Presenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm
الموضوعات :Mahmood Mohammadi 1 , Shohreh Yazdani 2 , Mohammadhamed Khanmohammadi 3
1 - Department of Accounting, Damavand Branch, Islamic Azad University, Damavand, Iran.
2 - Department of Accounting, Damavand Branch, Islamic Azad University, Damavand, Iran.
3 - Department of Accounting, Damavand Branch, Islamic Azad University, Damavand, Iran.
الکلمات المفتاحية: fraud detection, Data mining, financial reporting fraud, Genetic Algorithm,
ملخص المقالة :
both academic and auditing firms have been searching for ways to detect corporate fraud. The main objective of this study was to present a model to detect financial reporting fraud by companies listed on Tehran Stock Exchange (TSE) using genetic algorithm. For this purpose, consistent with theoretical foundations, 21 variables were selected to predict fraud in financial reporting that finally, using statistical tests, 9 variables including SALE/EMP, RECT/SALE, LT/CEQ, INVT/SALE, SALE/TA, NI/CEQ, NI/SALE, LT/XINT, and AT/LT were selected as the potential financial reporting fraud indexes. Then, using genetic algorithm, the final model of fraud detection in financial reporting was presented. The statistical population of this study included 66 companies including 33 fraudulent and 33 non-fraudulent companies from 2011 to 2016. The results showed that the presented model with the accuracy of 91.5% can detect fraudulent companies. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models.
[1] Abbasi, A., Albrecht, C., Vance, A., and Hansen, J., Meta Fraud: A meta-learning framework for detecting financial fraud, MIS Quarterly, 2012, 36(4), P. 1293–1327. Doi:10.2307/41703508.
[2] Agarwal, R., and Dhar, V., Editorial-Big Data, data science, and analytics: The opportunity and challenge for IS research, Information Systems Research, 2014, 25 (3), P. 443–448. Doi:10.1287/isre.2014.0546.
[3] Alden, M, E., Bryan, D, M., Lessley, B, J., and Tripathy, A., Detection of Financial statement fraud using Evolutionary Algoriyhms, Journal of Emerging Technologies in Accounting, 2012, 9(1), P. 71-94. Doi:10-2308/jeta-50390.
[4] Apostolou, B., Hassell, J., and Webber, S., Forensic expert classification of management fraud risk factors, Journal of Forensic Accounting, 2000, 1(2), P. 181–192. Doi:10.2308/bria.2001.13.1.1.
[5] Armstrong, C, S., Larcker, D, F., Ormazabal, G., and Taylor, D, J., The relation between equity incentives and misreporting: The role of risk-taking incentives, Journal of Financial Economics, 2013, 109 (2), P. 327–350. Doi:10.1016/j.jfineco.2013.02.019.
[6] Association of Certified Fraud Examiners (ACFE), Report to the Nation on Occupational Fraud and Abuse. Austin, TX: ACFE, 2014.
[7] Athey, S., and Imbens, G., A measure of robustness to misspecification, American Economic Review, 2015, 105(5), P. 476–480. Doi:10.1257/aer.p20151020.
[8] Bayley, L., and Taylor, S., Identifying earnings management: A financial statement analysis (red flag) approach, In Proceedings of the American Accounting Association Annual Meeting, Sarasota, FL: AAA, 2007.
[9] Beasley, M., An empirical analysis of the relation between the board of director composition and financial statement fraud, The Accounting Review, 1996, 71(4), P. 443–465.
[10] Bell, T., and Carcello, J., A decision aid for assessing the likelihood of fraudulent financial reporting, Auditing: A Journal of Practice & Theory, 2000, 19(1), P. 169–184. Doi:10.2308/aud.2000.19.1.169.
[11] Bellman, R., Adaptive Control Processes: A Guided Tour, Princeton, NJ: Princeton University Press, 1961.
[12] Beneish, M., Detecting GAAP violation: Implications for assessing earnings management among firms with extreme financial performance, Journal of Accounting and Public Policy, 1997, 16(3), P. 271–309. Doi:10.1016/50278-4254(97)00023-9.
