Predicting Fraudulent Financial Statements with Machine Learning Techniques

  • Sotiris Kotsiantis
  • Euaggelos Koumanakos
  • Dimitris Tzelepis
  • Vasilis Tampakas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3955)


This paper explores the effectiveness of machine learning techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. To this end, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 164 fraud and non-fraud Greek firms in the recent period 2001-2002. This study indicates that a decision tree can be successfully used in the identification of FFS and underline the importance of financial ratios.


Cash Flow Machine Learning Technique Financial Ratio Audit Firm Bayesian Belief Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sotiris Kotsiantis
    • 1
  • Euaggelos Koumanakos
    • 2
  • Dimitris Tzelepis
    • 1
  • Vasilis Tampakas
    • 1
  1. 1.Department of AccountingTechnological Educational Institute of PatrasGreece
  2. 2.Credit DivisionNational Bank of GreeceGreece

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