Fraudulent Firm Classification: A Case Study of an External Audit

References

• Ali, S., and K. A. Smith. 2006. On learning algorithm selection for classification. Applied Soft Computing 6 (2):119–38. doi:10.1016/j.asoc.2004.12.002.
   Web of Science ®Google Scholar
• Bose, I. et al. 2011. Detection of financial statement fraud and feature selection using data mining techniques. Decision Support Systems 50 (2):491–500. doi:10.1016/j.dss.2010.11.006.
   Web of Science ®Google Scholar
• Bradley, A. P. 1997. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition 30 (7):1145–59. doi:10.1016/S0031-3203(96)00142-2.
   Web of Science ®Google Scholar
• Buntine, W. 2016. Learning classification rules using bayes. Proceedings of the sixth international workshop on Machine learning, Sydney, Australia, ACM,94–98.
   Google Scholar
• Ramos, M. J. 2006. Wiley Practitioner’s Guide to GAAS 2006: Covering All SASs, SSAEs, SSARSs, and Interpretations. In Understanding the entity and its environment and assessing the risks of material misstatement 52–53. John Wiley & Sons.
   Google Scholar
• Chambers, J. M. 1977. Computational methods for data analysis. Technical report, New York.
   Google Scholar
• Cosserat, G. 2009. Accepting the engagement and planning the audit. In Modern auditing, ed. G. Cosserat and N. Rodda, 3rd ed., 734–36. John Wiley & Sons.
   Google Scholar
• Couceiro, M. 2016. Particle swarm optimization. In Fractional order darwinian particle swarm optimization: Applications and evaluation of an evolutionary algorithm, 1–10. Boston, MA: Springer.
   Google Scholar
• Fanning, K. M., and K. O. Cogger. 1998. Neural network detection of management fraud using published financial data. International Journal of Intelligent Systems in Accounting, Finance & Management 7 (1):21–41. doi:10.1002/(SICI)1099-1174(199803)7:1<21::AID-ISAF138>3.0.CO;2-K.
   Google Scholar
• Fawcett, T. 2006. An introduction to roc analysis. Pattern Recognition Letters 27 (8):861–74. doi:10.1016/j.patrec.2005.10.010.
   Web of Science ®Google Scholar
• Finney, D. J. 1992. Miscellaneous problems. Probit Analysi 4:140–45. JSTOR.
   Google Scholar
• Freund, Y., R. Schapire,and N. Abe, 1999. A short introduction to boosting. Journal of Japanese Society For Artificial Intelligence 14:771–80.
   Google Scholar
• Green, B. P., and J. H. Choi. 1997. Assessing the risk of management fraud through neural network technology. Auditing 16 (1):14–16.
   Web of Science ®Google Scholar
• Houston, R. W., M. F. Peters, and J. H. Pratt. 1999. The audit risk model, business risk and audit-planning decisions. The Accounting Review 74 (3):281–98. doi:10.2308/accr.1999.74.3.281.
   Web of Science ®Google Scholar
• Iba, W., and P. Langley 1992. Induction of one-level decision trees. In Proceedings of the ninth international conference on machine learning, Moffett Field, California. 233–40.
   Google Scholar
• Keerthi, S. S., and E. G. Gilbert. 2002. Convergence of a generalized smo algorithm for svm classifier design. Machine Learning 46 (1–3):351–60. doi:10.1023/A:1012431217818.
   Web of Science ®Google Scholar
• Kennedy, J. 2011. Particle Swarm Optimization. In Encyclopedia of Machine Learning, Boston, MA: Springer.
   Google Scholar
• Kothari, V. 2012. A survey on particle swarm optimization in feature selection, 192–201. Berlin, Heidelberg: Springer.
   Google Scholar
• Kotsiantis, S. 2006. Forecasting fraudulent financial statements using data mining. International Journal of Computational Intelligence 3 (2):104–10.
   Google Scholar
• Liaw, A., and M. Wiener. 2002. Classification and regression by randomforest. R News 2 (3):18–22.
   Google Scholar
• Maria, L., C. Murphy, and K. Tysiac. 2015. Data analytics helps auditors gain deep insight, 52–54, New York: Journal of Accountancy.
   Google Scholar
• Neapolitan, R. E., et al. 2004. Introduction to Bayesian Networks. In Learning Bayesian Networks, 40. Chicago: Pearson.
   Google Scholar
• Nikolovski, P., I. Zdravkoski, G. Menkinoski, S. Dicevska, and V. Karadjova. 2016. The concept of audit risk. International Journal of Sciences Basic and Applied Research (IJSBAR) 27 (3):22–31.
   Google Scholar
• Pearl, J. 1985. Bayesian networks: A model CF self-activated memory for evidential reasoning. In Proceedings of the 7th Conference of the Cognitive Science Society, Irvine, California, Pp. 329–334.
   Google Scholar
• Quinlan, J. R. 1986. Induction of decision trees. Machine Learning 1 (1):81–106. doi:10.1007/BF00116251.
   Google Scholar
• Quinlan, J. R. 1996. Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research 4:77–90.
   Web of Science ®Google Scholar
• Rish, I. 2001. An empirical study of the naive bayes classifier. In IJCAI workshop on empirical methods in artificial intelligence 3 (22):41–46. Seattle, Washington: IBM.
   Google Scholar
• Russell, S. J., P. Norvig, J. F. Canny, J. M. Malik, and D. D. Edwards. 2003. Artificial intelligence: A modern approach, Vol. 2, 65–71. Malaysia: Pearson Education Limited
   Google Scholar
• Sharma, A. 2013. A review of financial accounting fraud detection based on data mining techniques. International Journal of Computer Applications.
   Google Scholar
• Smith-Miles, K. A. 2009. Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys (CSUR) 41 (1):16–19.
   Web of Science ®Google Scholar
• Spathis, C. T. 2002. Detecting false financial statements using published data: Some evidence from greece. Managerial Auditing Journal 17 (4):179–91. doi:10.1108/02686900210424321.
   Google Scholar
• Srivastava, R. P., and G. R. Shafer. 1992. Belief-function formulas for audit risk. Accounting Review 67 (2):249–83.
   Web of Science ®Google Scholar
• Staff, A. 2014. Reimagining auditing in a wired world1. Technical report, University of Zurich, Department of Informatics. Zurich: Citeseer.
   Google Scholar
• Triantaphyllou, E. 2013. Multi-criteria decision making methods: A comparative study, Vol. 44. Boston, MA: Springer.
   Google Scholar
• Tschakert, N. 2016. The next frontier in data analytics. Journal of Accountancy. Accessed September 12, 2016. http://www.journalofaccountancy.com/issues/2016/aug/data-analytics-skills.html.
   Google Scholar
• Tysiac, K. 2015. Data analytics helps auditors gain deep insight. Journal of Accountancy. Accessed September 12, 2016. http://www.journalofaccountancy.com/issues/2015/apr/data-analytics-for-auditors.html.
   Google Scholar