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

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. © 2012 Springer-Verlag.