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    政大典藏 > College of Commerce > Department of MIS > Theses >  Item 140.119/31115


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/31115


    Title: 消費性金融之個人信用因素分析—以小型信用貸款為例
    Analysis of the personal credit characteristic on comsumer banking – based on small-scale credit loan
    Authors: 彭世文
    Peng,Shih-Weng
    Contributors: 劉文卿
    曾淑峰
    Liou,Wen-Ching
    Tseng,Shu-Feng
    彭世文
    Peng,Shih-Weng
    Keywords: 小型信用貸款
    信用因素
    還款績效
    逾期風險
    small scale credit loan
    credit factors
    repay performance
    default risk
    Date: 2007
    Issue Date: 2009-09-14 09:16:54 (UTC+8)
    Abstract: 本研究以還款績效的觀點，分析小型信用貸款中申貸者的特性，讓銀行放款的依據除了判斷正常戶與否之外，進一步以還款績效與風險區分出不同群組的申貸者，以期作不同的放款策略；同時將個人基本變數 、該銀行內徵信資料以及聯合徵信資料變數 作統計性分類，篩選出代表性因素，研究這些因素如何影響各還款績效群組。
    研究發現，申貸者可以區分為「還款能力平穩—逾期風險低」、「還款能力優良—逾期風險中」、「還款能力低下—逾期風險高」這三群。而從影響各群組的因素中可以瞭解，「還款能力平穩—逾期風險低」群組，多為各方面信用持平良好的申貸者；「還款能力優良—逾期風險中」群組，多為具有理財管理特質、財務狀況良好的申貸者；「還款能力低下—逾期風險高」群組，多為具有債務因素、信用表現不佳、申貸動作頻繁的申貸者。
    在放款利潤與風險方面，對三個群組之申貸戶分別採用不同方法放款，可以作到讓銀行對較少申貸戶放款並且可提升利潤並且改善損失。進行多元羅吉斯迴歸模型分析可以發掘出具影響力的因素，針對這些因素來進行分群後並採差異化放款方法，也可以作到對較少申貸戶放款並且能提升利潤以及降低損失的效果。由於因素代表具解釋性變數的歸納，配合這些具預測機能的因素及變數分群訂定差異化授信政策，有助於防範風險於未然。
    This research analyses the characteristics of small-scale credit loan applicants on the persepective of repay performances，allowing the banks not only to discriminate
    between good and bad applicants but also to establish different lending tatics for applicants of different repay performance groups。We also analyse the personal characteristics and joint credit informantion of these applicants to sieve out the representative factors，and study how these factors affect the repay performance
    groups。
    Our research discovers that the applicants can be discriminanted into three groups：「low but steady repay ability—low overdue loss」、「good repay ability—
    acceptable overdue loss」、「very low repay ability—high overdue loss」。We can learn from those factors，that most applicants grouped as 「low but steady repay ability—
    low overdue loss」also have good credit qualities in other aspect；applicants grouped as 「good repay ability—acceptable overdue loss」 have finance management concept
    and good financial condition；applicants grouped as 「very low repay ability—high overdue loss」have debt burdens and bad credit qualities。
    As for the revenues and riks，we can improve the profit and loss with fewer applicants by taking differenct lending policies to those three groups。By using multinomial logistic regression，we can discover those factors who has significant effects and use these factors to cluster applicants into groups and to adopt different lending policies for these groups。Because those factors represent the induction of the variables which can explain the applicants’ behaviors，we can somehow prevent the risks by establishing different policies with the coordination of these factors and clusters。
    Reference: 1. 呂金品，民89，運用決策樹分類方法估計小額放款倒帳率-兼論放款策略，國立中山大學財務管理學系研究所碩士論文。
    2. 呂美慧，民89，金融機構房貸客戶授信評量模式分析－Logistic 迴歸之應用，國立政治大學金融研究所碩士論文。
    3. 何貴清，民90，消費者小額信用貸款之信用風險研究-以一商業銀行客戶為例，國立中山大學人力資源管理研究所。
    4. 林建州，民90，銀行個人消費信用貸款授信風險評估模式之研究，國立中山大學人力資源管理研究所碩士論文。
    5. 施孟隆、游清芳、李佳珍，1999『Logit 模式應用於信用卡信用風險審核系統之研究-以國內某銀行為例』，金融財務月刊，第四期：85~104頁。
    6. 許愛惠，民83，信用卡信用風險審核範例學習系統之研究，國立政治大學資訊管理研究所碩士論文。
    7. 陳宗豪，民88，消費者小額信用貸款之信用風險研究--以甄選的觀點，國立中山大學人力資源管理研究所碩士論文。
    8. 曾俊堯，民80，信用卡信用管理之研究，國立政治大學企業管理研究所碩士論文。
    9. 曾俊堯，1995『信用卡信用風險評估模式之研究』，中州學報，第十期：300-314頁。
    10. 黃秋凌，民82，信用卡管理問題之研究，國立政治大學會計研究所碩士論文。
    11. 黃隆憲，民95，消費者小額信用貸款授信模式之研究，高雄第一科技大學財務管理研究所碩士論文。
    12. 彭昭英，2002，SAS與統計分析，第12版，儒林書局。
    13. 蔡明憲，民91，金融機構消費信用貸款授信評量模式，國立中山大學財務管理學系碩士在職專班碩士論文。
    14. 儲蓉，1999『對發展信用評等應有的態度與做法』，經濟情勢暨評論季刊，第五卷，第一期：51~74頁。
    15. 簡安泰，民66，消費者信用評分制度之研究，國立政治大學企業管理研究所碩士論文。
    16. 龔昶元，1998『Logistic Regression模式應用於信用卡信用風險審核之研究』，台北銀行月刊，第二十八卷，第九期：35-49頁。
    17. Akaike, H. ”A new look at the statistical model identification”, IEEE Transactions on Automatic Control (19:6) 1974, pp:716–723.
    18. David Lawrence and Arlene Solomon Managing a Consumer Lending Business,64 Solomon Lawrence Partners, 2002
    19. Elizabeth Mays Credit Scoring for Risk Managers：The Handbook for Lenders,Thomson/South-Western, Mason, Ohio, 2004
    20. Kim, J.S.、Tokuhata, G.K. and Bratz, J.R. ”Comparison of Multivariate Regression Analysis Between Logistic Model and the Least Square Model Using SAS Software，SAS Users Group International Conference Proceedings 1985, pp:1109-1112.
    21. Marija, J. Norušis SPSS 15.0 Statistical Procedures Companion, Prentice Hall, 2006
    22. Nagelkerke N. J. D. “A note on a general definition of the coefficient of determination”, Biometrika (78) 1991, pp:691-692.
    23. Rock A. ”Sure Ways to Score With Lender”, Money. 1984
    24. Shepard D. The New Direct Marketing：How to Implement a profit-driven Database Marketing Strategy, McGraw-Hill, New York, 1999
    25. Ronald, E. Walpole、Raymond, H. Myers and Sharon, L.Myers Probability and Statistics for Engineers and Scientists, Prentice Hall, 1997
    26. Steenackers, A. and Goovaerts, M.J. ”A Credit Scoring Model for Personal Loans”, Insurance Mathematics Economics 1989, pp:31-34.
    Description: 碩士
    國立政治大學
    資訊管理研究所
    91356034
    96
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0913560341
    Data Type: thesis
    Appears in Collections:[Department of MIS] Theses

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