Predicting fraudulent financial statements with machine learning techniques

Authors:
Sotiris Kotsiantis;Euaggelos Koumanakos;Dimitris Tzelepis;Vasilis Tampakas
Affiliations:
Department of Accounting, Technological Educational Institute of Patras, Greece;Credit Division, National Bank of Greece;Department of Accounting, Technological Educational Institute of Patras, Greece;Department of Accounting, Technological Educational Institute of Patras, Greece
Venue:
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Year:
2006

Citing 7
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Lazy learning

Lazy learning
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Using analytic QP and sparseness to speed training of support vector machines

Proceedings of the 1998 conference on Advances in neural information processing systems II
Machine Learning

Machine Learning
Introduction to Bayesian Networks

Introduction to Bayesian Networks
An adaptation of Relief for attribute estimation in regression

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.