Journal Article10.1016/J.ESWA.2006.02.016
Data Mining techniques for the detection of fraudulent financial statements
TL;DR: 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.
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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.
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Citations
The Necessity of Technological Intelligence for Startups performance: Insights from Algerian Startups using Neural Network Modelling and Fuzzy Logic
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