Abstract
This paper uses Artificial Neural Networks to develop a model for detecting management fraud. Although similar to the more widely investigated area of bankruptcy prediction, research has been minimal. To increase the body of knowledge on this subject, we offer an in-depth examination of important publicly available predictors of fraudulent financial statements. We test the value of these suggested variables for detection of fraudulent financial statements within a matched pairs sample. We use a self organizing Artificial Neural Network (ANN) AutoNet in conjunction with standard statistical tools to investigate the usefulness of these publicly available predictors. Our study results in a model with a high probability of detecting fraudulent financial statements on one sample. The study reinforces the validity and efficiency of AutoNet as a research tool and provides additional empirical evidence regarding the merits of suggested red flags for fraudulent financial statements.
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