銀行及金融機構主要業務在於管理客戶存款，藉由吸取存款者的資金，再放款給需要資金者以獲取利潤。而在放款時，必需考慮放款風險以及利潤，如果不能掌控信用風險，一旦授信客戶發生違約、無法償還貸款而造成呆帳，銀行反而承受重大損失。現有之風險評估模式之文獻中，大多僅針對預測企業是否會違約的準確率進行研究，未針對修模時機點進行研究。而過於頻繁修正模型會導致實務上操作的困難；而若過長時間未修正模型，則會導致模型對於未來預測樣本之準確率下降。本研究首先利用部分最小平方法的路徑模型，找出變數中具有交互作用之變數加入考慮，再利用逐步邏輯斯迴歸建構模型後，投入變數及交互作用因子，挑選出影響模型之顯著變數，再比較不同年度間預測樣本，顯著變數發生明顯變異之個數，若其佔總顯著變數1/3以上，則表示模型對於未來預測樣本之準確率有誤差，則應加入新資料以修正模型；若其總顯著變數1/3以下，則表示此模型對於不同年度之預測樣本準確率誤差較小，因此可繼續使用此模型對未來樣本預測。在建構此流程後，銀行及金融機構可利用此機制判斷修正模型之時機點，以避免修正模型次數頻繁，或模型長時間未修正造成準確率之誤差。

The principal activities of banks or financial institutions is the management of customer deposits, depositors of funds through the lessons, and then lending those funds to the need for profits. In lending, it is necessary to consider the lending risk and return. If banks or financial institutions could not control the credit risk of the customer, the credit default or bad loans would lend to heavy losses. Literature of credit risk model, the most part are studying the forecast accuracy about the probability of default. There are few about the timing point for the revision of model studies. The modified model too often lead to practical difficulties in operation. If it is too long to modified the model, would result in model prediction decreased for the future accuracy. First, this study used partial least squares path model to identify variables with the interaction, and then using stepwise logistic regression to constructed model. The input use original variables and the interaction of factors. Find the significant variables of the model, and then compare the inter-annual forecast sample. Count apparent variation of the number of significant variables. If the apparent variation of significant variables ratio larger than 1/3, then the model forecast for the future accuracy rate will be error. It should add new information to correct the model. If the total significant variables ratio less than 1/3, then the model predict for different years of the accuracy of error is small. This model can predict the future sample. After the construction of this process, banks and financial institutions can use this mechanism to determine the timing to modified the model. In order to prevent too often or too long to modified model.

中文摘要 I
英文摘要 II
目錄 IV
表目錄 VI
圖目錄 VIII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究方法 4
1.4 研究限制 4
1.5 研究架構 5
第二章 文獻探討 6
2.1 金融機構授信業務 6
2.1.1 授信之類別 6
2.1.2 授信之基本原則 7
2.2 信用評等與風險評估 8
2.2.1 新巴賽爾資本協定 9
2.2.2 信用評等與信用風險 10
2.2.3 信用評等與信用風險之相關文獻 12
2.3 部分最小平方法 18
2.3.1 部分最小平方法簡介 18
2.3.2 部分最小平方法路徑模型 19
2.3.3 部分最小平方法演算步驟 21
2.4 邏輯斯迴歸 24
第三章 研究方法 26
3.1 研究架構 26
3.2 資料收集與變數整理 27
3.3 建構信用風險模型 30
3.3.1 利用部分最小平方法分析變數間關係 30
3.3.2 利用邏輯斯迴歸建構信用風險模型 34
3.4 計算預測樣本間差異 35
第四章 實例驗證 37
4.1 資料收集與整理 37
4.2 實例分析 38
4.2.1 建構信用風險模型 39
4.2.2 將2004年樣本加入建構模型 45
4.2.3將2005年樣本加入建構模型 50
4.3實例分析結果彙總 55
第五章 結論與未來研究方向 57
5.1 結論 57
5.2 未來研究方向 57
參考文獻 59
附錄一 63

