evidence including adaptations/improvements in the models used during investigation. Fraud triangle theory specified by Cressey in 1953 is at the foundation applied in empirical predictive modelling postulated by a number of researchers. Prominent contributors are Beasley, M.S. (1996)., Dechow, et al. (1996).,Beneish M. D. (1997)., Nieschwietz et al. (2000), Skousen and Wright (2008). Convergence of fraud triangle theory to fraud diamond theory was suggested by Wolf and Hermanson in 2004. This paper additionally reviews specific computational models known as Z-Score (Altman,1968 ), M-Score (Beneish, 1999, 2012), and computer software based models from Green B.P. & Choi J.H.(1997) Zaki & Theodoulidis (2013) and Arta & Seyrek, (2009). There is a noticeable changing trend in research going towards numerous investigations now using computer supported machine learning and artificial intelligence tools for prediction of financial frauds. At the end an assessment is made about degree of success achieved in prediction of financial frauds till date. Empirical fraud prediction, Fraud Triangle/Diamond, M-Score, Z-Score, Machine Learning & Artificial Intelligence for fraud prediction.



[1] A. Sharma and P. Kumar Panigrahi, (2012) review of financial accounting fraud detection based on data mining techniques, International Journal of Computer Applications, vol. 39, no. 1, pp. 37–47.
[2] Abbott, L.J., S. Parker, and G.F. Peters. 2004. Audit Committee Characteristics and Restatements. Auditing: A Journal of Practice & Theory 23: 69-88.
[3] Aghghaleh, S.F., Z.M. Mohamed, and M.M. Rahmat. 2016. Detecting Financial Statement Frauds in Malaysia: Comparing the Abilities of Beneish and Dechow Models. Asian Journal of Accounting and Governance 7: 57-65.
[4] Albrecht, W. S. and M. B. Romney (1986) Red-flagging management fraud: A validation. Advances in Accounting 3: 323-334
[5] Altamuro, J., A. L. Beatty, and J. Weber. 2005. The Effects of Accelerated Revenue Recognition on Earnings Management and Earnings Informativeness: Evidence from SEC Staff Accounting Bulletin No. 101. The Accounting Review 80: 373-401.
[6] Altman, E.I. 1968. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance 23 (4): 589-609.
[7] American Institute of Certified Public Accountants (AICPA). 1997. Considerations of Fraud in a Financial Statement Audit Statement on Auditing Standards No. 82. New York, NY: AICPA.
[8] American Institute of Certified Public Accountants (AICPA). 2002. Considerations of Fraud in a Financial Statement Audit Statement on Auditing Standards No. 99. New York, NY: AICPA.
[9] Baucus, M.S. & Near, J.P. 1991. Can illegal corporate behaviour be predicted? An event history analysis. Academy of Management Journal. 34(1 ):9-36.
[10] Bayley, L., & S.L. Taylor. 2007. Identifying Earnings Overstatements: A Practical Test. Working paper.
[11] Beasley, M. S., Carcello, J. V., Hermanson, D. R., & Lapides, P. D. (2000). Fraudulent financial reporting: Consideration of industry traits and corporate governance mechanisms. Accounting Horizons, 14(4), 441-454.
[12] Beasley, M.S. 1996. An Empirical Analysis of the Relation Between the Board of Director Composition and Financial Statement Fraud. The Accounting Review 71: 443-465.
[13] Beasley, M.S. 1996. An empirical analysis of the relation between the board of director composition and financial statement fraud. The Accounting Review 71 (October): 443-464
[14] Beasley, M.S., J.V. Carcello, and D.R. Hermanson. 1999. Fraudulent Financial Reporting: 1987-1997. An Analysis of US Public Companies. Committee of Sponsoring Organizations of the Treadway Commission (COSO)
[15] Beasley, M.S., J.V. Carcello, D.R. Hermanson, and T.L. Neal. 2010. Fraudulent Financial Reporting: 1998-2007. An Analysis of US Public Companies. Committee of Sponsoring Organizations of the Treadway Commission (COSO)
[16] Bell, T.B., and J.V. Carcello. 2000. A Decision Aid for Assessing the Likelihood of Fraudulent Financial Reporting. Auditing: A Journal of Practice & Theory 19 (1): 169-184.
[17] Beneish, M.D. 1999. The Detection of Earnings Manipulation. Financial Analysts Journal 55 (5): 24-36.
[18] Bhasin M. L. (2012). Corporate Accounting Frauds: A Case Study of Satyam
[19] Bonner, S.E., Z-V. Palmrose, and S.M. Young. 1998. Fraud Type and Auditor Litigation: An Analysis of SEC Accounting and Auditing Enforcement Releases. The Accounting Review 73 (4): 503-532. 89
[20] Brazel, J.F., K.L. Jones, and M.F. Zimbelman. 2009. Using Nonfinancial Measures to Assess Fraud Risk. Journal of Accounting Research 47 (5): 1135-1166.
[21] Brazel, J.F., K.L. Jones, J. Thayer, and R.C. Warne. 2015. Understanding Investor Perceptions of Financial Statement Fraud and their Use of Red Flags: Evidence from the Field. Review of Accounting Studies 20: 1373-1406.
[22] Callen, J.L., S.W. Robb, and D. Segal. 2008. Revenue Manipulation and Restatements by Loss Firms. Auditing: A Journal of Practice & Theory 27: 1-29.
[23] Cohen, J.R., L.L. Holder-Webb, L. Nath, and D. Wood. 2012. Corporate Reporting of Nonfinancial Leading Indicators of Economic Performance and Sustainability. Accounting Horizons 26 (1): 65-90.
