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This paper reviewed the literature describing use of data mining algorithms including statistical test, regression analysis, Neural Network, decision tree, Bayesian network etc for financial accounting fraud detection

A Review of Financial Accounting Fraud Detection based on Data Mining Techniques.

International Journal of Computer Applications, no. 1 (2012): 37-47

Cited: 169|Views48

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Fraud Detectionfinancial statementfraudulent financial statementsfinancial fraudFinancial Accounting FraudMore(15+)

Abstract

With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. The failure of internal auditing system of the organization in identifying the accounting frauds has lead to use o...More

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Introduction

With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) have received considerable attention from the investors, academic researchers, media, the financial community and regulators.
The literature is missing a universally accepted definition of financial fraud, researcher has defined it as ―a deliberate act that is contrary to law, rule, or policy with intent to obtain unauthorized financial benefit‖ [4] and ―intentional misstatements or omission of amount by deceiving users of financial statement, especially investors and creditors‖ [5].
The accounting fraud is defined by accounting professionals as ―deliberate and improper manipulation of the recording of data in financial statements in order to achieve an operating profit of the company and appear better than it is‖ [6]

Highlights

With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) have received considerable attention from the investors, academic researchers, media, the financial community and regulators
This paper presents a comprehensive review of the research literature on the application of data mining techniques to detect financial accounting fraud
The systematic and comprehensive literature review of the data mining techniques applicable to financial accounting fraud detection may provide a foundation to future research in this field
To determine the main algorithms used for financial accounting fraud detection, we present a Review of data mining techniques identified in literature applied to the detection of financial fraud
This paper reviewed the literature describing use of data mining algorithms including statistical test, regression analysis, Neural Network, decision tree, Bayesian network etc for financial accounting fraud detection
The expert fuzzy classification techniques enable one to perform approximate reasoning that can improve performance in three ways
The researchers have not made any comparison so far, related with detecting effect and accuracy of Neural Network compared to regression model

Results

The logistic regression based accounting fraud detecting models are common in literature since the model based on logistic regression can reach up to 95.1% of detecting accuracy with significant expectation effect [41].
The expert fuzzy classification techniques enable one to perform approximate reasoning that can improve performance in three ways

Conclusion

This paper reviewed the literature describing use of data mining algorithms including statistical test, regression analysis, Neural Network, decision tree, Bayesian network etc for financial accounting fraud detection.
The researchers have not made any comparison so far, related with detecting effect and accuracy of Neural Network compared to regression model.
After correct allocation and proper training, Neural Network may perform great classification comparing with regression model.
There are other issues related with Neural Network like no clear explanation on connecting weight, complex accuracy and statistical reliability checking procedure, and lack of explanation

Tables

Table1: Research on Neural Network for Financial Accounting Fraud Detection
Table2: Research on Regression Models for Financial Accounting Fraud Detection
Table3: Research on Fuzzy Logic for Financial Accounting Fraud Detection
Table4: Research on Expert System and Genetic Algorithm for Financial Accounting Fraud Detection

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Funding

The logistic regression based accounting fraud detecting models are common in literature since the model based on logistic regression can reach up to 95.1% of detecting accuracy with significant expectation effect [41]
The expert fuzzy classification techniques enable one to perform approximate reasoning that can improve performance in three ways