王濟川, 郭志剛 (2004) Logistic迴歸模型-方法與應用: 五南出版社。
朱逸暉 (2009) 應用自組性演算法建構多階段信用風險評估模型。國立交通大學工業工程與管理學系碩士論文，新竹市。
江怡慧 (2009) 台灣半導體業海外直接投資之整合模型。國立交通大學科技管理研究所博士論文，新竹市。
行政院金融監督管理委員會 (2009a) 本國銀行備抵呆帳覆蓋率， http://www.banking.gov.tw/ftp/stat/coverage.xls。
行政院金融監督管理委員會 (2009b) 金融展望月刊3月。
吳佩珊 (2008) 建構台灣中小企業兩階段風險評估模型。國立交通大學工業工程與管理學系碩士論文，新竹市。
吳達凱 (2007) 銀行業建立企業信用評等以管理授信信用風險之研究。東吳大學商學院企業管理學系碩士在職專班碩士論文，台北市。
林大溱 (2003) 應用偏最小方差法及小波轉換於製程預測式錯誤診斷。國立臺灣大學化學工程學研究所碩士論文，台北市。
陳英豪 (2005) 應用自組性演算法建構企業信用評等模型。國立交通大學工業工程與管理學系碩士論文，新竹市。
萬哲鈺, 高崇瑋 (2003) 財務報表分析: 華泰文化事業公司。
銀行授信實務概要編輯委員會 (2006) 銀行授信實務概要: 財團法人台灣金融研訓院。
鄭光男 (2009) 金融機構管理者對中小企業之放款決策探討。國立交通大學工業工程與管理學系碩士論文，新竹市。
龐中懿 (2004) 巴塞爾銀行監理委員會對銀行外部稽核規範之探討。元智大學會計研究所碩士論文，桃園縣。
Altman, E. (1968). Discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
Altman, E., Haldeman, R., & Narayanan, P. (1977). Zeta analysis: a new model to identify bankruptcy risk of corporations. Journal of Banking & Finance, 1(1), 29-54.
Atiya, F. (2001). Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Transactions on Neutral Networks, 12(4), 929-935.
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 71-111.
Berkson, J. (1944). Application of the logistic function to bio-assay. Journal of the American Statistical Association, 39(227), 357-365.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295-336.
Collins, R. A., & Green, R. D. (1982). Statistical methods for bankruptcy forecasting. Journal of Economics and Business, 52-57.
Desai, V. S., Crook, J. N., & Overstreet, G. A. (1996). A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research, 95(1), 24-37.
Dutta, S., & Shekhar, S. (1988). Bond rating: a nonconservative application of neural networks. Paper presented at the Proceedings of the IEEE International Conference on Neural Networks.
Edmister, R. O. (1972). An empirical test of financial ratio analysis for small business failure prediction. The Journal of Financial and Quantitative Analysis, 7(2), 1477-1493.
Fan, A., & Palaniswamiet, M. (2000). Selecting bankruptcy predictors using a support vector machine approach. Paper presented at the Proceedings of the International Joint Conference on Neural Networks.
Fukuda, S., Kasuya, M., & Akashi, K. (2009). Impaired bank health and default risk. Pacific-Basin Finance Journal, 17(2), 145-162.
Grunert, J., Norden, L., & Weber, M. (2005). The role of non-financial factors in internal credit ratings. Journal of Banking & Finance, 29(2), 509-531.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2008). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 120, 227-319.
Huang, Z., Chen, H. C., Hsu, C. J., Chen, W. H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision Support Systems, 37(4), 543-558.
Hwang, R. C., Chung, H., & Chu, C. K. (2010). Predicting issuer credit ratings using a semiparametric method. Journal of Empirical Finance, 17(1), 120-137.
Kim, C. N., & McLeod, J. R. (1999). Expert linear models and nonlinear models of expert decision making in bankruptcy prediction: a lens model analysis. Journal of Management Information Systems, 16(1), 189-206.
Laitinen, E. K. (1999). Predicting a corporate credit analyst’s risk estimate by logistic and linear model. International Review of Financial Analysis, 97-121.
Lohmöller, J. B. (1989). Latent variable path modeling with partial least squares: Physica-Verlag.
Mensah, Y. M. (1984). An examination of the stationarity of multivariate bankruptcy prediction models. Journal of Accounting Research, 22(1), 380-395.
Odom, M., & Sharda, R. (1990). Bankruptcy prediction using neural networks. Paper presented at the Proceedings of the IEEE International Conference on Neural Networks.
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131.
Rose, P. S., Andrews, W. T., & Giroux, G. A. (1982). Predicting business failure: a macroeconomic perspective. Journal of Accounting, Auditing & Finance, 6(1), 20-31.
Shin, K. S., Lee, T. S., & J, K. H. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(1), 127-135.
Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
Wold, H. (1975). Path model with latent variables: The NIPALS approach. Quantitative sociology: International perspectives on mathematical and statistical modeling, 307-357.
Wu, C. Y. (2004). Using non-financial information to predict bankruptcy: a study of public. International Journal of Management, 21(2), 194-201.