財務報表舞弊除了公司本身受到傷害，更造成眾多投資人巨大的損失。近年來有許多學者使用資料探勘的方法來對財務報表舞弊進行研究，都有相當不錯的準確率，但整體文獻還不夠完整。而變數選取包括財務及非財務變數，研究對象為2009年至2016年之27家發生財務報表舞弊公司及81家非財務報表舞弊公司以1：3作樣本配對。本研究共分為二個階段，第一階段採用類神經網路及支援向量機(SVM)做變數篩選，第二階段以決策樹C5.0及支援向量機(SVM)來建立偵測模型。而本研究結果顯示以支援向量機(SVM)進行篩選變數再應用決策樹C5.0建立模型(SVM-C5.0)，偵測財務報表舞弊結果為最佳結果，其準確率為82.87%。
In addition to the damage to the company itself, financial statements fraud has also caused huge losses for many investors. In recent years, many researchers use data mining methods on the research of financial statements fraud to improve the detection accuracy. The research subjects are 27 of financial statement fraud companies and 81 non-financial statements fraud companies from 2009 to 2016 with financial and non-financial variables. In the first stage of this study, artificial neural network (ANN) and support vector machines (SVM) are used to screen the important variables. In the second stage, decision tree C5.0 and support vector machine (SVM) are used to build the prediction models. The results of this study show that the SVM-C5.0 model has the best detection accuracy of 82.87%.