[13] Beneish, M., Incentives and penalties related to earnings overstatements that violate GAAP, The Accounting Review, 1999, 74(4), P. 425– 457. Doi:10.2308/accr.1999.74.4.425.
[14] Brazel, J. F., Jones, K, L., and Zimbelman, M, F., Using nonfinancial measures to assess fraud risk, Journal of Accounting Research, 2009, 47(5), P. 1135–1166. Doi:10.1111/j.1475-679x.2009.00349.x.
[15] Brown, B., Chui, M., and Manyika, J., Are you ready for the era of ‘‘Big Data’’?, McKinsey Quarterly, 2011, 4, P. 24–35.
[16] Caskey, J., and Hanlon, M., Dividend policy at firms accused of accounting fraud, Contemporary Accounting Research, 2013, 30(2), P. 818–850. Doi:10.111/j.1911-3846.2012.01173.x.
[17] Cecchini, M., G. Koehler, Aytug, H., and Pathak,P., Detecting management fraud in public companies, Management Science, 2010, 56(7), P. 1146–1160. Doi:10.1287/mnsc.1100.1174.
[18] Chan, P., and Stolfo, S., Toward scalable learning with non-uniform class and cost distributions: A case study in credit card fraud detection, In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining. New York, NY, 1998.
[19] Chawla, N. V., Bowyer, K, W., Hall, L, O., and Kegelmeyer, W, P., SMOTE: Synthetic minority oversampling technique, Journal of Artificial Intelligence Research, 2002, 16, P. 321–357. Doi:10.1613/jair.953.
[20] Chen, H., Chiang, R. H., and Storey, V, C., Business intelligence and analytics: From Big Data to big impact, MIS Quarterly, 2012, 36(4), P. 1165–1188. Doi:10.2307/41703503.
[21] Dastjerdi, A, R., Foroghi, D., and Kiani, G,H., Detecting managers fraud risk using test analysis: evidence from Iran, Journal of Applied Accounting Research, 2019, 20(2), P. 154-171. Doi:10.1108/JAAR-01-2018-0016.
[22] Dechow, P. M., Sloan, R, G., and Sweeney, A, P., Causes and consequences of earnings manipulation: An analysis of firms subject to enforcement actions by the SEC, Contemporary Accounting Research, 1996, 13(1), P. 1–36. Doi:10.1111/j.1911-3846.1996.tb00489.x.
[23] Dechow, P. M., Ge, W., Larson, C, R., and Sloan, R, G., Predicting material accounting misstatements, Contemporary Accounting Research, 2011, 28(1), P. 17–82. Doi:10.1111/j.1911-3846.2010.01041.x.
[24] Duin, P. W. R., and Tax, M, J, D., Experiments with classifier combining rules, In Proceedings of the International Workshop on Multiple Classifier Systems, 2000.
[25] Erickson, M., Hanlon, M., and Maydew, E, L., Is there a link between executive equity incentives and accounting fraud?, Journal of Accounting Research, 2006, 44(1), P. 113–143. Doi:10.1111/j.1475-679x.2006.00194.x.
[26] Etemadi, H., Zolqhy, H., Using Logistic Regression to Identify Fraudulent Financial Reporting, Journal of Audit Science, 2013, 13(51), P. 163-145, (In Persian)
[27] Ettredge, M. L., Sun, L., Lee, P., and Anandarajan, A, A., Is earnings fraud associated with high deferred tax and/or book minus tax levels?, Auditing, A Journal of Practice & Theory, 2008, 27(1), P. 1–33. Doi:10.2308/aud.2008.27.1.1.
[28] Fanning, K., and Cogger, K., Neural network detection of management fraud using published financial data, International Journal of Intelligent Systems in Accounting, Finance and Management, 1998, 7(1), P. 21–41. Doi:10.2308/aud.2008.27.1.1.
[29] Feng, M., Ge, W., Luo, S., and Shevlin, T., Why do CFOs become involved in material accounting manipulations?, Journal of Accounting and Economics, 2011, 51(1), P. 21–36. Doi:10.1016/j.jacceco.2010.09.005.
