A REVIEW ON DATA MINING METHODS USED IN INTERNAL AUDIT AND EXTERNAL AUDIT

In this study, data mining methods used in audit activities are explained. Based on the results of the research on data mining, common data mining methods have been determined and the usability of these methods in audit activities is examined. In addition, the analyzed data mining methods were discussed in terms of fraud detection and the cost created by fraud. The study also evaluates which data mining method or methods are more appropriate to prevent these costs. This study focuses on DM techniques, especially artificial neural networks (ANN), logistic regression (LR), decision trees (DT), support vector machines (SVM), genetic algorithms (GA), and text mining (TM).

Keywords:

Data Mining, Internal Auditing, External Auditing Fraud, Fraudulent Financial Reporting.,

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Ağdeniz, Ş. and Yıldız, B. (2018). Muhasebede analiz yöntemi olarak metin madenciliği. Muhasebe Bilim Dünyası Dergisi, 20(2), 286-315.
AICPA. (1999). Top 10 technologies – plus 5 for tomorrow. Journal of Accountancy, 187(5), 16-17.
Albizri, A., Appelbaum, D. and Rizzotto, N. (2019). Evaluation of financial statements fraud detection research: A multi-disciplinary analysis. International Journal of Disclosure and Governance, 16(4), 206-241.
Alden, M., Bryan, D., Lessley, B. and Tripathy,A. (2012). Detection of financialstatement fraud using evolutionary algorithms. Journal of Emerging Technologies in Accounting, 9(1), 71-94.
Amani, F. and Fadlalla, A. (2017). Data mining applications in accounting: A review of the literature and organizing framework. International Journal of Accounting Information Systems, 24, 32-58.
An, A. (2009). Classification methods. In: J. Wang, (Ed.), Encyclopedia of Data Warehousing and Mining (196-201). Hershey: IGI Global.
Ata, A. and Seyrek, İ. (2009). The use of data mining techniques in detecting fraudulent financial statements: An application on manufacturing firms. Suleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, 14(2), 157-170.
Awodele, O., Akinjobi, J. and Akinsola, J.E.T. (2017). A framework for web based detection of journal entries frauds using data mining algorithm. International Journal of Computer Trends and Technology, 51(1), 1-9.
Bauder, R., Khoshgoftaar, T. and Seliya, N. (2017). A survey on the state of healthcare upcoding fraud analysis and detection, Health Services and Outcomes Research Methodology, 17(1), 31-55.
Bhattacharya, S., Xu, D. and Kumar, K. (2011). An ANN-based auditor decision support system using Benford's law. Decision Support Systems, 50(3), 576-584.
Bhattacharyya, S., Jha, S., Tharakunnel, K. and Westland, C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3), 602- 613.
Busta, B. and Weinberg, R. (1998). Using Benford’s law and neural networks as a review procedure. Managerial Auditing Journal, 13(6), 356-366.
Camps-Valls G., Martínez-Ramón M., Rojo-Álvarez, J. L. (2009). Applications of kernel methods. In: J. Wang, (Ed.), Encyclopedia of Data Warehousing and Mining (51- 57). Hershey: IGI Global.
Cerchiello, P. (2009). Data mining and the text categorization framework. In: J. Wang, (Ed.), Encyclopedia of Data Warehousing and Mining (394-399). Hershey: IGI Global.
Chen, W. S. and Du, Y. K. (2009). Using neural networks and data mining techniques for the financial distress prediction model. Expert Systems with Applications, 36(2), 4075-4086.
Chen, Y. J., Liou, W. C., Chen, Y. M. and Wu, J. H. (2019). Fraud detection for financial statements of business groups. International Journal of Accounting Information Systems, 32, 1-23.
Chrysostomou, K., Lee, M., Chen, S. and Liu, X. (2009). Wrapper feature selection. In: J. Wang, (Ed.), Encyclopedia of Data Warehousing and Mining (2103-2108). Hershey: IGI Global.
DallaValle,L.(2009).Dataminingforinternationalization.In:J.Wang,(Ed.),Encyclopedia of Data Warehousing and Mining (424-430). Hershey: IGI Global.
