銀行以授信業務為獲利的主要來源，因此授信品質的良窳將直接影響銀行的經營利潤。如何有效控管信用放款的質與量，成為目前各金融機構經營的首要目標。本研究應用類神經網路建立個人消費性貸款信用評等模式，期望能迅速客觀評估貸款申請人之信用風險狀況，以作為授信之依據，並將其與統計方法之鑑別分析進行信用分類準確度之比較。
　　本研究以某銀行消費性貸款授信戶為研究對象，進行兩階段資料探勘工作：第一階段應用類神經網路中的倒傳遞網路與統計鑑別分析方法分別建置個人消費性貸款信用評等之分類模式，以區分銀行客戶之信用類別，並同時採行交叉驗證方法進行模式訓練及驗證之工作，以確保模式之穩定性。第二階段利用關連法則找出不良授信戶之共同特徵與規則，使銀行能更詳盡的瞭解客戶特性，以便未來對不良授信戶進行防範與監控。
　　本研究實證分析結果顯示：倒傳遞網路模式在辨識信用不良顧客上有較佳之分類準確率，而在信用良好顧客之分類結果則與鑑別分析相近。運用關聯法則探勘信用不良顧客可提供銀行相關特徵及資訊以進行監控及防範之工作；而信用良好顧客之特徵及資訊則可作為行銷規劃及相關金融產品設計之依據。

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