本研究主要之目的在於探討公司內部控制缺失與管理階層舞弊訴訟之關係，藉以建構一舞弊訴訟預警模型，進而提供會計師做為查核策略判斷之依據。研究樣本以國內上市櫃公司為主，採一家起訴公司配對二家非起訴公司之模式，共選取74家起訴公司，148家非起訴公司，總計222家公司。參考美國SAS No.82之分類，分為四個風險構面，並以27個舞弊風險要素，做為模型之輸入變數，並運用邏輯斯迴歸及類神經網路建構舞弊訴訟預警模型，以獲取內控缺失與發生起訴與否之關聯性。經其分析回函問卷，結果顯示我國上市櫃公司在管理當局特性構面中風險偏高，此與我國公司多為家族企業之特性相呼應。邏輯斯迴歸模型結果顯示，「公司營運及財務穩定性」和「資產不當配置」兩者之符號為負，顯示當公司營運及財務穩定性狀況不佳和資產配置不當時，則發生舞弊之機會愈高，但「管理當局能力」與預期方向不一致，表示管理階層的教育程度和能力愈高，則發生舞弊之機會愈高。透過類神經網路與邏輯斯迴歸方法做預測準確率、型一型二錯誤率及錯誤歸類成本之分析後，發現以類神經網路所建構之預警模型，預測效能最佳，並優於會計師之實際判斷。

The purpose of this study is to examine the relationship between the internal control system and litigation presence, hence to construct an early warning litigation presence model for assisting accountants on audit strategy making. The samples entirely collected from public companies and the selection strategy are 222 companies, which including 74 companies of sued and 148 companies of nonsued; and one sued company match two nonsued companies.Refer to SAS No. 82, the enter variable in this model has four risk components and 27 embezzlement risk factors; and then try using Logistic regression and Early Warning Litigation Presence Model of Artificial Neural Network to obtain the relationship between internal control fault and whether it would have sued or not. According to the response information of questionnaire, the risk of Management''s characteristics structure is higher in domestic public companies, which is respond to family enterprise.Logistic regression model displays the negative symbol between operation characteristics and financial stability and susceptibility of assets to misappropriation, that means the embezzlement may be happed when company is in bad operating, unsteady finance, and susceptibility of assets to misappropriation. However, the managerial capacity inconsistent.with anticipation, there may be an opportunity for company to be corrupt when the stratum of management has excellent ability and knowledge.To forecast the rate of accuracy by Artificial Neural Network and Logistic regression, and analyze the rate of error and classified error cost in type of 1 error and type of 2 error, it can be found the best forecast efficacy is Warning Model of Artificial Neural Network and it is superior to CPA’s judgment.

目 錄
第一章 緒論
第一節 研究動機 ……..…..………………………….……..1
第二節 研究目地………..………………………………….. 5
第三節 預期貢獻………..………………………………….. 5
