一、中文部分
林嬋娟,張哲嘉(2009),董監事異常變動,家族企業與企業舞弊之關聯性,會計評論,(48),1-33。
財團法人中華民國會計研究發展基金會-審計準則公報第四十三號「查核財務報表對舞弊之考量」第五十七號「財務報表查核報告」。
曹俊漢(2001),中美審計體系功能之比較觀察,政治科學論叢,(14),127-152。
陳谷楓,林文貴,施光訓(2011),金融舞弊動機因素分析-以挪用資產舞弊為例,文大商管學報,16(2),1-20。
陳雪如,林琦珍,柯佳玲(2009),自願性資訊揭露對財務報導舞弊偵測之研究,會計與公司治理,6(2),1-30。
陳雅琪(2007),董事會結構、家族控制持股、集團企業與財務報表舞弊之關聯性研究,國立成功大學會計學研究所碩士論文,34-54。游謹安(2014),應用逐步迴歸、決策樹、約略集合及類神經網路於偵測企業舞弊,文化大學會計學研究所碩士論文,12-36。黃郁凱(2006),財務報表舞弊預警模型,國立政治大學會計學研究所碩士論文,23-48。楊清香,俞麟,陳娜(2009),董事會特徵與財務舞弊-來自中國上市公司的經驗證據,華中科技大學管理學院,1-7。
葉清江,齊德彰,林欣瑾(2008),企業財務報表舞弊偵測之研究,亞洲管理與人文科學刊,3(1-4),15-30。
廖玉惠(2006),舞弊的預防與查核-完整的機制與防範明確的落實與執行,會計研究月刊,(252),28-37。
劉若蘭,劉政淮,簡溥銘(2015),董監事暨重要職員責任保險與資訊揭露品質及企業舞弊關係之研究,中華會計學刊,11(1),79-114。
二、英文部分
Bierstaker, J. L., & Wright, S. (2001). A research note concerning practical problem-solving ability as a predictor of performance in auditing tasks. Behavioral Research In Accounting, 13(1), 49-62.
Carol, A., & Michael, C. (2001). The effects of experience and explicit fraud risk assessment in detecting fraud with analytical procedures. Accounting, Organizations And Society, 26(1), 25-37.
Chen, S. (2016). Detection of fraudulent financial statements using the hybrid data mining approach. SpringerPlus, 5(1), 5-89.
Green, B. P., & J. H. Choi. (1997). Assessing the risk of management fraud through neural network technology. Auditing:A Journal of Practice & Theory, 16(1), 14-28.
Jan, C. L. (2018). An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan. Open Access Journal, 10(2), 1-14.
Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data Mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), 995-1003.
Kotsiantis, S., Koumanakos, E., Tzelepis, D., & Tampakas, V. (2006). Forecasting Fraudulent Financial Statements using Data Mining. International Journal of Computation Intelligence, 1(3), 104-110.
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Science direct, 50(3), 559-569.
Perols, J. (2011). Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms. A Journal of Practice & Theory, 30(2), 19-50.
Seifert, J. W. (2004). Data mining and the search for security: Challenges for connecting the dots and databases. Government Information Quarterly, 21(4), 461-480.
Sharma, V. D. (2004). Board of Director Characteristics, Institutional Ownership, and Fraud: Evidence from Australia. Auditing: A Journal of Practice and Theory, 23(2), 105-117.
Sharma, A., & Panigrahi, P. K. (2012). A Review of Financial Accounting Fraud Detection based on Data Mining Techniques. International Journal of Computer Applications, 39(1), 37-47.
Song, X. P., Hu, Z. H., Du, J. G., & Sheng, Z. H. (2014). Application of Machine Learning Methods to Risk Assessment of Financial Statement Fraud: Evidence from China. Journal of Forecasting, 33(8), 611-626.
Tang, A., Nicholson, A., Jin, Y., & Han, J. (2007). Using Bayesian belief networks for change impact analysis in architecture design. The Jourmal of Systems and Software, 80(1), 127-148.
Yen, E. C. (2007). Warning signals for potential accounting in blue chip companies - An application of adaptive resonance theory. Information Sciences, 177(20), 4515-4525.