金融機構對中小企業放款風險評估與信用評等整合模式及放款決策之研究

Abstract

信用評等是金融機構用以評量借款企業償債能力的重要依據。由於國內近年來倒帳 事件頻傳，導致銀行對中小企業逾放比居高不下，且「新巴塞爾資本協定」(New Basel Capital Accord)首度明文規定允許銀行使用內部信用評等方法來衡量借款企業之信用風 險，因此，金融機構建構一套有效的信用評等模型已迫在眉睫。目前中外文獻利用統計 方法(Statistical methods)、無母數方法(Nonparametric methods)以及類神經網路(Artificial Neural Networks)來探討此問題，這些方法所構建的模式中以類神經網路架構出的信用評 等模型分類正確率表現較其他方法建構出之模型為佳，但由於類神經網路的運算過程為 一黑盒子，且無法找出重要之解釋變數，使得類神經網路架構出之信用評等模型在實務 應用效果不佳。由於過去中外文獻大多是提供一套判別信用等級之模式，很少文獻提及 如何針對違約樣本之存活期大小(即借款者於發生違約前依約還款之期間長短)構建一套 存活期預估模式，以瞭解其可能發生違約之嚴重程度，這些文獻中有些假設不符合實務 狀況，亦或是需進行適配度檢定(Goodness of Fit Test)來驗證存活期資料是否服從某種分 配，使得文獻中所提之存活期預估模式不恰當或應用不易，因而導致放款機構決策者較 無法瞭解關於放款企業可能還款期長短及擔保品多寡的資訊。放款機構在建構出信用評 等模式之後，可依其放款對像做出多等級之判別，以增加其放款之彈性。然而如何決定 各等級之放款策略，使放款機構能在放款前決定借款者需提供多少擔保品或是應設定多 高之利率，則是放款機構在放款前須考慮之課題。此外，近年來由於企業倒閉違約事件 頻傳，銀行及放款機構之逾期放款比率更趨惡化，造成逾期後償還之比率逐年降低。因 此銀行或放款機構針對償還率逐年下降的問題，急需建立一套完善的違約後償還率評估 模式，以做為銀行製訂客戶違約後催討決策之依據，並降低違約後之損失。因此本計畫 欲建立一個信用評等與放款策略及催收策略之整合流程，本計畫共分三年完成，第一年 將利用羅吉斯迴歸以及類神經網路方法發展出一套複合式違約預測模型，此信用評等模 型首先建立羅吉斯迴歸模式(Logistic Regression)，然後再將羅吉斯迴歸模式的預測結果及事後機率作為後續的類神經網路的輸入變數，藉此來增加整體複合式模型的分類正確 率；此外，藉由使用羅吉斯迴歸來鑑別具有顯著影響的變數，可增加整體複合式模型的 模型解釋能力。接下來，利用自組性演算法構建一套存活期預估模式，然後再根據存活 期的長短作違約企業的等級劃分，建立出多等級之信用評等模型；本計畫第二年的工作 則是將每一個等級中的借款者的預估存活期以及財務及非財務變數，利用TOPSIS 法進 行決策分析以決定可使放款機構利潤最大或是借款者違約時損失最小之擔保品價值、借 款利率或契約期限的長度。本計畫第三年的工作則是利用本計畫綜合借款者特質、信用 評估項目、產業指標及總體經濟指標，納入利用分類決策樹及類神經方法之複合式模型 中，所建立之呆帳及結清組判別式，再利用複迴歸分析構建較現有文獻更合理的償還率 評估模式。最後，本計畫利用償還率預測值、風險暴險額、信用評估項目及總體經濟等 指標構建風險等級模式，作為放款機構催收決策之參考指標。本計畫將利用國內某金 融機構所提供近幾年借款企業之歷史資料驗證本計畫所發展之整合流程確實能 有效地判別借款企業之信用等級。

Due to the increasing global competition, gathering sufficient capital has been a very important issue to many banks or loan companies. For this reason, credit rating become a crucial task for banks or loan companies in the recent decades. Consequently, developing a reliable credit rating model has become an urgent issue for loan companies. Among many studies on credit rating, many methods such as statistical methods, nonparametric methods, and artificial neural networks (ANN) are used to construct credit scoring models. Among these methods, ANN has been proven to have greater predictive power as compared to other credit rating techniques. However, ANN still has some drawbacks such as 「black box procedure」, 「lack of explanation」, 「complex network design」, 「lack of feature selection」etc. These drawbacks make ANN difficult to be used in practice. Some studies employ survival analysis to construct a credit rating model, but these methods require that data must possess certain distributions. After classifying the loan applicants by credit scoring models, banks or loan companies need to make the loan decisions. Due to economic recession and unstable financial market, some companies may not be able to make on-time payment after receiving mortgage loan. In this case, forecasting the recovery rate of these companies to collect the defaulted loan becomes an very important issue. This three-year study is divided into three phases. In the first year, logistic regression and ANN are used to construct hybrid models. First, logistic regression is utilized to select significant input variables. The chosen variables are then used as inputs for ANN to enhance the total predictive accuracy. Group method of data handling (GMDH) is used to construct the credit scoring model. The model can predict the survival rate of loan applicants which are classified as bad loaners. In the second year, TOPSIS method is used to make the loan decisions. The financial variables and non-financial variables of loan applicants are utilized as the attributes to determine the value of collaterals, the rate of loans and time length of the contract. In the third year, the variables such as the features of the borrowers, the terms of credit, the index of industry and the index of macroeconomic, etc. are utilized as the input variables to construct the classification and regression tree (CART). CART is then used to select significant input variables. The chosen variables are then used as inputs for ANN to construct a model to classify whether the loans are uncollectible. A multiple linear regression is also constructed to predict the recovery rates of the loaners. Finally, this study employs the predicted recovery rate, the exposure at default, the terms of credit, and the index of macroeconomic to construct a risk assessment model. A real case from a Taiwan』s loan company is utilized to demonstrate the effectiveness of the proposed method.