財務報表舞弊經常造成龐大損失，若能利用公開資訊偵測財報舞弊將對公司利害關係人有莫大幫助。本文以2001~2012年間台灣上市(櫃)公司因財務報表舞弊遭金融監督管理委員會證期局起訴與投資者保護中心公布的團體訴訟及仲裁案件當中之29間公司為舞弊樣本，與其配對之非舞弊公司為58間，藉由資料探勘技術(機率神經網路、支援向量機、羅吉斯迴歸、類神經網路、決策樹)、財務變數以及公司治理變數，針對財務報表年報資料來進行分析，建構出財報舞弊偵測模型。變數篩選採用獨立樣本t檢定法，選取較有影響力、差異性的變數，減少投入不必要的變數而影響分析結果。
所建構之模型依據不同類型的投入變數而分成三種型態之舞弊偵測模型，而最後結果認為以機率神經網路方法(PNN)配合選取之財務變數、公司治理變數以及Z-score所建構出來的財報舞弊偵測模型具有較佳的偵測率。

Fraudulent financial reporting often leads to huge losses. Using publicly available information to detect fraudulent financial statement will be great help to the companies’ interested. This thesis uses data mining techniques such as Probabilistic Neural Network (PNN), Support Vector Machines (SVM), Artificial Neural Network (ANN), Logistic Regression (LR), and Decision Tree (DT) to identify companies that have financial statement fraud. Each of these techniques is tested on a dataset involving 87 Taiwan companies and compared with feature selection. The model accuracy of PNN with financial variables and corporate governance variables outperformed others in this thesis, so regarded this model as a better fraud detection model.

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