Abstract
The objective of this study is to identify the financial statement fraud factors and rank the relative importance. First, this study reviews the previous studies to identify the possible fraud indicators. Expert questionnaires are distributed next. After questionnaires are collected, Lawshe’s approach is employed to eliminate these factors whose CVR (content validity ratio) values do not meet the criteria. Further, the remaining 32 factors are reviewed by experts to be the measurements suitable for the assessment of fraud detection. The Analytic Hierarchy Process (AHP) is utilized to determine the relative weights of the individual items. The result of AHP shows that the most important dimension is Pressure/Incentive and the least one is Attitude/rationalization. In addition, the top five important measurements are “Poor performance”, “The need for external financing”, “Financial distress”, “Insufficient board oversight”, and “Competition or market saturation”. The result provides a significant advantage to auditors and managers in enhancing the efficiency of fraud detection and critical evaluation.


Similar content being viewed by others
References
Agrawal, C., Knoeber, R., & Tsoulouhas, T. (2006). Are outsiders handicapped in CEO successions? Journal of Corporate Finance, 12(3), 619–644.
Anderson, R., & Reeb, D. (2003). Founding-family ownership and firm performance: evidence from the S&P 500. Journal of Finance, 58(3), 1301–1327.
Apostolou, B. A., Hassell, J. M., Webber, S. A., & Sumners, G. E. (2001). The relative importance of management fraud risk factors. Behavioral Research in Accounting, 13(1), 1–24.
Beasley, M. S. (1996). An empirical analysis of the relation between the board of director composition and financial statement fraud. Accounting Review, 71(4), 443–465.
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111.
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.
Bell, T. B., Szykowny, S. & Willingham, J. J. (1991). Assessing the likelihood of fraudulent financial reporting: a cascaded logit approach. Working paper, KPMG Peat Marwick.
Benítez, J., Delgado-Galván, X., Izquierdo, J., & Pérez-García, R. (2012). An approach to AHP decision in a dynamic context. Decision Support Systems, 53(3), 499–506.
Burns, N., & Kedia, B. (2006). The impact of performance-based compensation on misreporting. Journal of Financial Economics, 79(1), 35–67.
Carcello, J., & Neal, T. (2000). Audit committee composition and auditor reporting. The Accounting Review, 75(4), 453–467.
Chen, K. Y., & Elder, R. J. (2007). Fraud risk factors and the likelihood of fraudulent financial reporting: Evidence from statement on Auditing Standards No. 43 in Taiwan. In Working Paper. National Taiwan University and Syracuse University, 36.
Chen, H. J., Huang, S. Y., & Kuo, C. L. (2009). Using the artificial neural network to predict fraud litigation: some empirical evidence from emerging markets. Expert Systems with Applications, 36(2), 1478–1484.
Chivers, H., Clark, J. A., Nobles, P., Shaikh, S. A., & Chen, H. (2013). Knowing who to watch: identifying attackers whose actions are hidden within false alarms and background noise. Information Systems Frontiers, 15(1), 17–34.
Cleary, R., & Thibodeau, J. C. (2005). Applying digital analysis using Benford's law to detect fraud: the dangers of type I errors. Auditing: A Journal of Practice & Theory, 24(1), 77–81.
Cressey, D. R. (1973). Other people’s money (p. 30). Patterson Smith: Montclair.
DeAngelo, H., & DeAngel, L. (1990). Dividend policy and financial distress: an empirical investigation of troubled NYSE firms. Journal of Finance, 45(5), 1415–1431.
Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1996). Causes and consequences of earnings manipulation: an analysis of firms subject to enforcement actions by the SEC. Contemporary Accounting Research, 13(1), 1–36.
Durtschi, C., Hillison, W., & Pacini, C. (2004). The effective use of Benford’s law to assist in detecting fraud in accounting data. Journal of Forensic Accounting, 5(1), 17–34.
