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論文中文名稱:信用卡持有人顧客價值估計及行為異質性分析 [以論文名稱查詢館藏系統]
論文英文名稱:Credit Card Holder's Customer Value Measurement and Heterogeneity Analysis [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:管理學院
系所名稱:商業自動化與管理研究所
畢業學年度:97
出版年度:98
中文姓名:邱芳榆
英文姓名:Fang-Yu Chiu
研究生學號:96488040
學位類別:碩士
語文別:中文
口試日期:2009-06-17
論文頁數:45
指導教授中文名:邱志洲
口試委員中文名:蔡榮發;傅新彬
中文關鍵詞:行為評分信用評分貝氏統計潛在變項模型分類迴歸樹
英文關鍵詞:Behavior scoringCredit scoringBayesianLatent variable modelClassification and regression tree
論文中文摘要:本研究嘗試提出藉由應用貝氏統計的潛在變項模型 (Latent variable model)與分類迴歸樹(Classification and Regression Tree)建構出一套整合模式以解決目前金融機構面對授信業務時需面對的三項難題:第一,銀行無法明確地得知目前的授信決策對未來的風險影響程度為何;當金融機構欲針對申請者的歷史交易和還款情況授予適當的產品(如:信用額度或循環利率)時,常苦無適當的資訊說明授信過程與結果;最後,在授信作業受限於人工審核機制下,易使作業成本增加且效率不彰。因此,為驗證本研究所提整合模式的可行性,針對台灣某大銀行的顧客歷史交易與還款紀錄,以及人口統計變數、歷史信用行為及銀行的授信程度(信用額度及循環利率)資訊於模式中。經由研究證實,藉由持卡人的交易行為中萃取的顧客價值對評分模式而言,其重要程度不僅遠高於人口統計變數,且具有顯著的價值性。此外,整合模式在辨識顧客的能力上除平均鑑別正確率可達91.425%外,亦具有誤判成本較低的優勢。
論文英文摘要:A Bayesian latent variable model with classification and regression tree approach is built to overcome three challenges encountered by a bank in credit-granting process. These three challenges include (1) the bank wants to predict the future performance of an applicant accurately; (2) given current information about cardholders’ credit usage and repayment behavior, financial institutions would like to determine the optimal credit limit and APR for an applicant; and (3) the bank would like to improve its efficiency by automating the process of credit-granting decisions. The data set consists of each credit card holder’s credit usage and repayment data, demographic information, and credit report. Empirical study shows that the demographic variables used in most credit scoring models have little explanatory ability with regard to a cardholder’s credit usage and repayment behavior. A cardholder’s credit history provides the most important information in credit scoring. Compared to the performance of discriminate analysis and logistic regression, the proposed model has a 91.425% average accurate rate in predicting customer types, and has the lowest misclassification cost.
論文目次:摘 要 i
ABSTRACT ii
誌 謝 iii
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究範圍 3
1.4 論文架構 4
第二章 文獻探討 6
2.1信用評分 6
2.1.1信用評分的歷史和機構 6
2.1.2 信用申請流程 8
2.1.3 信用評分及行為評分 9
2.2貝氏模型 11
2.3分類迴歸樹 13
2.4鑑別分析 14
2.5羅吉斯迴歸 15
第三章 研究方法 17
3.1層級貝氏行為評分模式 18
3.2 CART-base信用評分模式 20
第四章 實證分析 23
4.1 資料描述 23
4.1.1人口統計變數 23
4.1.2信用活動資訊 25
4.1.3授信程度 26
4.2 層級貝氏行為評分模式估計結果 27
4.3 CART-base 信用評分模式估計結果 34
4.4顧客價值預測與模式比較 37
4.4.1鑑別能力 38
4.4.1.1資料比例不相同 38
4.4.1.2資料比例相同 38
4.4.2誤判成本 39
第五章 結論與建議 40
5.1研究結論 40
5.2未來研究方向 41
參考文獻 42
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論文全文使用權限:同意授權於2010-07-28起公開