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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Kotsiantis, S., Koumanakos, E., Tzelepis, D., & Tampakas, V. (2006). Predicting fraudulent financial statements with machine learning techniques. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3955 LNAI, pp. 538–542). https://doi.org/10.1007/11752912_63
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