黃崇興Huang, Chung-Hsing臺灣大學:商學研究所蔡辰裕Tsai, Chen-YuChen-YuTsai2010-06-012018-06-292010-06-012018-06-292008U0001-1401200815535300http://ntur.lib.ntu.edu.tw//handle/246246/184440以Live直播型態的電視購物頻道在台灣近年來快速蓬勃發展,加上郵購、網路購物等以虛擬通路做為銷售方式的無店面零售業市場產值不斷上升,市場已達到成熟階段。各無店面零售業者為了衝業績,紛紛的推出信用卡分期付款的方式,減少顧客每月負擔的金額,以降低商品購買門檻,刺激消費者的購買意願。一方面,在信用卡發卡銀行的宣傳下,近年來信用卡的發卡張數不斷創新高,消費者能更輕易的膨脹自身的信用。在銀行以及企業兩方面的推波助瀾下,消費者的還款能力被過度高估,以致過度濫用個人信用,終於在2005年年底發生了震驚全台的雙卡債風暴,除了造成消費者自身負債累累外,連帶的衝擊發卡銀行與許多的產業,造成企業極為嚴重的壞帳損失。了防範企業再次遭受如此巨大的損失,一套良好的信用風險評估模型是極其必要的。然而,目前企業使用的信用風險評估模型較為簡單,多數僅使用從業人員的經驗法則進行信用風險的控管,容易造成偏誤。研究利用較客觀精確的統計模型-分類與迴歸樹(CART)-進行信用風險評估模型的建置,並以東森購物的顧客資料庫做為驗證分析的資料來源,希望在考慮多元廣範圍的變項下,可以建立出精確度較高的信用風險評估模型,幫助企業降低壞帳損失。TV shopping companies using live broadcast are growing rapidly in Taiwan recently. Plus the sales channels of mail order and web shopping, the market value of the virtual store in retail industry is continuously escalating, and the market has arrived the mature stage. In order to raise the sales, all virtual-store companies in the retail industry provide the service of installment payment by credit card to reduce customers’ burden each month. It also lowers down the threshold of the product purchase and inspires consumers’ purchase willing. Moreover, under the marketing by the credit card issue banks, the number of credit cards outstanding is soaring high year by year. The consumers now can expand their credit easily by using the credit cards. Therefore, under the promotion by both banks and firms, consumers’ abilities of installment payment are strongly overvalue. One can use his credit freely without any limitation. As a result, in the end of 2005, there was a card debt crisis which shocked the nation. The crisis not only caused consumers to have heavy debt burden, but also struck card issue banks and many industries, which leaded firms to the serious loss of the overdue payment. In order to prevent firms from having such huge loss again, a well-designed credit risk evaluation model is extremely necessary. However, the credit risk evaluation model which firms use now is simple. Most of them only use the experience from their employees to control the credit risk, which can cause bias easily. In this research, a more objective and precise statistics model- classification and regression tree (CART) - is used for the construction of the credit risk evaluation model. It also uses consumers’ data from the database of the Eastern Home Shopping and Leisure Company as the source for the verification of the model we construct. It’s expected that under the consideration of various variables, the credit risk evaluation model with higher precision could be established to help firms lower the loss of the overdue payment.目錄一章 緒論 1.1研究背景與動機 1.2研究目的 4.3研究方法與架構 6二章 文獻探討 8.1消費性信用貸款簡介 8.2非金融機構之消費性信用貸款 9.3信用風險管理概論 10.4信用風險模型 12.5分類與迴歸樹簡介 16.6小結 20三章 模型建立 21.1模型情境 21.2研究範圍 22.3變數定義 28.4資料整理 30.5模型建立 33四章 驗證分析 37.1模型結果分析 37.2模型參數選取 38.3模型效用分析 43.4營運流程改善 45五章 結論 47.1研究結論 47.2研究貢獻 48.3研究限制 49.4未來研究方向 50考文獻 51次1-1 信用卡相關資料 22-1 羅吉斯迴歸﹑分類與迴歸樹及類神經網路之特性比較 142-2 典型的信用評分模型所使用之變數 153-1 單變數區別分析之範例 293-2 範例分枝所代表之審單規則 354-1 不同模型參數設定所產生之審單規則數之比較 374-2 分枝門檻挑選值為80%,且樹深在8以上之審單規則範例 374-3 型A誤差與型B誤差說明範例 384-4 樹深為5,分枝挑選門檻值為80%的模型驗證結果 384-5 樹深為8,分枝挑選門檻值為80%的模型驗證結果 394-6 樹深為10,分枝挑選門檻值為80%的模型驗證結果 394-7 樹深為12,分枝挑選門檻值為80%的模型驗證結果 394-8 樹深為15,分枝挑選門檻值為80%的模型驗證結果 394-9 樹深為18,分枝挑選門檻值為80%的模型驗證結果 394-10 樹深為5,延滯顧客佔比90%以上的模型驗證結果 404-11 樹深為8,分枝挑選門檻值為90%的模型驗證結果 404-12 樹深為10,分枝挑選門檻值為90%的模型驗證結果 404-13 樹深為12,分枝挑選門檻值為90%的模型驗證結果 404-14 樹深為15,分枝挑選門檻值為90%的模型驗證結果 404-15 樹深為18,分枝挑選門檻值為90%的模型驗證結果 414-16 東森購物現有審單機制之驗證結果 434-17 本研究之模型與東森購物現有審單機制之比較 434-18 本研究之模型與東森購物現有審單機制之比較(續) 44次1-1 信用卡累計流通張數 21-2 信用卡簽帳金額 31-3 信用卡循環信用餘額 31-4 研究架構流程圖 72-1 使用兩個自變數做判別的樹 172-2 分類與迴歸樹之平面空間分群圖 172-3 節點之分裂過程 192-4 分類與迴歸樹應用於醫療產業之範例(Breiman et al, 1984) 203-1 東森購物營運流程圖-以顧客以信用卡分期付款為例 233-2 東森購物目前的審單流程圖 273-3 範例變數不同分組間延滯比率之比較 293-4 完整資料處理流程圖 323-5 分枝挑選之範例 343-6 模型建立流程圖 364-1分枝挑選門檻值為80%的模型驗證結果 414-2分枝挑選門檻值為90%的模型驗證結果 424-3 改善後之審單流程圖 46application/pdf497166 bytesapplication/pdfen-US信用風險分類與迴歸樹credit riskclassification and regression tree modelCART應用分類與迴歸樹模型於台灣地區無店面零售產業之信用風險評估Applying Classification and Regression Trees Model to the Evaluation of Credit Risk of Virtual Store in Retailing Industry in Taiwanthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/184440/1/ntu-97-R94741047-1.pdf