[24] Cressey, D. R. (1953). Other People’s Money. Montclair, NJ: Patterson Smith, pp.1-300.
[25] Dechow, P.M, R.G. Sloan, and A.P. Sweeney. 1996. Causes and consequences of earnings manipulation: An analysis of firms subject to enforcement actions by the SEC. Contemporary Accounting Research, Vol 13, no. 1, pp. 1-36.
[26] Dechow, P.M. and A. Sweeney. 1995. Detecting Earnings Management. The Accounting Review 70: 193-225.
[27] Dechow, P.M., W. Ge, C.R. Larson, and R.G. Sloan. 2011. Predicting Material Accounting Misstatements. Contemporary Accounting Research 28: 17-82.
[28] Elda du Toit (2208) Characteristics of companies with a higher risk of financial statement fraud: A survey of the literature. South African Journal of Accounting Research, Volume 22, 2008- issue 1,pages 19-44
[29] Farber, D. 2005. Restoring Trust After Fraud: Does Corporate Governance Matter? The Accounting Review 80: 539-561.
[30] Feroz, E.H., K. Park, and V.S. Pastena. 1991. The Financial and Market Effects of the SEC’s Accounting and Auditing Enforcement Releases. Journal of Accounting Research 29: 107-142.
[31] Francis, J., D. Philbrick, and K. Schipper. 1994. Shareholder Litigation and corporate Disclosures. Journal of Accounting Research 32 (Autumn): 137-164.
[32] Green B.P. and Choi J.H.,(1997). Assessing the risk of management fraud through neural network technology, Auditing: A journal of Practice and Theory, Vol. 16(1), 14-28
[33] Gul F.A., Lynn S.G., Tsui J.S.L. (2002), Audit quality management ownership and the informativeness of accounting earnings, Journal of Accounting, Auditing and Finance, Vol. 17, No.1, pp. 25-49.
[34] Gupta P.K.,Gupta Sanjeev, (2015),‘Corporate frauds in India – perceptions and emerging issues’, Journal of Financial Crime, Vol 22, Iss 1.pp. 79-133.
[35] Hogan, C.E., Z. Rezaee, R.A. Riley, Jr., and U.K. Velury. 2008. Financial Statement Fraud: Insights from the Academic Literature. Auditing: A Journal of Practice & Theory 27 (2): 231-252.
[36] ICSI (2007), The Institute of Company Secretaries of India, Guidance Note on Corporate Governance Certificate, ICSI, New Delhi.
[37] Jones, J. 1991. Earnings Management during Import Relief Investigations. Journal of Accounting Research 29: 193-228.
[38] Juszczak, P., Adams, N.M., Hand, D.J., Whitrow, C., & Weston, D.J. (2008). Off-the-peg and bespoke classifiers for fraud detection‖, Computational Statistics and Data Analysis, vol. 52 (9): 4521-4532
[39] Kaminski, K.A., and T.S. Wetzel. 2004. Financial Ratios and Fraud: An Exploratory Study using Chaos Theory. Journal of Forensic Accounting V: 147-172.
[40] Kaminski, K.A., T.S. Wetzel, and L. Guan. 2004. Can Financial Ratios Detect Fraudulent Financial Reporting? Managerial Auditing Journal 19 (1): 15-28.
[41] Marquardt, C.A., and C.I. Wiedman. 2004. How Are Earnings Managed? An Examination of Specific Accruals. Contemporary Accounting Research 21: 461-491.
[42] Nieschwietz, Robert J., Joseph J. Schultz, Jr. and Mark F. Zimbelman, Empirical Research on External Auditors’ Detection of Financial Statement Fraud. Journal of Accounting Literature Vol. 19, 2000: pp 190-246.
[43] OECD (1999) Principles of corporate governance 1999, OECD
[44] OECD (2004) Principles of corporate governance 2004, OECD
[45] Palmrose, Z-V. 1988. An Analysis of Auditor Litigation and Audit Service Quality. The Accounting Review 63 (January): 55-73.
[46] Public Company Accounting Oversight Board (PCAOB). 2010. Identifying and Assessing Risks of Material Misstatement Accounting Standard No. 2110. Washington, DC: PCAOB
[47] PWC’s 2018 Global Economic Crime and Fraud Survey.
[48] Rathinaraj D. (2010). Financial fraud, cyber scams and India – A small survey of popular recent cases, Anna University of Technology, Chennai.
[49] Rezaee, Z. 2005. Causes, Consequences, and Deterrence of Financial Statement Fraud. Critical Perspectives on Accounting 16: 277-298.
[50] Securities and Exchange Commission (SEC). 1999. Revenue Recognition. Staff Accounting Bulletin No. 101. Washington D.C.: Government Printing Office.
[51] Skousen, Christopher J., Charlotte J Wright,(2008) Contemporaneous Risk Factors and The prediction of Financial Statement Fraud, Journal of Forensic Accoutning IX:37-62
[52] Skousen, Christopher J., Kevin R. Sminth, and Charlotte J. Wright (2009). “Detecting and Predicting Financial Staement Fraud: The Effectiveness of the Fraud Triangle and SAS No. 99”. SSRN Working Paper Series Feb
[53] Trompeter, G., T. Carpenter, N. Desai, K. Jones, and R. Riley. 2013. A Synthesis of Fraud Related Research. Auditing: A Journal of Practice and Theory 32: 287-321.
[54] Verma Gakhar, D. (2013). Earnings management practices in India: A study of auditor’s perception. Journal of Financial Crime, 21(1), 100-110.
[55] Wells, J.T. 1990. Six common myths about fraud. Journal of Accountancy. 169(2):82-88.
[56] Zmijewski, M.E. 1984. Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research 22: 59-82