關鍵字：舞弊，資料探勘，決策樹，約略集合，類神經網路，決策樹C5.0 (Decision Tree C5.0)，決策樹CHAID (Decision Tree CHAID)
More and more corporate frauds have been discovered in recent years. When a corporation is involved in frauds, the corporation and its investors fall victims to all such frauds. Consequently, the society incurs huge costs to make up for the loss caused by corporate frauds. The great majority of literatures adopted regression models to analyze corporate frauds. In recent years, however, researchers began to trace corporate frauds using data mining method with satisfactory accuracy. Unfortunately, not enough literatures are available at this moment. Therefore, this study attempted to identify the critical variables related to corporate frauds using the conventional stepwise regression method, Chi-square Automatic Interaction Detection (CHAID), and rough set. This study constructed an effective tool, using financial variables and non-financial variables to trace frauds. This study focused on 41 enterprises involved in corporate frauds and 123 enterprises not involved in corporate frauds in 2003 – 2013. According to the research results, both financial information and non-financial information were sufficient to reveal corporate frauds. When variables were selected using rough set and artificial neural network, 88.46% of accuracy was obtained. Therefore, this study concluded that the fraud detection model constructed with artificial neural network and rough set is sufficient to trace corporate frauds, and is a perfect tool that helps auditors and investors to reach major decisions in connection with investment or disinvestment.
Key words: fraud, data mining, decision tree, rough set, artificial neural network, C5.0 (Decision Tree C5.0), CHAID (decision tree CHAID)