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.

[1]  Andreas Rauber,et al.  The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data , 2002, IEEE Trans. Neural Networks.

[2]  Sankar Virdhagriswaran,et al.  Camouflaged fraud detection in domains with complex relationships , 2006, KDD '06.

[3]  Thomas R. Kiehl,et al.  A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud: Research Articles , 2007 .

[4]  Andreas Rauber,et al.  Uncovering hierarchical structure in data using the growing hierarchical self-organizing map , 2002, Neurocomputing.

[5]  Yu-Jung Chang,et al.  Using GHSOM to construct legal maps for Taiwan's securities and futures markets , 2008, Expert Syst. Appl..

[6]  Kurt Fanning,et al.  Neural Network Detection of Management Fraud Using Published Financial Data , 1998 .

[7]  Obeua S. Persons.  Using Financial Statement Data To Identify Factors Associated With Fraudulent Financial Reporting , 2011 .

[8]  Yannis Manolopoulos,et al.  Data Mining techniques for the detection of fraudulent financial statements , 2007, Expert Syst. Appl..

[9]  Dana R. Hermanson,et al.  Fraudulent Financial Reporting: 1987-1997 An Analysis of U.S. Public Companies , 1999 .

[10]  Joseph V. Carcello,et al.  A Decision Aid for Assessing the Likelihood of Fraudulent Financial Reporting , 2000 .

[11]  Andreas Rauber,et al.  The growing hierarchical self-organizing map , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[12]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[13]  Deniz Senturk-Doganaksoy,et al.  A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud , 2007, Intell. Syst. Account. Finance Manag..