隨著時代的演進，財務欺詐成為一個全球性問題，對公司和相關利益相關者造成嚴重的負面影響，一個普通的組織應該預計由於欺詐造成的損失佔 3% 到 6%，在某些情況下甚至高達 10%，而自 2009 年以來，欺詐造成的損失增加了 56.5%，這個情況在近二十年中變得更加嚴重，在嚴重的情況下，欺詐活動可能導致公司和相關利益相關者破產，各種業務和公司都受到欺詐的影響。
在過去的文獻中，有很多研究針對財務詐欺進行研究，而對於財務報表，已經有許多文本挖掘和計算智能方法應用於檢測財務報表欺詐，像是支持向量機、決策樹、神經網路等等，但並沒有使用BERT來進行偵測，因此本研究運用這個新興模型來檢測財務報表欺詐，此外，本研究運用Entropy impurity、TF-IDF以及LDA來歸納出針對財務報表詐欺用字的分類，讓使用者能清楚知道財務報表的詐欺出現在哪方面。
在未來研究的延伸中，可以將分析目標擴大到其它的領域，成為不同領域的詐欺判斷工具。

As time elapsed, financial fraud has become a global problem that impacts a severe negative impact on the company and relevant stakeholders. An average organization should expect losses due to fraud to account for between 3% and 6%, in some cases are as high as 10%. Since 2009, losses owing to fraud have risen by 56.5%, and this situation has become more serious in the past two decades, in severe cases, fraudulent activities can lead to bankruptcy of companies and related stakeholders, and all kinds of businesses and companies are influenced by fraud.
In the past literature, there are many studies on financial fraud. For financial statements, there also have been many text mining and computational intelligence methods applied to detect financial statement fraud, for instance, support vector machines, decision trees, neural networks, etc., but there did not have study using BERT for detection, thus this study uses this emerging model to detect financial statement fraud. In addition, this research uses Entropy impurity, TF-IDF, and LDA to summarize the classification of words used for financial statement fraud, allowing users can clearly know where the fraud in the financial statements occurs.
In the extension of future research, the analysis target can be expanded to other fields and become a fraud judgment tool in different fields.

論文審定書 i
致謝 ii
摘要 iii
Abstract iv
TABLE OF CONTENTS vi
LIST OF FIGURES viii
LIST OF TABLES ix
CHAPTER 1 Introduction 1
1.1 Research background and motivation 1
1.2 Research purpose and goals 2
1.3 Organization of the research 2
CHAPTER 2 Literature Review 4
2.1 Financial fraud 4
2.2 Financial statement fraud 6
2.2.1 Fraud triangle 7
2.2.2 Five interactive factors of financial statement fraud - CRIME 9
2.2.3 Management theories of the behavioral aspects of fraud 10
2.2.4 Financial statement fraud detection methods 11
2.2.5 Case in Taiwan 12
CHAPTER 3 PROPOSED RESEARCH APPROACH 14
3.1 Data collection & preprocess 15
3.2 Fraudulent word differentiation 17
3.3 LDA fraudulent topics 20
3.4 BERT fraudulent word expansion 22
3.5 Fraudulent financial statement determination 25
CHAPTER 4 Experiments and results 27
4.1 Data analysis 27
4.2 Fraudulent words 29
4.3 Model training 35
4.4 Model validations 36
4.5 Managerial implications 38
CHAPTER 5 Conclusions 40
5.1 Concluding remarks 40
5.2 Research limitations 40
5.3 Future work 41
References 42

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