[30] Feroz, E., Kwon, T., Pastena, V., and Park, K., The efficacy of red-flags in predicting the SEC’s targets: An artificial neural networks approach, International Journal of Intelligent Systems in Accounting, Finance and Management, 2000, 9(3), P. 145–157. Doi:10.1002/1099-1174.
[31] Galar, M., Ferna´ndez, A., Barrenechea, E., Bustince, H., and Herrera, F., A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Application sand Reviews, 2012, 42(4), P. 463–484. Doi:10.1109/TSMCC.2011.2161285.
[32] Glancy, F. H., and Yadav, S, B., A computational model for financial reporting fraud detection, Decision Support Systems, 2011, 50(3), P. 595–601. Doi:10.1016/j.dss.2010.08.010.
[33] Goel, S., and Gangolly, J., Beyond the numbers: Mining the annual reports for hidden cues indicative of financial statement fraud, Intelligent Systems in Accounting, Finance and Management, 2012, 19(2), P. 75–89. Doi:10.1002/isaf.1326.
[34] Green, B. P., and Choi, J, H., Assessing the risk of management fraud through neural network technology, Auditing: A Journal of Practice & Theory, 1997, 16(1), P. 14–28.
[35] Gupta, R., and Gill, N, S., A solution for preventing fraudulent financial reporting using descriptive data mining techniques, International Journal of Computer Applications, 2012, 58(1), P. 22–28.
[36] Jahanshad, A., and Sardarizadeh, s., Relation between difference of financial measure (Revenue growth) and nonfinancial measure (Employee growth) with fraudulent financial reporting, Journal of Accounting Research, 2014, 4 (13), P. 181-198, (In Persian).
[37] Jones, K. L., Krishnan, G, V., and Melendrez, K, D., Do models of discretionary accruals detect actual cases of fraudulent and restated earnings? An empirical analysis, Contemporary Accounting Research, 2008, 25(2). P. 499–531. Doi:10.1506/car.25.2.80
[38] Kaminski, K., Wetzel, S., and Guan, L., Can financial ratios detect fraudulent financial reporting?, Managerial Auditing Journal, 2004, 19(1). P. 15–28. Doi:10.1108/02686900410509802.
[39] Kittler, J., Hatef, M., Duin, R, P, W., and Matas, J., On combining classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(3), P. 226–239. Doi:10.1109/34.667881.
[40] Larcker, D. F., and Zakolyukina, A, A., Detecting deceptive discussions in conference calls, Journal of Accounting Research, 2012, 50(2), P. 495–540. Doi:10.1111/j.1475-679x.2012.00450.x.
[41] Lee, T. A., Ingram, R, W., and Howard, T, P., The difference between earnings and operating cash flow as an indicator of financial reporting fraud, Contemporary Accounting Research, 1999, 16(4), P. 749–786. Doi:10.1111/j.1911-3846.1999.tb00603.x.
[42] Lennox, C., and Pittman, J, A., Big five audits and accounting fraud, Contemporary Accounting Research, 2010, 27(1), P. 209–247. Doi:10.1111/j.1911-3846.2010.01007.x.
[43] Lin, J., Hwang, M., and Becker, J., A fuzzy neural network for assessing the risk of fraudulent financial reporting, Managerial Auditing Journal, 2003, 18(8), P. 657–665. Doi:10.1108/02686900310495151.
[44] Loebbecke, J. K., Eining, M, M., and Willingham, J, J., Auditors’ experience with material irregularities: Frequency, nature, and detectability, Auditing: A Journal of Practice & Theory, 1989, 9(1), P. 1–28.
[45] Maham, K., Torabi, A., Providing Financial Reporting Fraud Risk Rating Model, National Conference on Economic Jihad (With Emphasis On National Production, Supporting Iranian Labor and Capital), University of Mazandaran, 2012, (In Persian)
[46] Maloof, M., Learning when data sets are imbalanced and when costs are unequal and unknown, In Proceedings of the 20th International Conference on Machine Learning. Washington, 2003.
[47] Markelevich, A., and Rosner, R, L., Auditor fees and fraud firms, Contemporary Accounting Research, 2013, 30(4), P. 1590–1625. Doi:10.1111/1911-3846.12013.