Fama, E. F., & Jensen, M. C. (1983). Separation of ownership and control. Journal of Law and Economics, 26(2), 301–325.
Fanning, K. M., & Cogger, K. O. (1994). A comparative analysis of artificial neural networks using financial distress prediction. Intelligent Systems in Accounting, Finance and Management, 3(4), 241–252.
Farahmand, F., & Spafford, E. H. (2013). Understanding insiders: an analysis of risk-taking behavior. Information Systems Frontiers, 15(1), 5–15.
Francis, J., & Wilson, E. (1988). Auditor changes: a joint test of theories relating to agency costs and auditor differentiation. The Accounting Review, 63(4), 663–682.
Gillett, P. R., & Uddin, N. (2005). CFO intentions of fraudulent financial reporting. Auditing: A Journal of Practice & Theory, 24(1), 55–76.
Glass, L., & Co. (GLC). (2005). Control Deficiencies—Finding Financial Impurities Analysis of the 2004 and Early 2005 of Deficiency Disclosures. Control Deficiencies Trend Alert (June 24). Available at: http://www.glasslewis.com.
Goldman, E., & Slezak, S. (2006). An equilibrium model of incentive contracts in the presence of information manipulation. Journal of Financial Economics, 80(3), 603–626.
Green, B., & Choi, J. (1997). Assessing the risk of management fraud through neural network technology. Auditing: A Journal of Practice & Theory, 16(1), 14–28.
Hadani, M. (2012). Institutional ownership monitoring and corporate political activity: governance implications. Journal of Business Research, 65(7), 944–950.
Hammersley, J. S., Myers, L. A., & Zhou, J. (2012). The failure to remediate previously-disclosed material weaknesses in internal controls. Auditing: A Journal of Practice and Theory, 31(2), 73–111.
Hernandez, J. R., & Groot, T. (2007). Corporate fraud: Preventive controls which lower fraud risk. ARCA, Amsterdam Research Center in Accounting.
Hogan, C. E., Rezaee, Z., Riley, R. A., & Velury, U. K. (2008). Financial statement fraud: insights from the academic literature. Auditing: A Journal of Practice & Theory, 27(2), 231–252.
Huang, S. M., Yen, D. C., Yang, L. W., & Hua, J. S. (2008). An investigation of Zipf's Law for fraud detection. Decision Support Systems, 46(1), 70–83.
Huang, S. Y., Tsaih, R. H., & Lin, W. Y. (2012). Unsupervised neural networks approach for understanding fraudulent financial reporting. Industrial Management & Data Systems, 112(2), 224–244.
Kaplan, S. E. (2001). Ethical related judgments by observers of earnings management. Journal of Business Ethics, 32(4), 285–298.
Kerr, D. S., & Murthy, U. S. (2013). The importance of the CobiT framework IT processes for effective internal control over financial reporting in organizations: an international survey. Information & Management, 50(7), 590–597.
Kinney, W. (2005). The auditor as gatekeeper: A perilous expectations gap. In J. Lorsch, L. Berlowitz, & S. Zelleke (Eds.), Published in restoring trust in American business (pp. 99–107). Cambridge, Massachusetts: The MIT Press.
Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), 995–1003.
Knechel, W. R., Naiker, V., & Pacheco, G. (2007). Does auditor industry specialization matter? Evidence from market reaction to auditor switches. AUDITING: A Journal of Practice & Theory, 26(1), 19–45.
Kothari, S. P., Leone, A., & Wasley, C. (2005). Performance matched discretionary accruals. Journal of Accounting and Economics, 39(1), 163–197.
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575.
Lewis, B. R., Snyder, C. A., & Rainer, K. R. (1995). An empirical assessment of the information resource management construct. Journal of Management Information Systems, 12(1), 199–223.
Loebbecke, J. K., Eining, M. M., & Willingham, J. J. (1989). Auditors’ experience with material irregularities: frequency, nature, and detect-ability. Auditing: A Journal of Practice & Theory, 9(1), 1–28.
