隨著國人持信用卡消費購物方式的蔚為風氣，致使發卡機構每日所需處理

的申請案件激增；同時，由於信用卡業務的日趨多元化，更增添了審核的

複雜度。傳統以人為判斷為主的審核方法，在有限的人力之下，勢將難以

因應如此龐大的審核需求，而在時間緊迫、經驗累積不足的情形下，難免

會危及授信品質，而增加了此項授信業務的風險。有鑑於此，本研究希望

能藉由範例學習法建立一信用卡信用風險審核模式，期能有效輔助信用卡

發卡審核作業，以降低授信風險，並提昇發卡機構的經營績效。本研究以

某發卡機構為研究對象。抽取個案機構81全年度，審核通過的資料作為研

究樣本。其中以截至82年度3月止，被強制停卡者之不良卡戶，計2,788筆

；而仍繼續流通的正常卡戶，計有97,001筆，總計99,789筆，作為系統學

習及測試所需之資料。本研究首先針對信用卡審核問題的特性，探討範例

學習法的處理策略，我們將計質線索以相對風險的觀念轉換為順序尺度，

並使所建構的二元樹之葉節點（判斷法則）精簡了二分之一左右，和原分

類樹預測能力並無顯著差異。其次，我們進一步運用修剪策略，可將原判

斷法則數由230條減至26條，大幅的提升了執行效率；修剪策略的運用，

雖然降低了區別率，但卻將預測能力（命中率）由67.1%提升至72.58%。

亦即研究結果顯示，避免分類過細，有助於系統預測能力的提升。本研究

範例學習審核模式之預測能力達72.58%，較 Logistic Re- gression 審

核模式高出約6.49%。在重要性線索的選取上，二者具有相當的一致性；

研究結果顯示，原持有一般卡張數、金卡張數、教育程度、公司等級、職

級等為區別力較佳的信用要素。其中區別能力最強的因素為原一般卡持有

張數，張數愈多，信用風險愈高，而此因素為原審核模式所疏漏者，值得

授信人員警惕。此外，我們再將成本效益因素加入分類樹判斷法則，透過

此方式可調整授信的門檻，以增加發卡機構所能獲取的利潤。進一步考量

申請者的信用風險與所得，建立一信用額度核定的方式。研究結果顯示，

以此一模式授與信用額度較原機構之授與方式,高出約一仟二佰萬元淨收

益。此乃由於本模式能有效辨識出不良客戶（命中率為 80.58%），因而

大幅地降低了呆帳的損失。最後，我們綜合本研究的心得，提出一些未來

研究課題，期能使最適分類樹的產生更具效率，並且擴大研究的範圍，希

望能將信用卡範例學習系統推展至各發卡機構，並應用於信用管理的各層

面，以有效提昇信用卡經營效益。

三、中文書籍

[1]　林友田［民７５］，新票據法實施後的因應策略，實業家出版社。
[2]　金融人員研訓中心［民７８］，銀行消費性貸款實務。
[3]　洪燦輝［民７３］，徵信典範，台北：中華徵信所。
[4]　省屬行庫中小企業聯合輔導中心編撰［民８１］，企業徵信與呆帳預防，第三版。
[5]　黃俊英［民８０］，多變量分析，中華經濟企業研究所出版，第四版。
[6]　彭昭英［民８２］，ＳＡＳ與統計分析，台北：儒林書局，初版。
[7]　葉國興［民６９］，授信管理理論與應用，台北：三民書局，初版。
[8]　楊建民［民８０ａ］，專家系統與機器學習－財務專家系統知識庫建構與學習之研究，時英出版社。
[9]　簡國隆［民６５］，如何減少信用交易損失，聯經出版公司。
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