[48] Mashayekhi, B., and Hosseinpour , A, H., The relationship between real earnings management and accrual earnings management in companies suspected of fraud listed in tehran stock exchange, Empirical Studies in Financial Accounting Quarterly, 2016, 12(49), P. 29-52, (In Persian).
[49] McKee, T, E., A meta-learning approach to prediction financial statement fraud, Journal of Emerging Technologies in Accounting, 2009, 6(1), P. 5-26. Doi:10.2308/jeta.2009.6.1.5.
[50] Perols, J, L., Financial statement fraud detection: An analysis of statistical and machine learning algorithms, Auditing: A Journal of Practice & Theory, 2011, 30(2), P. 19–50. Doi:10.2308/ajpt-50009.
[51] Perols, J, L., and Lougee, B, A., The relation between earnings management and financial statement fraud, Advances in Accounting, 2011, 27(1), P. 39–53. Doi:10.1016/j.adiac.2010.10.004.
[52] Perols, J, L., Bowen, R, M., and Zimmermann, c., Finding Needles in a Haystack: Using Data Analytics to Improve Fraud Prediction, The Accounting Review, 2017, 92(2), P. 221-245. Doi:10.2308/accr-51562.
[53] Persons, O., Using financial statement data to identify factors associated with fraudulent financial reporting, Journal of Applied Business Research, 1995, 11, P. 38-46. Doi:10.19030/jabr.v11i3.5858.
[54] Phua, C., Alahakoon, D., and Lee, V., Minority report in fraud detection: Classification of skewed data, SIGKDD Explorations, 2004, 6(1), P. 50–59. Doi:10.1145/1007730.1007738.
[55] Price, R, A., Sharp, N, Y., and Wood, D, A., Detecting and predicting accounting irregularities: A comparison of commercial and academic risk measures, Accounting Horizons, 2011, 25(4), P. 755–780. Doi:10.2308/acch-50064.
[56] Rahimian, N., Hajiheidari, R., Fraudulent Financial Statement detection Using: Adjusted-M-score-Beneish models and financial ratios, Journal of "Empirical Research in Accounting, 2019, 8(3), P. 47-70. Doi: 10.22051/jera.2018.15993.1713.
[57] Ravisankar, P., Ravi, V., Rao, G, R., and Bose, I., Detection of financial statement fraud and feature selection using data mining techniques, Decision Support System, 2011, 50, P. 491-500. Doi:10.1016/j.dss.2010.11.006.
[58] Sharma, V., Board of director characteristics, institutional ownership, and fraud: Evidence from Australia, Auditing: A Journal of Practice & Theory, 2004, 23(2), P. 105–117. Doi:10.2308/aud.2004.23.2.105.
[59] Summers, S. L., and Sweeney, J, T., Fraudulently misstated financial statements and insider trading: An empirical analysis, The Accounting Review, 1998, 73(1), P. 131–146.
[60] Varian, H. R., Big Data: New tricks for econometrics, Journal of Economic Perspectives, 2014, 28(2), P. 3–27. Doi:10.1257/jep.28.2.3.
[61] Walter, E., Harnessing tomorrow’s technology for today’s investors and markets, Speech Presented at American University School of Law, Washington, DC, 2013.
[62] Weiss, G., Mining with rarity: A unifying framework, ACM SIGKDD Explorations Newsletter, 2004, 6(1), P. 7–19. Doi:10.1145/1007730.1007734.
[63] Whiting, D. G., Hansen, J, V., McDonald, J, B., Albrecht, C., and Albrecht, W. S., Machine learning methods for detecting patterns of management fraud, Computational Intelligence, 2012, 28(4), P. 505–527. Doi:10.1111/j.1476-8640.2012.00425.x.
[64] Witten, I. H., and Frank, E., Data Mining: Practical Machine Learning Tools and Techniques, Second Edition. San Francisco, CA: Morgan Kaufmann Publishers, 2005.
[65] Yang, Q., and Wu, X., 10 challenging problems in data mining research, International Journal of Information Technology and Decision Making, 2006, 5(4), P. 597–604. Doi:10.1142/S0219622006002258.