第四節 研究架構………..………………………………….. 6

第二章 文獻探討
第一節 影響審計人員判斷之因素.………….………..…....7
第二節 內部控制、舞弊和訴訟之關聯性.….……..……...11
第三節 審計決策支援工具之運用與評估…..…….………16
第四節 邏輯斯迴歸、專家系統與類神經網路之比較.......22
第三章 研究方法與假說
第一節 觀念性架構…………………………….…............. 26
第二節 變數定義…………………………….…………..... 27
第三節 問卷設計及研究樣本選取………….…………... ..28
第四節 研究方法………………………………………….. 31
第四章 實證結果分析
第一節 敘述性統計及信度與效度分析………………….. 41
第二節 邏輯斯迴歸之結果………………………..….…... 46
第三節 類神經網路之結果………………………………..52
第五章 結論與建議
第一節 實證結論……………………………………….….60
第二節 研究限制………………………………......……... 61
第三節 研究建議及未來研究方向…………………….… 62
第四節 總結………………………………………..…..…..64
參考文獻…………………………………………….…………....... 65
附錄一 全國司法機關審理偵辦之舞弊案…………….………71
附錄二 公開發行公司問卷……………………………….……74
附錄三 被起訴案件舞弊風險因素判讀紀錄………….………77
附錄四 審計人員問卷…………………………………….……90

參考文獻
一、中文部分
財團法人中華民國會計研究發展基金會審計準則委員會，審計準則公報第一號一般公認審計準則總綱。
財團法人中華民國會計研究發展基金會審計準則委員會，審計準則公報第二十五號內部稽核工作之採用。
財團法人中華民國會計研究發展基金會審計準則委員會，審計準則公報第三十一號電腦資訊系統環統下執行查核工作之考量。
財團法人中華民國會計研究發展基金會審計準則委員會，審計準則公報第三十二號內部控制之考量。
王濟川、郭志剛，2003，Logistic 迴歸模型-方法及應用，五南書局。
呂佳蕙，1998，審計人員對舞弊風險因素認知之研究，國立台灣大學會計學研究所碩士論文，未出版。
陳文彬，2002，2001年臺灣上市上櫃公司內部高層權責劃分及內部控制狀況研析--公司治理與上市上櫃公司內部控制之探討 (上)，實用月刊326期。
陳文彬，2002，2001年臺灣上市上櫃公司內部高層權責劃分及內部控制狀況研析--公司治理與上市上櫃公司內部控制之探討 (上)”，實用月刊327期。
馬秀如，1996，研究企業行為 洞悉三方關係－非法事項、錯誤及舞弊，會計研究月刊129期。
馬秀如，1996，傳遞真意 解讀疑義－員工舞弊與管理舞弊釋疑，會計研究月刊126期。
張瑞當、曾玉琦，1997，決策支援系統，今日會計67期。
林嬋娟，管夢欣，1997，財務報表查核之舞弊考量，今日會計68期。
羅贊興、陳怡成、凌啟東、呂佳惠，2001，上市公司重大舞弊案例解析及偵測之探討，台灣證券交易所。
葉怡成，2003，類神經網路模式應用與實作，儒林書局。
葉怡成，2001，類神經網路模式應用，儒林書局。
周文賢，2002，多變量統計分析:SAS/STAT使用方法，智勝文化。
黃金火，1989，專家系統應用於審計程式規劃，國立成功大學工業管理研究所碩士論文，未出版。
柯鎮洪，“明槍易躲暗箭難防舞弊、錯誤與非法事件－會計師執業風險”，會計研究月刊 103期，民國八十三年六月。
劉立恩，羅震國，1999，落實企業自評「與法令遵循有關」之內部控制何能吳窒無礙，會計研究月刊 166期。
劉立恩，羅震國，1999，從內部控制聲明書論公司法令遵循之法與罰，會計研究月刊 103期。
陳文彬，2000，企業如何自行評估內部控制(上)，實用月刊304期。
陳文彬，2000，企業如何自行評估內部控制(下)，實用月刊305期。
陳惠玲，2004，偵察財報舞弊可能性之理論—以SAS No.99為師，貨幣觀測與信用評等。
馬秀如、李美雀，2004，內部控制與員工舞弊-理侓案的省思，會計研究月刊218期。
康榮寶、陳美娥、羅吉台，2003，以財務預警模式強化公司治理價值，會計研究月刊212期。
歐進士、王秀珍，2002，淺論財務報表詐欺，會計研究月刊205期。
蔡篤村，2002，以內部控制強化公司治理之探討，國立台灣大學會計學研究所碩士論文，未出版。
陳耀茂、殷純淵，2004，類神經網路使用手冊，鼎茂書局
二、英文部分
Abdolmohammadi, M. and Wright, A. (1987) “An Examination of the Effects of Experience and Task Complexity”, The Accounting Review, 11(1), pp.-13.
Anandarajana, M. and Anandarajanb, A. (1999) “A comparison of machine learning techniques with a qualitative response model for auditor’s going concern reporting”, Expert Systems with Applications, 16, pp.385–392.