Reference

Kotsiantis et al. (2006)
Neural Networks, To explore the effectiveness of neural networks, Kirkos et al. (2007)
Fen-May Liou (2008)
Ravisankar et al. (2011)
Zhou and Kapoor (2011)
3 Hansen et al. (1996)
Summers and Sweeney (1998)
Abbot, Park and Parker (2000)
Bell and Carcello (2000)
Spathis, Doumpos, and Zopounidis (2002)
Owusu-Ansah et al. (2002)
Xuemin Huang (2006)
Haisong Ren (2006)
12 Guoxin et al. (2007) Logistic Regression
Bai, Yen and Yang (2008)
Yuan et al. (2008)
Welch, Reeves and Welch (1998)
Kiehl, Hoogs and LaComb (2005)
Hoogs et al. (2007)
Juszczak et al. (2008)
The paper suggests that one of the reasons for the limited number of relevant journal articles published for financial accounting fraud detection is the difficulty of obtaining sufficient research data. Fanning and Cogger [9] highlight the challenge of obtaining fraudulent financial statements, and note that this creates enormous obstacles in financial accounting fraud detection research. The most urgent challenge facing financial accounting fraud detection is to bridge the gap between practitioners and researchers. The existing financial accounting fraud detection research concentrates on particular types of data mining techniques or models, but future research should direct its attention toward finding more practical principles and solutions for practitioners to help them to design, develop, and implement data mining and business intelligence systems that can be applied to financial accounting fraud detection.
This study has two major limitations. First, our review applied several keywords to search only some online databases for articles published between 1992 and 2011. A future review could be expanded in scope. Second, we considered only articles for financial accounting fraud. Future research could be expanded to include relevant data mining application for detection of frauds in other area like health, communication, insurance, banking etc.
[1] Yue, X., Wu, Y., Wang, Y. L., & Chu, C. (2007). A review of data mining-based financial fraud detection research, international conference on wireless communications Sep, Networking and Mobile Computing (2007) 5519–5522.
[2] Oxford Concise English Dictionary, 11th Edition, Oxford University Press, 2009.
[3] Phua, C., Lee, V., Smith, K. & Gayler, R. (2005). A comprehensive survey of data mining-based fraud detection research, Artificial Intelligence Review (2005) 1–14.
[4] Wang, J., Liao, Y., Tsai, T. & Hung, G. (2006). Technology-based financial frauds in Taiwan: issue and approaches, IEEE Conference on: Systems, Man and Cyberspace Oct (2006) 1120–1124.
[5] Wang, S. (2010). A Comprehensive Survey of Data Mining-Based Accounting-Fraud Detection Research. International Conference on Intelligent Computation Technology and Automation, vol. 1, pp.50-53, 2010.
[6] Accounting Fraud Definition and Examples retrieved from http://www.accountingelite.com/accountingtips/accounting-fraud-definition-and-examples-freeaccounting-fraud-article/
[7] Ngai, E.W.T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2010). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature, Decision Support System (2010), doi:10.1016/j.dss.2010.08.006.
[8] Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements, Expert Systems with Applications 32 (4) (2007) 995–1003.
[9] Fanning, K., Cogger, K., & Srivastava, R. (1995). Detection of management fraud: a neural network approach. International Journal of Intelligent Systems in Accounting, Finance & Management, vol. 4, no. 2, pp. 113– 26, June 1995.
[10] Fanning, K., & Cogger, K. (1998). Neural network detection of management fraud using published financial data. International Journal of Intelligent Systems in Accounting, Finance & Management, vol. 7, no. 1, pp. 21- 24, 1998.
[11] Silverstone, Howard, & Sheetz, M. (2004). Forensic Accounting and Fraud Investigation for Non-Experts. Hoboken, John Wiley & Sons, 2004.
[12] Bologna, Jack & Lindquist, R. J. (1987). Fraud Auditing and Forensic Accounting. New York: John Wiley & Sons, 1987.
[13] Elkan, C. (2001). Magical Thinking in Data Mining: Lessons from COIL Challenge 2000. Proc. of SIGKDD01, 426-431.
[14] Turban, E., Aronson, J.E., Liang, T.P., & Sharda, R. (2007). Decision Support and Business Intelligence Systems, Eighth edition, Pearson Education, 2007.
[15] Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C.J. (1992). Knowledge discovery in databases: An overview, AI Magazine 13 (3) (1992) 57–70.
[16] Bolton, R. J., & Hand, D.J. (2002). Statistical fraud detection: a review, Statistical Science 17 (3) (2002) 235–255.
[17] Zhang, D., & Zhou, L. (2004). Discovering golden nuggets: data mining in financial application, IEEE Transactions on Systems, Man and Cybernetics 34 (4) (2004) Nov.
[18] Derrig, R. A. (2002). Insurance fraud, The Journal of Risk and Insurance 69 (3) (2002) 271–287.