Debreceny, R. and Gray, G. (2011). Data mining of electronic mail and auditing: a research agenda. Journal of Information Systems, 25(2), 195-226.
Dominik, A., Walczak, Z. and Wojciechowski, J. (2009). In: J. Wang, (Ed.), Encyclopedia of Data Warehousing and Mining (202-207). Hershey: IGI Global.
Dutta, I., Dutta, S. and Raahemii, B. (2017). Detecting financial restatements using data mining techniques. Expert Systems with Applications, 90, 374-393.
Feroz, E. H., Kwon, T. M., Pastena, V. and Park, K. (2000). The efficacy of red flags in predicting the sec's targets: An artificial neural networks approach. Intelligent Systems in Accounting, Finance & Management, 9(3), 145-157.
Gaganis, C. (2009). Classification techniques for the identification of falsified financial statements: a comparative analysis. Intelligent Systems in Accounting, Finance & Management: International Journal, 16(3), 207-229.
Gepp, A., Linnenluecke, M., O’Neill, T. and Smith, T. (2018). Big data techniques in auditing research and practice: Current trends and future opportunities. Journal of Accounting Literature, 40, 102-115.
Goel, S. and Gangolly, J. (2012). Beyond the numbers: Mining the annual reports for hidden cues indicative of financial statement fraud. Intelligent Systems in Accounting, Finance and Management, 19(2), 75-89.
Goldberg, D. E.(1989). Genetic algorithms in search, optimization, and machine learning. Reading, USA: Addison-Wesley.
González, P. C. and Velásquez, J. (2013). Characterization and detection of taxpayers with false invoices using data mining techniques. Expert Systems with Applications, 40(5), 1427-1436.
Gray, G. and Debreceny, R. (2014). A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits. International Journal of Accounting Information Systems, 15(4), 357-380.
Green, B. P. and Choi, J. H. (1997). Assessing the risk management fraud through neural network technology. Auditing A Journal of Practice & Theory, 16(1), 14-28.
Gupta, R. and Gill, N. S. (2012). Financial statement fraud detection using text mining. International Journal of Advanced Computer Science and Applications, 3(12), 189-191.
Hajek, P. and Henriques, R. (2017). Mining corporate annual reports for intelligent detection of financial statement fraud-A comparative study of machine learning methods. Knowledge-Based Systems, 128, 139-152.
Han, J. and Kamber, M. (2006). Data mining: Concepts and techniques. Morgan Kaufmann Publishers, USA: San Francisco.
Hassani, H., Huang, X., Silva, E. and Ghodsi, M. (2016). A review of data mining applications in crime. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9(3), 139-154.
Holton, C. (2009). Identifying disgruntled employee systems fraud risk through text mining: A simple solution for a multi-billion dollar problem. Decision Support Systems, 46(4), 853-864.
Hsu, W. (2009). Evolutionary computation and genetic algorithms. In: J. Wang, (Ed.), Encyclopedia of Data Warehousing and Mining (817-821). Hershey: IGI Global.
Jans, M., Lybaert, N. and Vanhoof, K. (2010). Internal fraud risk reduction: Results of a data mining case study. International Journal of Accounting Information Systems, 11(1), 17-41.
Jans, M., Van Der W., Jan M., Lybaert, N. and Vanhoof, K. (2011). A business process mining application for internal transaction fraud mitigation. Expert Systems with Applications, 38(10), 13351-13359.
Keyvanpour, M. R., Javideh, M. and Ebrahimi, M. R. (2011). Detecting and investigating crime by means of data mining: A general crime matching framework. Procedia Computer Science, 3, 872-880.
Kim, Y. and Vasarhelyi, M. (2012). A model to detect potentially fraudulent/abnormal wires of an insurance company: An unsupervised rule-based approach. Journal of Emerging Technologies in Accounting, 9(1), 95-110.
Kirkos, E., Spathis, C. and Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert systems with applications, 32(4), 995-1003.
Kirkos, E., Spathis, C. and Manolopoulos, Y. (2008). Support vector machines, decision trees and neural networks for auditor selection. Journal of Computational Methods in Sciences and Engineering, 8(3), 213-224.
Koh, H. C. and Low, C. K. (2004). Going concern prediction using data mining techniques. Managerial Auditing Journal, 19(3), 462-476.