Ashton, R. H., (1974a) “An Experimental Study of Internal Control Judgments”, Journal of Accounting Research, 28(1), pp.143-157.
Ashton, R. H. and Brown, P. R. (1980) “Descriptive Modeling of Auditors Internal Contrlo Judgments : Replication and Extension”, Journal of Accounting Research, 17(2), pp.269-277.
Apostolou, A.B., Hassell, J. M., Webber, S. A., and Sumners, G. E. (2001) “The relative importance of management fraud risk factors”, Behavioral Research in Accounting, 13, pp.-24.
Apostolou, A.B., Hassell, J. M., and Webber, S. A. (2001) “Management fraud risk factors: Ratings by forensic experts”, The CPA Journal, 71(10), pp.48-53.
Ashutosh, D. and Talluru, L. (1998) “A rule-based fuzzy reasoning system for assessing the risk of management fraud”, International Journal of Intelligent Systems in Accounting, Finance and Management, 9, pp.223-241.
Bazerman, M.H., Loewenstein, G., and Moore, D. A. (2002) “Why good accountants do bad audits”, Harvard Bussiness Review, 80(11), pp.97-102.
Bell, T. B. and Carcello, J. V. (2000) “A decision aid for assessing the likelihood of fraudulent financial reporting”, Auditing, 19(1), pp.169-185.
Bonner, S. E. (1990) “Experience Effects in Auditing:The Role of Task-Specific Knowledge”, The Accounting Review, 65(1), pp.72-92.
Bonner, S. E. and Walker, P. L. (1994) “The Effects of Instruction and Experience on the Acquisition of Auditing Knowledge”, The Accounting review, 69(1), pp.157-178.
Bonner, S. E. and Lewis, B. L. (1990) “Determinants of Auditor Expertise”, Journal of Accounting Research, 28(2), pp.1-20.
Caplan, D. (1999) “Internal controls and detection of management fraud”, Journal of Accounting Research, 37(1), pp.101-118.
Calderon, T. G., and Cheh, J. J. (2002) “A roadmap for future neural networks research in auditing and risk assessment”, International Journal of Accounting Information Systems, pp.203–236.
Davis, J. T., Massey, A. P., and Lovell, R. E. (1997) “Supporting a complex audit judgment task：An expert network approach”, European Journal of Operational Research, 103, pp.350-372.
Davis, J. T. (1996)“Experience and auditors'' selection of relevant information for preliminary control risk assessments”, Auditing, 15(1), pp.16-37.
Etheridge, H. L., Sriram, R. S., and Hsu, H. Y. (2000) “A comparison of selected artificial neural networks that help auditors evaluate client financial viability”, Decision Science, 31, pp.23-43.
Eining, M. M., Jones, D. R., and Loebbecke, J. K. (1997) “Reliance on decision aids: An examination of auditors'' assessment of management fraud”, Auditing, 16(2), pp.1-20.
Feroz, E. H., Kwon, T. M., Pastena, V. S., and Park, K. (2000) “The efficacy of red flags in predicting the SEC’s target: An artifical neural networks approach”, International journal of intelligent systems in accounting, financial ＆ management, 9, pp.145-157.
Fanning, K. K., Cogger, O., and Srivastava, R. (1995) “Detection of management fraud: a neural network approach”, International Journal of Intelligent Systems in Accounting, Finance & Management, 4(3), pp.113-126.
Fanning, K. and Cogger, K. O. (1998) “Neural network Detection of management fraud using published financial data”, International Journal of Intelligent Systems in Accounting, Finance & Management, 7(1), pp.21-41.
Frederick, D. M. and Libby, R. (1986) “Expertise and Auditors Judgments of conjunctive Events”, Journal of Accounting Research, 24(2), pp.270-290.
Green, B. P. and Choi, J. H. (1997) “Assessing the risk of management fraud through neural network technology”, Auditing, 16(1), pp14-28.
Gray S.J. (1988) “Towards a theory of cultural influence on the development of accounting systems internationally”, Abacus, 24(1), pp.23-38.