[19] Ahmed, S.R. (2004). Applications of data mining in retail business, International Conference on Information Technology: Coding and Computing 2 (2) (2004) 455– 459.
[20] Mitra, S., Pal, S.K., & Mitra, P. (2002). Data mining in soft computing framework: a survey, IEEE Transactions on Neural Networks 13 (1) (2002) 3–14.
[21] FBI, Federal Bureau of Investigation, Financial Crimes Report to the Public Fiscal Year, Department of Justice, United States, 2007, http://www.fbi.gov/publications/financial /fcs_report2007/financial_crime_2007.htm.
[22] Fawcett, T., & Provost, F. (1997). Adaptive fraud detection, Data Mining and Knowledge Discovery 1 (3) (1997) 291–316.
[23] Sánchez, D., Vila, M.A., Cerda, L., & Serrano, J.M. (2009). Association rules applied to credit card fraud detection, Expert Systems with Applications 36 (2) (2009) 3630–3640.
[24] Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques, Second edition, Morgan Kaufmann Publishers, 2006, pp. 285–464.
[25] Persons O.S. (1995). Using Financial Statement Data to Identify Factors Associated with Fraudulent Financial Reporting.Journal of Applied Business Research,1995,11(3):38-46.
[26] Agyemang, M., Barker, K., & Alhajj, R. (2006). A comprehensive survey of numeric and symbolic outlier mining techniques, Intelligent Data Analysis 10 (6) (2006) 521–538.
[27] Yamanishi, K., Takeuchi, J., Williams, G., & Milne, P. (2004). On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms, Data Mining and Knowledge Discovery 8 (3) (2004) 275–300.
[28] Viaene, S., Derrig, R.A., Baesens, B., & Dedene, G. (2002). A comparison of state-of-the-art classification techniques for expert automobile insurance claim fraud detection, The Journal of Risk and Insurance 69 (3) (2002) 373–421.
[29] Eick, S.G. & Fyock, D.E. (1996). Visualizing corporate data, AT&T Technical Journal 75 (1) (1996) 74–86.
[30] Spathis, C. T. (2002). Detecting false financial statements using published data: some evidence from Greece, Managerial Auditing Journal 17 (4) (2002) 179– 191.
[31] Yeh, I., & Lien, C. (2008). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients, Expert Systems with Applications 36 (2) (2008) 2473–2480.
[32] Ghosh, S., & Reilly, D. L. (1994). Credit card fraud detection with a neural-network, 27th Annual Hawaii International, Conference on System Science 3 (1994) 621–630.
[33] Beasley, M. (1996). An empirical analysis of the relation between board of director composition and financial statement fraud. The Accounting Review, 71(4), 443– 466.
[34] Hansen, J. V., McDonald, J. B., Messier, W. F., & Bell, T. B. (1996). A generalized qualitative—response model and the analysis of management fraud. Management Science, 42(7), 1022–1032.
[35] Summers, S. L., & Sweeney, J. T. (1998). Fraudulent misstated financial statements and insider trading: an empirical analysis. The Accounting Review, 73(1), 131– 146.
[36] Abbot, J. L., Park, Y., & Parker, S. (2000). The effects of audit committee activity and independence on corporate fraud. Managerial Finance, 26(11), 55–67.
[37] Bell, T., & Carcello, J. (2000). A decision aid for assessing the likelihood of fraudulent financial reporting. Auditing: A Journal of Practice & Theory, 9(1), 169– 178.
[38] Spathis, C., Doumpos, M., & Zopounidis, C. (2002). Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques. The European Accounting Review, 11(3), 509–535.
[39] Huang, X. (2006). Research on Public Company Accounting Fraud and regulation-from perspective of protecting investors [D]. Xiamen: Xiamen University, 2006.
[40] Ren, H. (2006). Investigation Research on Public company financial report fraud. [D] Dalian: Dongbei University of Finance, 2006.
[41] Chen, G., Zhanjia L., & Feng, H. (2007). Empirical Study on detecting financial statements Fraud- based on empirical data of public companies. [J] Auditing Study, 2007.
[42] Green, B. P., & Choi, J. H. (1997). Assessing the risk of management fraud through neural-network technology. Auditing: A Journal of Practice and Theory, 16(1), 14– 28.
[43] Cerullo, M. J., Cerullo, V. (1999). Using neural networks to predict financial reporting fraud, Computer Fraud & Security May/June (1999) 14–17.
[44] Lin, J. W., Hwang, M. I., & Becker, J. D. (2003). A Fuzzy Neural Network for Assessing the Risk of Fraudulent Financial Reporting. Managerial Auditing Journal, 2003, 18(8):657-665.
[45] Viaene, S., Derrig, R. A., & Dedene G. (2004). A Case Study of Applying Boosting Naive Bayes to Claim Fraud Diagnosis, IEEE Transactions on Knowledge and Data Engineering, v.16 n.5, p.612-620, May 2004.
[46] Viaene, S., Derrig, R. A., Baesens, & Dedene, G. (2002). A comparison of state-of-the-art classification techniques for expert automobile insurance claim fraud detection, The Journal of Risk and Insurance 69 (3) (2002) 373– 421.
[47] Welch, J., Reeves, T. E., & Welch, S. T. (1998). Using a genetic algorithm-based classifier system for modeling auditor decision behavior in a fraud setting, International Journal of Intelligent Systems in Accounting, Finance & Management 7 (3) (1998) 173–186.