Krambia-Kapardis, M., Christodoulou, C. andAgathocleous, M. (2010). Neural networks: The panacea in fraud detection? Managerial Auditing Journal, 25(7), 659-678.
Kurban, O. C., Niyaz, Ö. and Yıldırım, T. (2016). Neural network based wrist vein identification using ordinary camera. In 2016 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), Sinaia, Romania, 1-4.
Lewis, R. and Ras, Z. (2009). Facial recognition. In: J. Wang, (Ed.), Encyclopedia of Data Warehousing and Mining (857-862). Hershey: IGI Global.
Li, H., Sun, J. and Wu, J. (2010). Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classificationminingmethods.Expert Systems with Applications, 37(8), 5895-5904.
Lin, C. C., Chiu, A. A., Huang, S. Y. and Yen, D. (2015). Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts' judgments. Knowledge-Based Systems, 89, 459-470.
Lin, S. C. and Lehto, M. (2009). A bayesian based machine learning application to task analysis. In: J. Wang, (Ed.), Encyclopedia of Data Warehousing and Mining (133-139). Hershey: IGI Global.
Liou, F. M. (2008). Fraudulent financial reporting detection and businessfailure prediction models: A comparison. Managerial Auditing Journal, 23(7), 650-662.
Ngai, W. T. E., Hu, Y., Wong, Y. H., Chen, Y. and Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559-569.
Peji ́c Bach, M., Krstic, Ž., Seljan, S. and Turulja, L. (2019). Text mining for big data analysis in financial sector: A literature review. Sustainability, 11(5), 1-27.
Ravisankar, P., Ravi V., Rao, G. R. and Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision Support Systems, 50(2), 491-500.
Rivero, D., Rabuñal, J., Dorado, J. and Pazos, A. (2009). Evolutionary development of ANNs for data mining. In: J. Wang, (Ed.), Encyclopedia of Data Warehousing and Mining (829-835). Hershey: IGI Global.
Saxena, A., Kothari, M. and Pandey, N. (2009). Evolutionary approach to dimensionality reduction. In: J. Wang, (Ed.), Encyclopedia of Data Warehousing and Mining (810-816). Hershey: IGI Global.
Siciliano, R. and Conversano, C. (2009). Decision tree induction. In: J. Wang, (Ed.), Encyclopedia of Data Warehousing and Mining (624-630). Hershey: IGI Global.
Sun, J. and Li, H. (2008). Data mining method for listed companies' financial distress prediction. Knowledge-Based Systems, 21(1), 1-5.
Welch, O., Reeves, T. and Welch, S. (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 and Management, 7(3), 173-186.
West,J. andBhattacharya,M.(2016).Intelligent financial fraud detection:Acomprehensive review. Computers & Security, 57, 47-66.
Xiao, M., Xiaoli, H. and Gaojin, L. (2010). Research on application of data mining technology in financial decision support system. In 2010 3rd International Conference on Information Management, Innovation Management and Industrial Engineering, 4, 381-384.
Yigitbasioglu, O. and Velcu, O. (2012). A review of dashboards in performance management: Implications for design and research. International Journal of Accounting Information Systems, 13(1), 41-59.
Yildiz, B. and Yezegel, A. (2010). Fundamental analysis with artificial neural network. The International Journal of Business and Finance Research, 4(1), 149-158.
Yom-Tov, E. (2003). An introduction to pattern classification decision tree induction. In: O. Bousquet, U. Von Luxburg and G. Rätsch, (Ed.), Advanced Lectures on Machine Learning (1-20). Berlin, Heidelberg: Springer.
Yu, Z., Guang, Y. and Zi-qi, J. (2013). Violations detection of listed companies based on decision tree and K-nearest neighbor. In 2013 International Conference on Management Science and Engineering 20th Annual Conference Proceedings, 1671-1676.
Zhou, W. and Kapoor, G. (2011). Detecting evolutionary financial statement fraud. Decision Support Systems, 50(3), 570-575.
Zhu, D. (2009). Analytical competition for managing customer relations. In: J. Wang, (Ed.), Encyclopedia of Data Warehousing and Mining (25-30). Hershey: IGI Global.