近年來本國金融機構信用快速擴充，而金融機構的營收重於放款業務，但提高放款業務比例並不能代表銀行利潤的增加，放款的風險為逾期放款，必須有好的放款品質，方不致使逾放比率增加，因此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重目標。為減少逾放比率、提高放款量、爭取審核時間，利用知識管理之知識再使用(reuse)的審核機制是必需的，客觀科學化的評分方法更能使徵信資料得到迅速的整理與分析，以利信用放款的決策。
本研究以國內銀行業申貸案例為研究對象，採用案例式推論法(Case Based Reasoning)結合基因演算法(Genetic Algorithm)發展信用評等決策輔助系統，探討國內銀行業者使用之個人信用評分表之表列變數，並將歷史資料分為訓練及測試案例，支援本系統學習出最佳的案例屬性權重，應用在案例的擷取過程，以擷取新申貸案例的最相似歷史案例，建立最適之信用評等模式，從而預測新申貸者授信的成敗，並提供相似的案例供信用審核人員進行決策。研究結果顯示：一、大量訓練案例數會有較佳的預測申貸成敗之結果。二、以k最臨近理論，投票案例數為5時，正常案例預測率及滯繳案例預測率皆可超過75%以上。三、使用投票法為低風險低獲利策略，不使用投票法為高風險高獲利策略。

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