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Exploring Fraudulent Financial Reporting with GHSOM

  • Rua-Huan Tsaih
  • Wan-Ying Lin
  • Shin-Ying Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5477)

Abstract

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.

Keywords

Financial Reporting Fraud Growing Hierarchical Self-Organizing Map Knowledge Extraction 

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References

  1. 1.
    Association of Certified Fraud Examiners. Report to the nation on occupational fraud & abuse [Electronic Version], http://www.acfe.com/documents/2006-rttn.pdf
  2. 2.
    Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance 23(4), 589–609 (1968)CrossRefGoogle Scholar
  3. 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
  4. 4.
    Bell, T.B., Carcello, J.V.: A Decision Aid for Assessing the Likelihood of Fraudulent Financial Reporting. Auditing 19(1), 169–184 (2000)CrossRefGoogle Scholar
  5. 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
  6. 6.
    Dittenbach, M., Rauber, A., Merkl, D.: Uncovering hierarchical structure in data using the growing hierarchical self-organizing map. Neurocomputing 48(1-4), 199–216 (2002)CrossRefzbMATHGoogle Scholar
  7. 7.
    Fanning, K.M., Cogger, K.O.: Neural network detection of management fraud using published financial data. International Journal of Intelligent Systems in Accounting, Finance & Management 7(1), 21–41 (1998)CrossRefGoogle Scholar
  8. 8.
    Hoogs, B., Kiehl, T., Lacomb, C., Senturk, D.: A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud. Intelligent Systems in Accounting Finance and Management 15(1/2), 41–56 (2007)CrossRefGoogle Scholar
  9. 9.
    Kirkos, E., Spathis, C., Manolopoulos, Y.: Data Mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications 32(4), 995–1003 (2007)CrossRefGoogle Scholar
  10. 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
  11. 11.
    Persons, O.S.: Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research 11(3), 38–46 (1995)CrossRefGoogle Scholar
  12. 12.
    Rauber, A., Merkl, D., Dittenbach, M.: The Growing Hierarchical Self-Organizing Map: Exploratory Analysis of High-Dimensional Data. IEEE Transactions on Neural Networks 13(6), 1331–1341 (2002)CrossRefzbMATHGoogle Scholar
  13. 13.
    Shih, J.-Y., Chang, Y.-J., Chen, W.-H.: Using GHSOM to construct legal maps for Taiwan’s securities and futures markets. Expert Systems With Applications 34(2), 850–858 (2008)CrossRefGoogle Scholar
  14. 14.
    Tipgos, M.A.: Why management fraud is unstoppable. CPA Journal 72(12), 34–41 (2002)Google Scholar
  15. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rua-Huan Tsaih
    • 1
  • Wan-Ying Lin
    • 2
  • Shin-Ying Huang
    • 1
  1. 1.Department of Management Information SystemsNational Chengchi UniversityTaipeiTaiwan
  2. 2.Department of AccountingNational Chengchi UniversityTaipeiTaiwan

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