中小企業對國內經濟的發展具有舉足輕重的地位,是台灣經濟發展的磐石基礎,更是創造就業機會的中堅力量。而在當前嚴峻的金融情勢及經濟環境之下,銀行要兼顧因應政府調控經濟發展政策所付予授信供給的任務,以及本身放款業務量的增加,以獲取基本利潤。故銀行對中小企業戶徵、授信品質的提升,防止逾放的發生,以避免限縮盈餘,並善盡大眾存款資金管理人的責任等作為,則顯得更加重要。因此銀行對中小企業的授信政策及徵審角度須有所修正。 本研究主要的目的係以客觀的角度,針對「中小企業」財務結構及經營特性,設計包括「貸方股東往來佔資產比率」等11項以實際值表達之解釋變數,以及包括「有/無會計師財簽」等4項以虛擬類別表達之解釋變數,以建構「銀行對中小企業授信戶之違約預警及風險預測系統」。並應用「因素分析法」萃取出具相關構面(11個變數)的5個主成份因子,加入4項虛擬變數,配合「Logistic迴歸」,以設定實證模型,再以比例機率(Proportional Chance)、最大猜測機率(Maximum Chance)及Press Q 值,評估該系統預測之準確度。 此外,本研究輔以「資料探勘技術」的研究方法,運用「貝氏分類法(Bayesian Classification)」及「決策樹模式(Classification Tree Model)」等二個系統作分析,進行robust測試。以提供銀行授信徵審人員對於中小企業「新貸戶」核貸前風險預測之參考,並作為銀行授信帳管人員對於中小企業「授信戶」貸放後的監控與管理之依據。
This research mainly applies the factor analytic method and the Logistic regression to construct the bank’s “early default-warning and the risk-prediction system of potential lenders in view of the small and medium-sized enterprises”. Considering the financial structure and management characteristic of “small and medium-sized enterprise”, we design an empirical model which includes 11 explanation variables as well as 4 dummy variables, and carry on the empirical analysis. Again by the Proportional Chance, the Maximum Chance and Press the Q value, we evaluate the accuracy of this system prediction. Furthermore, such as “Bayesian Classification and Classification Tree Model”, to conduct robustness test. In order to help the bank to give the loan reference of the risk profile, the loaning out monitoring and the management before the small and medium-sized enterprise to be the new loan household.