Presenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm
Subject Areas : Financial and Economic ModellingMahmood 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.
Keywords:
Abstract :
[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.
