Publications-Theses
Article View/Open
Publication Export
-
題名 如何藉由信用評分模型協助融資/租賃公司車貸業務授信管理 – 以某租賃公司為例
How Can a Credit Scoring Model Help Auto Loan Business in Finance/Leasing Companies – A Case Study of T-Leasing Company作者 梁芫銘
Liang, Roy Yuan-Ming貢獻者 吳文傑
Wu, Jack
梁芫銘
Liang, Roy Yuan-Ming關鍵詞 信用評分
汽車信貸
邏輯迴歸
金融/租賃公司
Credit Scoring
Auto Loan
Logistic Regression
Finance/Leasing Companies日期 2020 上傳時間 3-Aug-2020 17:45:31 (UTC+8) 摘要 The aim of this study attempts to introduce how a credit scoring model can be used to help finance/leasing companies manage and reinforce their auto loan approval process. Credit scoring models are most frequently applied methods adopted by banks when evaluating loan applications. This is a data-driven approach and it is built by using historical data and statistical techniques to identify the possibility that the failure by borrowers to make repayments in accordance with the term of a loan. Finance/leasing companies focus on the lending business in high risk borrowers. Default risk is what finance/leasing companies have to be concerned about.In this study, a real data of auto loan product was used to build a credit scoring model. The logistic regression is the most common method to establish the model and the model validation was also conducted to prove the effectiveness. The paper presents a credit scoring model that provides finance/leasing companies an objective way to measure and manage the credit risk. The model has also identified the risk factors associated with measuring the credit risk. This thesis illustrated how the credit scoring model works and it is recommended financing/leasing companies should develop their own credit scoring model. 參考文獻 1. Crook, J.N., Edelman, D.B., Thomas, L.C. (2002). Credit Scoring and its Applications. Philadelphia: Siam2. Finlay, S. (2012). Credit Scoring, Response Modeling, and Insurance Rating: A Practical Guide to Forecasting Consumer Behavior (2nd edition). Hampshire UK: Palgrave Macmillan3. Guo, J.J., Meng, J.W., Shan, L., Wang, H.S. (2010).12 Lessons for Credit Rating Models. Taiwan: Taiwan Academy of Banking and Finance4. Hosmer Jr., D.W., Lemeshow, S., Sturdivant, R.X. (2013). Applied Logistic Regression (3rd edition). New Jersey:John Wiley & Sons, Inc.5. Mays, E. (2001). Handbook of Credit Scoring. Chicago: Glenlake Publishing Company6. Olson, D.L., Wu, D.D. (2008). Enterprise Risk Management. Canada: World Scientific Publishing Co. Pte. Ltd7. Shu, X. (2020). Knowledge Discovery in the Social Science: a Data Mining Approach. Oakland California: University of California8. Siddiqi, N. (2006). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. Hoboken, New Jersy: John Wiley & Sons, Inc.9. Bank for International Settlements (2005), Working Paper No. 14 Studies on the Validation of Internal Rating Systems. Retrieved from: https://www.bis.org/publ/bcbs_wp14.pdf (access March 15th, 2020)10. Bank for International Settlements (2017), Basel III: Finalising post-crisis reforms. Retrieved from: https://www.bis.org/bcbs/publ/d424.htm (access March 7th, 2020)11. Chen, G.H., Chu, M.J., Huang, J.C. (2011). A research on credit rating model of leasing industry. (Master’s thesis, Ming Chuan University, Taipei, Taiwan) Retrieved from: http://oplab.im.ntu.edu.tw/csimweb/system/application/views/files/ICIM/2011008212. Financial Supervisory Commission, R.O.C. Taiwan (2020). How to enhance the function of Consumer Debt Relief Program and the Regulation Governing for the Improvement of Banking Business of Credit Card and Cash Card. Retrieved from: https://law.fsc.gov.tw/law/LawContent.aspx?id=FE057275&KeyWord=%e6%b6%88%e8%b2%bb%e9%87%91%e8%9e%8d%e5%82%b5%e5%8b%99%e5%8d%94%e5%95%86%e6%a9%9f%e5%88%b6 (accessed June 16th, 2020)13. Banking Bureau (2020). Banking Statistics. Retrieved from: https://www.banking.gov.tw/ch/home.jsp?id=595&parentpath=0,590 (accessed June 10th, 2020)14. Chailease Holding (2019). Annual Report 2018-2019。