Forecasting fraudulent financial statements with committee of cost-sensitive decision tree classifiers

Authors:
Elias Zouboulidis;Sotiris Kotsiantis
Affiliations:
Hellenic Open University, Greece;Department of Mathematics, University of Patras, Greece
Venue:
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Year:
2012

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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.