Data Mining techniques for the detection of fraudulent financial statements

Authors:
Efstathios Kirkos;Charalambos Spathis;Yannis Manolopoulos
Affiliations:
Department of Accounting, Technological Educational Institution of Thessaloniki, P.O. Box 141, 57400 Thessaloniki, Greece;Department of Economics, Division of Business Administration, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Venue:
Expert Systems with Applications: An International Journal
Year:
2007

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Abstract

This paper explores the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. In accomplishing the task of management fraud detection, auditors could be facilitated in their work by using Data Mining techniques. This study investigates the usefulness of Decision Trees, Neural Networks and Bayesian Belief Networks in the identification of fraudulent financial statements. The input vector is composed of ratios derived from financial statements. The three models are compared in terms of their performances.