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Journals / EKEV Akademi Dergisi / 2021 / Cilt: 0 - Sayı: 88

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

İlker KEFE
Jale SAĞLAR
Pages
259–274
Publication Date
2022-10-11
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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).

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, data mining methods used in audit activities are explained.
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Key Findings & Data: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.
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Conclusion & Impact: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).
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Data Mining · Internal Auditing · External Auditing

Abstract

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).

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Cite This Article

KEFE, İ., SAĞLAR, J. (2022). A REVIEW ON DATA MINING METHODS USED IN INTERNAL AUDIT AND EXTERNAL AUDIT. EKEV Akademi Dergisi, Cilt: 0 - Sayı: 88, 259–274. https://ajindex.com/articles/498300

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References (11)

  1. Ağdeniz, Ş. and Yıldız, B. (2018). Muhasebede analiz yöntemi olarak metin madenciliği. Muhasebe Bilim Dünyası Dergisi, 20(2), 286-315.
  2. AICPA. (1999). Top 10 technologies – plus 5 for tomorrow. Journal of Accountancy, 187(5), 16-17.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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 netw