Exploring Fraudulent Financial Reporting with GHSOM
The issue of fraudulent financial reporting has drawn much public as well as academic attention. However, most relevant researches focus on predicting financial distress or bankruptcy. Little emphasis has been placed on exploring the financial reporting fraud itself. This study addresses the challenge of obtaining an enhanced understanding of the financial reporting fraud through the approach with the following four phases: (1) to identify a set of financial and corporate governance indicators that are significantly correlated with fraudulent financial reporting; (2) to use the Growing Hierarchical Self-Organizing Map (GHSOM) to cluster data from listed companies into fraud and non-fraud subsets; (3) to extract knowledge from the fraudulent financial reporting through observing the hierarchical relationship displayed in the trained GHSOM; and (4) to provide justification to the extracted knowledge.
KeywordsFinancial Reporting Fraud Growing Hierarchical Self-Organizing Map Knowledge Extraction
Unable to display preview. Download preview PDF.
- 1.Association of Certified Fraud Examiners. Report to the nation on occupational fraud & abuse [Electronic Version], http://www.acfe.com/documents/2006-rttn.pdf
- 3.Beasley, M.S., Carcello, J.V., Hermanson, D.R.: Fraudulent financial reporting: 1987-1997 an analysis of U.S. public companies (1999)Google Scholar
- 5.Dittenbach, M., Merkl, D., Rauber, A.: The Growing Hierarchical Self-Organizing Map. In: The Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks- IJCNN 2000 (2000)Google Scholar
- 10.Loebbecke, J.K., Eining, M.M., Willingham, J.J.: Auditors’ experience with material irregularities: frequency, nature, and detectability. Auditing 9(1), 1–28 (1989)Google Scholar
- 14.Tipgos, M.A.: Why management fraud is unstoppable. CPA Journal 72(12), 34–41 (2002)Google Scholar
- 15.Virdhagriswaran, S., Dakin, G.: Camouflaged fraud detection in domains with complex relationships. In: The Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (2006)Google Scholar