Lou, Y. I., & Wang, M. L. (2009). Fraud risk factor of the fraud triangle assessing the likelihood of fraudulent financial reporting. Journal of Business & Economics Research, 7(2), 61–78.
Lu, C. L., & Chen, T. C. (2009). A study of applying data mining approach to the information disclosure for Taiwan’s stock market investors. Expert Systems with Applications, 36(2), 3536–3542.
Millar, J. A., & Yeager, F. C. (2007). The recent regulatory response to corporate economic crime in the United States: observations and comments. Economic Affairs, 27(1), 39–43.
Ngai, E. W. T. (2003). Selection of web sites for online advertising using the AHP. Information and Management, 40(4), 233–242.
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in the fraud detection: a classification framework and an academic review of literature. Decision Support Systems, 50(3), 559–569.
Owusu-Ansah, S., Moyes, G. D., Oyelere, P. B., & Hay, D. (2002). An empirical analysis of the likelihood of detecting fraud in New Zealand. Managerial Auditing Journal, 17(4), 192–204.
Persons, O. (1995). Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research, 11(3), 38–46.
PricewaterhouseCoopers (PwC) (2007). Global Economic Crime Survey. Available at: http://www.pwc.com.
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(2), 491–500.
Reddy, K., Venter, H. S., & Olivier, M. S. (2012). Using time-driven activity-based costing to manage digital forensic readiness in large organizations. Information Systems Frontiers, 14(5), 1061–1077.
Rezaee, Z. (2005). Causes, consequences, and deterrence of financial statement fraud. Critical Perspectives in Accounting, 16(3), 277–298.
Saaty, T. L. (1980). Multicriteria decision making: The analytic hierarchy process. New York: McGraw-Hill.
Spathis, C. (2002). Detecting false financial statements using published data: some evidence from Greece. Managerial Auditing Journal, 17(4), 179–191.
Spathis, C., Doumpos, M., & Zopounidis, C. (2003). Using client performance measures to identify pre-engagement factors associated with qualified audit reports in Greece. The International Journal of Accounting, 38(3), 267–284.
Srivastava, R. P., Mock, T. J., & Turner, J. L. (2009). Bayesian fraud risk formula for financial statement audits. Abacus, 45(1), 66–87.
Stice, J. D. (1991). Using financial and market information to identify pre-engagement factors associated with lawsuits against auditors. The Accounting Review, 66(3), 516–533.
Stratopoulos, T. C., Vance, T. W., & Zou, X. (2013). Incentive effects of enterprise systems on the magnitude and detectability of reporting manipulations. International Journal of Accounting Information Systems, 14(1), 39–57.
Suyanto, S. (2009). Fraudulent financial statement: evidence from statement on auditing standard no. 99. Gadjah Mada International Journal of Business, 11(1), 117–144.
Wilks, T. J., & Zimbelman, M. F. (2004). Decomposition of fraud-risk assessments and auditors’ sensitivity to fraud cues. Contemporary Accounting Research, 21(3), 719–745.
Young, M. R. (2000). Accounting irregularities and financial fraud. San Diego: Harcourt Inc.
Yu, H. C., Wang, W. Y., & Chang, C. (2009). The pricing of intellectual capital in the IT industry. Working paper.
Zahra, S., Priem, R., & Rasheed, A. (2007). Understanding the causes and effects of top management fraud. Organizational Dynamics, 36(2), 122–139.
Zandstra, G. (2002). Enron, board governance and moral failings. Corporate Governance, 2(2), 16–19.
Zhou, W., & Kapoor, G. (2011). Detecting evolutionary financial statement fraud. Decision Support Systems, 50(3), 570–575.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Huang, S.Y., Lin, CC., Chiu, AA. et al. Fraud detection using fraud triangle risk factors. Inf Syst Front 19, 1343–1356 (2017). https://doi.org/10.1007/s10796-016-9647-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10796-016-9647-9