Hansen, J. V., McDonald, J. B., and Stice, J. D. (1992) “Artificial intelligence and generalized qualitative-response models: an empirical test on two audit decision-making domains”, Decision Science, 23, pp.56-69.
Hansen, J. V., McDonald, J. B., Messier Jr, W. F., and Bell, T. B. (1996) “A generalized qualitative-response model and the analysis of management fraud”, Management Science, 42(7), pp.96-114.
Hamilton, R. E. and Wright, W. F. (1982) “Internal Control Judgments and Effects of Experience: Replications and Extensions”, Journal of Accounting Research, 20(2), pp.756-765.
Jerry, W. L., Hwang, M. I., and Becker, J. D. (2003) “A fuzzy neural network for assessing the risk of fraudulent financial reporting”, Managerial Auditing Journal, 18(8), pp657-665.
Joseph, T. W. (2005) “Corporate Fraud Handbook Prevention and Detection”, The CPA Journal, 75(1), pp.16-17.
Kaplan, S. and Reckers, M. J. (1995) “Auditors’ reporting decisions for accounting estimates: the effect of assessments of the risk of fraudulent financial reporting”, Managerial Auditing Journal, 10(5), pp.27-36.
Koskivaara, E. (2004) “Artificial Neural Networks in analytical review procdures”, Turku Centre for Computer Science, TUCS Technical Report No.509, pp.8-32.
Kranacher, M. J. and Lorraine, S. (2004), “Enhancing Fraud Detection Through Eduaction”, The CPA Journal, 74(1), pp.66-68.
Lenard, M. J., Alam, P., and David, B. (2000) “An analysis of fuzzy clustering and a hybrid model for the auditor''s going concern assessment”, Decision Sciences, 31(4), pp.861-885.
Lenard, M. J. Alam, P., and Gregory, M. R. (1995) “The application of neural networks and a qualitative response model to the auditor''s going concern uncertainty decision”, Decision Sciences, 26(2), pp.209-228.
Messier, W. F. (1983) “The Effect of Experience and Firm Type on Materiality/Disclosure Judgments”, Journal of Accounting Research, 21(2), pp.611-618.
O’Leary, E. D. (1993) “Determining differences in expert judgment : implication for knowledge acquisition and validation ”, Decision Sciences, 24(2), pp.65-87.
Ragothaman, S., Carpenter, J., and Butters, T. (1995) “using rule induction for knowledge acquision : an expert systems approach to evaluation material errors and irregularities”, Expert Systems with Application, 9(4), pp.483-490.
Romney, M. B., Albrecht, W. S., and Cherrington, D. J. (1980) “Auditors and the Detection of Fraud”, The Journal of Accountancy, pp.63-69.
Rose A. M. and Jacob M. R. (2003) “The Effect of Fraud Risk Assessments and a Risk Analysis Aid on Auditor Evaluation of Evidence and Judgment”, Accounting Forum, 27(3), PP.312-338.
Sorensen, J., Grove, H. D., and Selto, F. H. (1982) “Detecting Management Fraud: An Empirical Approach”, Symosium on Auditing Research, 18(2), pp.75-89.
Stephen, O. A., David, G. M., Oyelere, P. B., and David, H. (2002) “An empirical analysis of the likelihood of detecting fraud in New Zealand”, Managerial Auditing Journal, 17(4), pp.192-204.
Shelton, S. W., Whittington, O. R., and David, L. (2001) “Auditing firms'' fraud risk assessment practices”, Accounting Horizons, 15(1), pp.19-34.
Willet, C. and Page, M. (1996) “A suryey of time budgets and irregular auditing practices among newly qualigied UK chartered accountants”, The British Accounting Review, 28, pp.101-120.
Zimbelman, M. F. and Hoffman, V. B. (1997) “ The effects of SAS No. 82 on auditors'' attention to fraud risk factors and audit planning decisions / Discussion”, Journal of Accounting Research, 35, pp.75-104.