SavedAdd your journal

Journals / İşletme Araştırmaları Dergisi / 2015 / Cilt: 7 - Sayı: 1

Artificial Neural Network Methodology In Fraud Risk Prediction On Financial Statements; An Emprical Study In Banking Sector

Mustafa UĞURLU
ŞERAFETTİN SEVİM
Pages
60–89
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

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

The proposal model that was developed is highly successful in predicting fraud risk in financial statements with an accuracy ratio of 90%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Credit risk is among the foremost risk factors that banks may encounter.
📊
Key Findings & Data:This research includes commercial and corporate customers of banks.
💡
Conclusion & Impact:The proposal model that was developed is highly successful in predicting fraud risk in financial statements with an accuracy ratio of 90%.
🔬Design:Modeling & Simulation
📍Scope:2007 Verileri
⚙️Variables:Domain Parameters

Abstract

Credit risk is among the foremost risk factors that banks may encounter. Banking function is rendering credits. In rendering commercial credits based on fraudulent financial statements, credit risk can occur if banks cannot ensure the repayments of credits completely or partially. This case lead to an important problem for banks. The accuracy and reliability of information provided by financial statements are of crucial importance in credit risk management. In this context, main purpose of this study is to make it possible to predict fraud risk in financial statements. By doing so, it is possible prevent credit risk that may emerge in banks. In this study, to predict and determine fraud risk in financial statements, artificial neural network (ANN) methodology is utilized. This research includes commercial and corporate customers of banks. Financial data of 289 firms, belonging to the year of 2007, (97 firms were assumed to have fraudulent financial statements and 192 firms were in control group) was analyzed, and an ANN model is proposal. The proposal model that was developed is highly successful in predicting fraud risk in financial statements with an accuracy ratio of 90%.

🔖

Cite This Article

UĞURLU, M., SEVİM, Ş. (2015). Artificial Neural Network Methodology In Fraud Risk Prediction On Financial Statements; An Emprical Study In Banking Sector. İşletme Araştırmaları Dergisi, Cilt: 7 - Sayı: 1, 60–89. https://ajindex.com/articles/269148

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (8)

  1. Albrecht, S., Romney, M. (1986). "Red-Flagging Management: a Validation", Advances in Accounting, Vol. 3, pp.32-33.
  2. Apastolou, B., Hassell, J. M., Webber, S. A. (2001). "Management Fraud Risk Factors: Ratings by Forensic Experts", the CPA Journal, 71 (10), pp. 48-52
  3. Arens, A., Loebbecke J. (1994). Auditing: An Integrated Approach, 6th Ed, Englewood Cliffs, and NJ: Prentice-Hall. Ata, H. A., Seyrek, İ. H. (2009). "The Use of Data Mining Techniques in Detecting Fraudulent Financial Statements: An Applicatıon on Manufacturing Firms", Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt: 14, Sayı: 2, ss.157-170 BDDK, (2013). Türk Bankacılık Sektörü Genel Görünümü, BDDK Yayınları,
  4. İstanbul Beasley, M. (1996). "An Empirical Analysis of the Relation Between Board of Director Composition and Financial Statement Fraud", Accounting Review, 71(4), pp.443- 66. Bell, T., Szykowny, S., Willingham, J. (1993). "Assessing the Likelihood of Fraudulent Financial Reporting: a Cascaded Logic Approach", Working Paper, KPMG Peat Marwick, Montvale, NJ. Beneish, M. D. (1997). "Detecting GAAP Violation: Implications for Assessing Earnings Management Among Firms with Extreme Financial Performance", Journal of Accounting and Public Policy, Vol:16, No: 3, pp. 271-309
  5. Beneish, M. D. (1999). "The Detection of Earnings Manipulation", Financial Analysts Journal, Vol:55, No:5, pp. 24-36
  6. Bologna, G., Lindquist R., Wells J. (1996). The Accountant's Handbook of Fraud and Commercial Crime. New York: John Wiley. Braiotta, L. (2004).
  7. Audit Committee Handbook, John Wiley & Sons, New Jearsy/USA. Chen, W. S., Du, Y. K. (2009). "Using Neural Networks and Data Mining Techniques for the Financial Distress Prediction Model". Expert Systems with Applications, Vol: 36, pp. 4075-4086
  8. Clarence, N., Tan, W. (1997). An Artificial Neural Networks Primer with Financial Applications Examples in Financial Distress Predictions and Foreign Exchange Hybrid Trading System, Bond University, Avusturalya. Davia, H., Coggins, P., Wideman, J., Kastantin, J. (1992). Management Accountant's Guide to Fraud Discovery and Control. New York: John Wiley. Elliott, R. K., Willingham, J. J. (1980). Management Fraud: Detection and Deterrence, Petrocelli Boks, New York. Fausett, L. (1994). Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Prentice Hall, New York. Green, B. P., Choi, J. H. (1997). "Assessing the Risk of Management Fraud Through Neural Network Technology", Auditin