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研究生:王信勝
研究生(外文):Wang Hsin Sung
論文名稱:整合分析層級程序與類神經網路之信用評分模型
論文名稱(外文):Integrating Analytic Hierarchy Process and Neural networks for Credit Scoring Model
指導教授:李俊民李俊民引用關係
學位類別:碩士
校院名稱:輔仁大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2001
畢業學年度:89
語文別:中文
論文頁數:88
中文關鍵詞:信用評分類神經網路分析層級程序法羅吉斯迴歸法
外文關鍵詞:credit scoringneural networkAHPlogistic regression
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根據財政部中央銀行統計,台灣地區個人消費性放款總額已於民國八十五年年底超過法人企業放款,同時個人消費性放款成長率也大於法人企業放款,使得兩者之間的差距也逐年增加中;再加上台灣地區將加入WTO、經濟自由化與國際化、個人投資理財觀念的盛行,個人放款會巨幅的成長是可以預期的結果,也將成為各銀行未來主要的經濟命脈之一。
在歐美各國,許多較具規模的金融機構皆有屬於自己各項產品的信用評分模型及相關系統與研究部門,但國內銀行所走的腳步較為緩慢,雖有信用評分制度的實行,但多為參照它行加以修改而得之,缺乏科學依據;Shanker (1996)的研究顯示,資料標準化可以改善區別率,本研究希望能利用分析層級程序法將專家的對各項變數權重的看法加入類神經網路中,增加信用評分模型之正確性。資料來源為國內某上市銀行之88-89無擔保信用貸款,採問券方式取得分析層級程序法各項權重,做為類神經網路資料標準化的依據,另一方面以線性方式將資料標準化,訓練出另一倒傳遞類神經網路,再發展羅吉斯迴歸法之信用評分模型,最後將三者以測試樣本做正確率比較,
結論發現,結合分析層級程序法之類神經網路有較高的正確率,發展類神經網路信用評分模型時,我們可以使用分析層級法來做為資料標準化的依據,相信會有不錯的效果。
According to a 1996 national survey conducted by the R.O.C. Central Bank in Taiwan, both the amount and the increasing rate of consumer loan are greater than those of corporate loan. Further, joining the World Trade Organization (WTO), personalized financial management, and international business on Internet make Taiwan’s consumer loan grow up hugely and then consumer loan owns a large proportion of each banks’s total assets in the neare future. In this situation, a successful credit scoring model can be used for each bank to classify applicants for credit into “good” and “bad” risk classes, as well as to survive in a competitive banking market.
In a survey of current techniques for solving a credit scoring model in business, the back propagation neural network and logistics regression models dominated other methods (Velido et al., 1999; West, 2000). On the oter hand, data standardization and the mixture of experts could improve the classification rate and accuracy respectively (Shanker, 1997; West, 2000). Therefore, this study integrates the Analytic Hierarchy Process (AHP) and the back-propagation neural network model sequentially. First, experts’ views and wetghrs related to several consumers’ properties are consistently obtained from the AHP process and then the results are used to train the neural network for more accurate credit scoring model. The data sets are from a bank which already runs over twenty years in Taiwan. Experimental results show that the proposed new approach is found to be the most accurate of the three methods descreibed in this study. Finally, some general guidelines and future research directions ate presented.
表 次vi
圖 次ix
第一章 緒論1
第一節 研究動機1
第二節 研究目的4
第三節 研究範圍5
第四節 預定論文結構5
第二章 文獻探討8
第一節 消費者小額信用貸款8
第二節 國內外相關研究13
一、國外部份13
二、國內部份18
第三章 研究方法25
第一節 研究架構25
第二節 理論基礎與使用工具40
一、 羅吉斯迴歸模式(Logistic Regression )40
二、 類神經網路44
三、分析層級程序法48
第三小節 使用工具55
第四章 實證分析56
第一節 羅吉斯迴歸信用評分模型56
第二節 倒傳遞類神經網路模型60
第三節 AHP與類神經網路混合模型61
一、AHP權重之的決定61
二、混合模型之建立69
第五章 結論與建議70
第一節 結論70
第二節 研究限制與建議74
一、研究限制74
二、研究建議75
參考文獻76
附表一、AHP訪談表81
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