Retrieved from: http://www.chaileaseholding.com/ImgChaileaseHolding/20200519183251.pdf , (accessed April 10th, 2020)15. Ding, J.J. (2004).Study of Consumer Credit Risk - Introduction to Credit Scoring. Retrieved from: https://member.jcic.org.tw/main_member/fileRename.aspx?fid=298&kid=1 (accessed June 10th, 2020)16. Lai, B.Z. (2006). The Current Development Status of Consumer Credit Scoring System. Retrieved from: https://member.jcic.org.tw/main_member/fileRename.aspx?fid=378&kid=1 (accessed June 10th, 2020)17. Lai, B.Z., Tsai, R.M. (August, 2009). Consumer Credit Scoring System Product (J10) the Use in the Approval Process of Credit Card. Retrieved from: https://www.google.com/url?client=internal-element-cse&cx=010192925252739662080:8bojmtikgsc&q=https://www.jcic.org.tw/main_ch/fileRename/fileRename.aspx%3Ffid%3D631%26kid%3D1&sa=U&ved=2ahUKEwjO-J7F2_boAhUpxYsBHX_1CJI4FBAWMAV6BAgFEAI&usg=AOvVaw33ZTMH_-AyymIcZrTQJwDr (accessed March 20th, 2020)18. Liu, M.H., Wu, C.L. (July 24th, 2015). Reach an agreement of giving the permission to the financing and leasing companies of non-JCIC members to apply for credit reports on behalf of consumers. Retrieved from: https://www.ndc.gov.tw/News_Content.aspx?n=114AAE178CD95D4C&sms=DF717169EA26F1A3&s=15214408D3578A59 (accessed December 10th, 2019) 描述 碩士
國立政治大學
國際經營管理英語碩士學位學程(IMBA)
102933014資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102933014 資料類型 thesis dc.contributor.advisor 吳文傑 zh_TW dc.contributor.advisor Wu, Jack en_US dc.contributor.author (Authors) 梁芫銘 zh_TW dc.contributor.author (Authors) Liang, Roy Yuan-Ming en_US dc.creator (作者) 梁芫銘 zh_TW dc.creator (作者) Liang, Roy Yuan-Ming en_US dc.date (日期) 2020 en_US dc.date.accessioned 3-Aug-2020 17:45:31 (UTC+8) - dc.date.available 3-Aug-2020 17:45:31 (UTC+8) - dc.date.issued (上傳時間) 3-Aug-2020 17:45:31 (UTC+8) - dc.identifier (Other Identifiers) G0102933014 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131033 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 國際經營管理英語碩士學位學程(IMBA) zh_TW dc.description (描述) 102933014 zh_TW dc.description.abstract (摘要) The aim of this study attempts to introduce how a credit scoring model can be used to help finance/leasing companies manage and reinforce their auto loan approval process. Credit scoring models are most frequently applied methods adopted by banks when evaluating loan applications. This is a data-driven approach and it is built by using historical data and statistical techniques to identify the possibility that the failure by borrowers to make repayments in accordance with the term of a loan. Finance/leasing companies focus on the lending business in high risk borrowers. Default risk is what finance/leasing companies have to be concerned about.In this study, a real data of auto loan product was used to build a credit scoring model. The logistic regression is the most common method to establish the model and the model validation was also conducted to prove the effectiveness. The paper presents a credit scoring model that provides finance/leasing companies an objective way to measure and manage the credit risk. The model has also identified the risk factors associated with measuring the credit risk. This thesis illustrated how the credit scoring model works and it is recommended financing/leasing companies should develop their own credit scoring model. en_US dc.description.tableofcontents 1. Introduction 11.1. The Business Growth of Auto Loan in Finance/Leasing Companies 11.2. Research Motivation 21.3. Research Objectives 61.4. Research Structure 62. Literature Review 82.1. How the Banks Reduce the Credit Risk 82.2. The Statistical Technique to Establish a Credit Rating System 93. A Case of Leasing Company 103.1. Market Segmentation 103.2. Common Practice of Running Auto Loan Business and the Difficulties 113.3. Company SWOT Analysis 134. Research Methodology 174.1. Model Design 174.2. Model Development Process 184.2.1. Data Preparation 184.2.2. Data Analysis 194.2.3. Model Development 234.2.4. Model Validation 355. Implementation and Use 415.1. The Proposed Framework 415.2. Cost-Benefit Analysis 426. Research Conclusion 44References 46Appendix 49 zh_TW dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102933014 en_US dc.subject (關鍵詞) 信用評分 zh_TW