Unsupervised Neural Networks Approach for Understanding Fraudulent Financial Reporting

Purpose – Creditor reliance on accounting‐based numbers as a persistent and traditional standard to assess a firm's financial soundness and viability suggests that the integrity of financial statements is essential to credit decisions. The purpose of this paper is to provide an approach to explore fraudulent financial reporting (FFR) via growing hierarchical self‐organizing map (GHSOM), an unsupervised neural network tool, to help capital providers evaluate the integrity of financial statements, and to facilitate analysis further to reach prudent credit decisions.Design/methodology/approach – This paper develops a two‐stage approach: a classification stage that well trains the GHSOM to cluster the sample into subgroups with hierarchical relationship, and a pattern‐disclosure stage that uncovers patterns of the common FFR techniques and relevant risk indicators of each subgroup.Findings – An application is conducted and its results show that the proposed two‐stage approach can help capital providers evalua...

[1]  A. Shleifer,et al.  Investor Protection and Corporate Valuation , 1999 .

[2]  Kevin C. Moffitt,et al.  Identification of fraudulent financial statements using linguistic credibility analysis , 2011, Decis. Support Syst..

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

[4]  Yong Hu,et al.  The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature , 2011, Decis. Support Syst..

[5]  Lawrence P. Kalbers,et al.  Fraudulent financial reporting, corporate governance and ethics: 1987‐2007 , 2009 .

[6]  Hang Nguyen,et al.  Distractions and motor vehicle accidents: Data mining application on fatality analysis reporting system (FARS) data files , 2005, Ind. Manag. Data Syst..

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

[8]  Chi-Jie Lu,et al.  Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting , 2010 .

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

[10]  Ching-Wen Lin,et al.  Financial early-warning models on cross-holding groups , 2008, Ind. Manag. Data Syst..

[11]  J. Sweeney,et al.  Fraudulently Misstated Financial Statements and Insider Trading: An Empirical Analysis , 1997 .

[12]  Carlos Serrano-Cinca,et al.  Self organizing neural networks for financial diagnosis , 1996, Decision Support Systems.

[13]  Wuchun Chi,et al.  BOARD EFFECTIVENESS: INVESTIGATING PAYMENT ASYMMETRY BETWEEN BOARD MEMBERS AND SHAREHOLDERS , 2008 .

[14]  E. Imhoff.  Accounting Quality, Auditing and Corporate Governance , 2003 .

[15]  Yin‐Hua Yeh,et al.  Family Control and Corporate Governance: Evidence from Taiwan , 2001 .

[16]  Antonio J. Serrano,et al.  Web mining based on Growing Hierarchical Self-Organizing Maps: Analysis of a real citizen web portal , 2008, Expert Syst. Appl..

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

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

[19]  Richard A. Riley,et al.  Financial Statement Fraud: Insights from the Academic Literature , 2008 .

[20]  Patricia M. Dechow,et al.  Causes and Consequences of Earnings Manipulation: An Analysis of Firms Subject to Enforcement Actions by the SEC* , 1996 .

[21]  Y. C. George Lin,et al.  Post-earnings-announcement drift and post-earnings-announcement news , 2008 .

[22]  Jeong Yong Ahn,et al.  On the design concepts for CRM system , 2003, Ind. Manag. Data Syst..

[23]  Yin‐Hua Yeh,et al.  Corporate Governance and Financial Distress: Evidence from Taiwan , 2004 .

[24]  Ronald C. Anderson,et al.  Board Characteristics, Accounting Report Integrity, and the Cost of Debt , 2004 .

[25]  Hannu Vanharanta,et al.  Assessing The Feasibility Of Self-organizing Maps For Data Mining Financial Information , 2002, ECIS.

[26]  A. Shleifer,et al.  A Survey of Corporate Governance , 1996 .

[27]  B. Green,et al.  Assessing the risk of management fraud through neural network technology , 1997 .

[28]  T. Kohonen.  Self-Organized Formation of Correct Feature Maps , 1982 .

[29]  David Zipser,et al.  Feature Discovery by Competive Learning , 1986, Cogn. Sci..

[30]  Asif Ullah Khan,et al.  Classification of Stocks Using Self Organizing Map , 2009 .

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

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

[33]  Andreas Rauber,et al.  Automatic text representation, classification and labeling in European law , 2001, ICAIL '01.

[34]  Florencio López‐de‐Silanes,et al.  Corporate Ownership Around the World , 1998 .

[35]  Joseph V. Carcello,et al.  Fraudulent Financial Reporting: Consideration of Industry Traits and Corporate Governance Mechanisms , 2000 .

[36]  Larry H. P. Lang,et al.  The Separation of Ownership and Control in East Asian Corporations , 1999 .

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

[38]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[39]  Stijn Claessens,et al.  Disentangling the Incentive and Entrenchment Effects of Large Shareholdings , 2002 .

[40]  F. Liou.  Fraudulent financial reporting detection and business failure prediction models: a comparison , 2008 .

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

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

[43]  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.