[48] Chen, R., Chen, T., & Lin, C. (2006) A new binary support vector system for increasing detection rate of credit card fraud, International Journal of Pattern Recognition and Artificial Intelligence 20 (2) (2006) 227–239.
[49] Ammar, Salwa, Wright, R., & Selden, S. (2000). Ranking State Financial Management: A Multilevel Fuzzy Rule-based System.‖ Decision Sciences. 31: 449481. 2000.
[50] Lenard, M. J., & Alam, P. (2004). The use of fuzzy logic and expert reasoning for knowledge management and discovery of financial reporting fraud. In H. Nemati and C. Barko (Eds.), Hershey, PA: Idea Group, Inc.
[51] Lenard, M. J., Watkins, A.L., and Alam, P. (2007). Effective use of integrated decision making: An advanced technology model for evaluating fraud in service-based computer and technology firms. The Journal of Emerging Technologies in Accounting 4(1): 123-137.
[52] Pathak, J., Vidyarthi, N., & Summers, S. L. (2005). A fuzzy-based algorithm for auditors to detect elements of fraud in settled insurance claims, Managerial Auditing Journal 20 (6) (2005) 632–644.
[53] Bentley, P. (2000). Evolutionary, my dear Watson: Investigating Committee-based Evolution of Fuzzy Rules for the Detection of Suspicious Insurance Claims. In the Proc. of GECCO 2000.
[54] Hoogs, B., Kiehl, T., Lacomb, C., & Senturk, D. (2007). A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud, Intelligent Systems in Accounting, Finance and Management, 2007, vol. 15: 41-56.
[55] 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.
[56] Bai, B., Yen, J. & Yang. X. (2008). False financial statements: characteristics of China's listed companies and CART detecting approach, International Journal of Information Technology & Decision Making 7 (2) 339– 359.
[57] Deshmukh, A., & Talluru, L. (1998). A rule-based fuzzy reasoning system for assessing the risk of management fraud, International Journal of Intelligent Systems in Accounting, Finance & Management 7 (4) 223–241.
[58] Deshmukh, A., Romine, J., & Siegel, P.H. (1997). Measurement and combination of red flags to assess the risk of management fraud: a fuzzy set approach, Managerial Finance 23 (6) 35–48.
[59] Eining, M. M., Jones, D. R., & Loebbecke, J. K. (1997). Reliance on decision aids: an examination of auditors‘ assessment of management fraud. Auditing: A Journal of Practice and Theory, 16(2), 1–19.
[60] Kotsiantis, S., Koumanakos, E., Tzelepis, D. & Tampakas, V. (2006). Forecasting fraudulent financial statements using data mining, International Journal of Computational Intelligence 3 (2) 104–110.
[61] Owusu-Ansah, S., Moyes, G.D., Oyelere, P.B., Hay, P. (2002). An empirical analysis of the likelihood of detecting fraud in New Zealand, Managerial Auditing Journal 17 (4) 192–204.
[62] Yuan, J., Yuan, C., Deng, Y., & Yuan, C. (2008). The effects of manager compensation and market competition on financial fraud in public companies: an empirical study in China, International Journal of Management 25 (2) (2008) 322–335.
[63] Chai, W., Hoogs, B.K., & Verschueren, B.T. (2006). Fuzzy Ranking of Financial Statements for Fraud detection. In proceeding of International Conference on Fuzzy System, (2006), 152–158.
[64] Kiehl, T. R., Hoogs, B. K., & LaComb, C. A. (2005). Evolving Multi-Variate Time-Series Patterns for the Discrimination of Fraudulent Financial Filings. In Proc. of Genetic and Evolutionary Computation Conference, 2005.
[65] Koskivaara, E. (2000). Different pre-processing models for financial accounts when using neural networks for auditing, Proceedings of the 8th European Conference on Information Systems, vol. 1, 2000, pp. 326–3328, Vienna, Austria.
[66] Feroz, E.H., Kwon, T.M., Pastena, V., & Park, K.J. (2000). 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 9 (3) 145–157.
[67] Pacheco, R., Martins, A., Barcia, R.M., & Khator, S. (1996). A hybrid intelligent system applied to financial statement analysis, Proceedings of the 5th IEEE conference on Fuzzy Systems, vol. 2, 1996, pp. 1007– 10128, New Orleans, LA, USA.
[68] Liou, F. M. (2008). Fraudulent financial reporting detection and business failure prediction models: a comparison. Managerial Auditing Journal Vol. 23 No. 7, pp. 650-662.
[69] Kapardis, M. K., Christodoulou, C. & Agathocleous, M. (2010). Neural networks: the panacea in fraud detection? Managerial Auditing Journal, 25, 659-678.
[70] Ravisankar P., Ravi V., Rao G.R. & Bose I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision Support System, 50, 491-500.
[71] Perols, J. (2011). Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms, Auditing: A Journal of Practice and Theory, Vol. 30(2), pp. 19-50.
[72] Zhou, W. and Kapoor, G. (2011). Detecting evolutionary financial statement fraud. Decision Support Systems, v. 50, n. 3, p. 570-575.

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