dc.subject (關鍵詞) 汽車信貸 zh_TW dc.subject (關鍵詞) 邏輯迴歸 zh_TW dc.subject (關鍵詞) 金融/租賃公司 zh_TW dc.subject (關鍵詞) Credit Scoring en_US dc.subject (關鍵詞) Auto Loan en_US dc.subject (關鍵詞) Logistic Regression en_US dc.subject (關鍵詞) Finance/Leasing Companies en_US dc.title (題名) 如何藉由信用評分模型協助融資/租賃公司車貸業務授信管理 – 以某租賃公司為例 zh_TW dc.title (題名) How Can a Credit Scoring Model Help Auto Loan Business in Finance/Leasing Companies – A Case Study of T-Leasing Company en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1. Crook, J.N., Edelman, D.B., Thomas, L.C. (2002). Credit Scoring and its Applications. Philadelphia: Siam2. Finlay, S. (2012). Credit Scoring, Response Modeling, and Insurance Rating: A Practical Guide to Forecasting Consumer Behavior (2nd edition). Hampshire UK: Palgrave Macmillan3. Guo, J.J., Meng, J.W., Shan, L., Wang, H.S. (2010).12 Lessons for Credit Rating Models. Taiwan: Taiwan Academy of Banking and Finance4. Hosmer Jr., D.W., Lemeshow, S., Sturdivant, R.X. (2013). Applied Logistic Regression (3rd edition). New Jersey:John Wiley & Sons, Inc.5. Mays, E. (2001). Handbook of Credit Scoring. Chicago: Glenlake Publishing Company6. Olson, D.L., Wu, D.D. (2008). Enterprise Risk Management. Canada: World Scientific Publishing Co. Pte. Ltd7. Shu, X. (2020). Knowledge Discovery in the Social Science: a Data Mining Approach. Oakland California: University of California8. Siddiqi, N. (2006). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. Hoboken, New Jersy: John Wiley & Sons, Inc.9. Bank for International Settlements (2005), Working Paper No. 14 Studies on the Validation of Internal Rating Systems. Retrieved from: https://www.bis.org/publ/bcbs_wp14.pdf (access March 15th, 2020)10. Bank for International Settlements (2017), Basel III: Finalising post-crisis reforms. Retrieved from: https://www.bis.org/bcbs/publ/d424.htm (access March 7th, 2020)11. Chen, G.H., Chu, M.J., Huang, J.C. (2011). A research on credit rating model of leasing industry. (Master’s thesis, Ming Chuan University, Taipei, Taiwan) Retrieved from: http://oplab.im.ntu.edu.tw/csimweb/system/application/views/files/ICIM/2011008212. Financial Supervisory Commission, R.O.C. Taiwan (2020). How to enhance the function of Consumer Debt Relief Program and the Regulation Governing for the Improvement of Banking Business of Credit Card and Cash Card. Retrieved from: https://law.fsc.gov.tw/law/LawContent.aspx?id=FE057275&KeyWord=%e6%b6%88%e8%b2%bb%e9%87%91%e8%9e%8d%e5%82%b5%e5%8b%99%e5%8d%94%e5%95%86%e6%a9%9f%e5%88%b6 (accessed June 16th, 2020)13. Banking Bureau (2020). Banking Statistics. Retrieved from: https://www.banking.gov.tw/ch/home.jsp?id=595&parentpath=0,590 (accessed June 10th, 2020)14. Chailease Holding (2019). Annual Report 2018-2019。Retrieved from: http://www.chaileaseholding.com/ImgChaileaseHolding/20200519183251.pdf , (accessed April 10th, 2020)15. Ding, J.J. (2004).Study of Consumer Credit Risk - Introduction to Credit Scoring. Retrieved from: https://member.jcic.org.tw/main_member/fileRename.aspx?fid=298&kid=1 (accessed June 10th, 2020)16. Lai, B.Z. (2006). The Current Development Status of Consumer Credit Scoring System. Retrieved from: https://member.jcic.org.tw/main_member/fileRename.aspx?fid=378&kid=1 (accessed June 10th, 2020)17. Lai, B.Z., Tsai, R.M. (August, 2009). Consumer Credit Scoring System Product (J10) the Use in the Approval Process of Credit Card. Retrieved from: https://www.google.com/url?client=internal-element-cse&cx=010192925252739662080:8bojmtikgsc&q=https://www.jcic.org.tw/main_ch/fileRename/fileRename.aspx%3Ffid%3D631%26kid%3D1&sa=U&ved=2ahUKEwjO-J7F2_boAhUpxYsBHX_1CJI4FBAWMAV6BAgFEAI&usg=AOvVaw33ZTMH_-AyymIcZrTQJwDr (accessed March 20th, 2020)18. Liu, M.H., Wu, C.L. (July 24th, 2015). Reach an agreement of giving the permission to the financing and leasing companies of non-JCIC members to apply for credit reports on behalf of consumers. Retrieved from: https://www.ndc.gov.tw/News_Content.aspx?n=114AAE178CD95D4C&sms=DF717169EA26F1A3&s=15214408D3578A59 (accessed December 10th, 2019) zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202000865 en_US
