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Forecasting Fraudulent Financial Statements with Committee of Cost-Sensitive Decision Tree Classifiers

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Artificial Intelligence: Theories and Applications (SETN 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7297))

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Abstract

This paper uses 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. A random committee of cost-sensitive decision tree classifiers is the best choice according to our experiments.

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Author information

Authors and Affiliations

  1. Hellenic Open University, Greece

    Elias Zouboulidis

  2. Department of Mathematics, University of Patras, Greece

    Sotiris Kotsiantis

Authors
  1. Elias Zouboulidis
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  2. Sotiris Kotsiantis
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Biomedical Informatics, University of Central Greece, 2-4 Passiopoulou Street, 35100, Lamia, Greece

    Ilias Maglogiannis

  2. Department of Computer Science and Biomedical Informatics, University of Central Greece, 2-4 Papassiopoulou Street, 35100, Lamia, Greece

    Vassilis Plagianakos

  3. Department of Informatics, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Ioannis Vlahavas

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Zouboulidis, E., Kotsiantis, S. (2012). Forecasting Fraudulent Financial Statements with Committee of Cost-Sensitive Decision Tree Classifiers. In: Maglogiannis, I., Plagianakos, V., Vlahavas, I. (eds) Artificial Intelligence: Theories and Applications. SETN 2012. Lecture Notes in Computer Science(), vol 7297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30448-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-30448-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30447-7

  • Online ISBN: 978-3-642-30448-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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