2007年美國次級房貸違約衍生國際金融市場之流動性危機，造成金融商品與資產價格下跌，銀行業損失擴大，流動性危機擴散成為健全性危機。導致2008年9月雷曼公司倒閉，引發全球金融經濟危機，蔓延到全世界，百年難得一見。歸納金融危機的緣由，主要為英美大型金融機構利用國際監理裁定，進行營運套利，並從事高槓桿操作，無視於資本適足性的不足。 此外，金融機構的流動性未能確保，表外交易特別是店頭衍生性交易，揭露不透明，監理未能落實。導致金融市場機能失序，顯見國際金融監理核心基準的巴賽爾資本協定，已無法因應金融創新與金融環境的巨變。 為處理本次全球金融危機所凸顯的市場失靈（market failure），解決銀行部門的脆弱性問題，因此，2010年11月12日G20各國領袖通過『新巴塞爾協議』(Basel III)，提高銀行資本適足率與流動性的標準。 本文係以國內某家公營銀行在後金融海嘯『新巴塞爾協議』(Basel III)，提高銀行資本提列要求與加強銀行流動性管理的 Basel III規範， 將自 2013 年起分階段逐步實行，2019 年起則正式全盤施行；屆時更為嚴格的規定，可能會促使銀行改變投資組合、影響銀行的準備金需求與流動性管理策略，強化自有資本比率規範，並訂定槓桿比率、流動性覆蓋比率等相關規定，對其資本適足性及流動性要求的挑戰與對策。

2008年，美國華爾街危機影響全球金融市場，即使美國擬出許多救市計畫，全球股市依舊暴跌。在此危機衝擊下，各大金融機構不但利潤下滑，且資產減記和信貸損失也愈來愈嚴重。造成此一現象的主因即是次級房貸的影響，次級房貸主要是針對收入低、信用不佳卻需要貸款購屋的民眾，這類客戶通常借貸不易，倘若銀行內部沒有完善的評等機制那放款則需承受較大的違約風險。為因應此趨勢，本研究以台灣未上市中小企業為實例，資料的觀察期間為2003至2005年，透過資料採礦流程，建構企業違約風險模型及其信用評等系統。 本研究分別利用羅吉斯迴歸、類神經網路、和分類迴歸樹三種方法建立模型並加以評估比較其預測能力。發現羅吉斯迴歸模型對於違約戶的預測能力及有效性皆優於其他兩者，並選定為本研究之最終模型，並對選定之模型作評估及驗證，發現模型的預測能力表現尚屬穩定，確實能夠在銀行授信流程實務中加以應用。 / In 2008, the financial crisis on Wall Street had severe impacted the global economy. Although the US government has drawn up regulatory policies in an attempt to save the stock market, the value of global stock market has shrunk drastically. As such, the profits of many financial institutes’ have not only plunged, their value of assets have decreased while loss related to mortgage became more severe. The main cause behind this global phenomenon can be attributed to the effect of subprime mortgages. Subprime mortgages are mainly aimed at consumers who have low income and poor credit history but wish to purchase homes through the means of mortgage. These consumers usually find it difficult to obtain mortgage loans. If banks do not have a well structured evaluation system, they would have to bear more risks in the case of a default. To better understand this trend, this research chooses middle and small private enterprises as its samples. The period of observation is 2003 to 2005. Using the data mining process, this research builds a model that shows the risk associated with contract failure and credit score system. The research builds a model based on logistic regression, Neural Network, and cart to compare and contrast each of the three model’s ability to predict. The result shows that logistic regression is better at predicting defaults and is more effective than the other two models. The research, therefore, concludes logistic regression model as the research’s final model to study and evaluate. In process, the research result demonstrates that the logistic regression model makes more precise prediction and its prediction is fairly stable. Logistic regression model is capable for banks to employ in performing credit check.