臺灣博碩士論文加值系統

傳統的信用風險評估模型未考慮放款是一個隨時間進行的過程，僅關心借款者未來是否會違約，而非借款者何時會違約，這樣的判別結果不能提供管理者做出獲利最大化的決策。因為即使借款者違約，在某些情境之下金融機構依然可以從中獲利。目前的研究主流是使用 Cox 模式存活期模型建立時間信用風險評估模型，預測借款者違約的時點，以解決前述決策無法獲利最大化之問題。然而影響違約的因子眾多且複雜，目前的文獻對於哪些因子會影響違約發生的機率、以及如何影響，並沒有共識，Cox模式的預測結果因此並不夠準確。本研究提出一個以決策樹為基礎的時間信用風險評估模型，有別於以往預估每一筆案件違約時點的方式，本研究使用決策樹直接篩選出可能會在短期內違約的案件 (Short-term default)，目標是以高準確率找出會在短期內違約的高風險案件，這些高風險案件會給金融機構帶來巨大損失。為了改善決策樹不穩定，和放款資料呈現高度不對稱的情形對決策樹判別準確率的影響，本論文之信用風險模型還結合拔靴集成法 (Bootstrap aggregating, Bagging) 和增生少數類別技術 (Synthetic minority over-sampling technique, SMOTE)，用以提昇決策樹的判別能力。套用國內某金融機構的中小企業放款資料顯示，本研究所提出之風險評估模型，在判別高風險案件的準確率和精確率之上，都明顯優於現在廣泛使用的羅吉斯迴歸和Cox模式。

Traditional credit scoring models do not put time factor into consideration, only assess whether a customer will default, but not when. However, when making profit maximum decisions, managers of financial institutions can hardly rely on this kind of model. That is because even if a customer defaulted in the future, there is still possibility that the financial institution could gain profit from issuing the loan. Most of recent researches applied Cox proportional hazard model into their credit scoring models, predicting the time when a customer is most likely to default, to solve this problem. Nevertheless, the prediction provided by Cox proportional hazard model is not accurate enough. It is because there are vast amount of factors contribute to the timing of default, in a perplexing way. Today, there is no consensus among researchers on which indicators, and by which means, influences the probability of default. This thesis develops a decision tree based credit risk model. Unlike conventional approach, which gives individual duration, we filter out borrowers who tend to default in short-term, i.e., short-term default loans. These loans post the highest risk of significant loss to a financial institution. Our goal is to produce a highly accurate model which could distinguish high risk lending from others. This research integrate Bootstrap aggregating, or Bagging, and Synthetic minority over-sampling technique, otherwise known as SMOTE into the credit risk model, to improve the decision tree stability and its performance on unbalanced data respectively. Empirical result on real world small-and-median-enterprise loan data, provided by a local financial institution, shows that our credit risk model has good recall and precision on classifying high risk customers, out performs popular methods namely logistic regression and Cox proportional hazard models.

中文摘要 I
英文摘要 II
目錄 III
表目錄 IV
圖目錄 V
第一章　緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 3
1.4 研究方法 4
1.5 研究架構 4
第二章　文獻探討 5
2.1 信用風險評估模型 5
2.2 時間信用風險評估模型 7
2.3 C4.5決策樹與拔靴集成法 8
2.4 增生少數類別技術 10
第三章　研究方法 13
3.1模型建立 13
第四章　實例驗證 16
4.1 驗證方法 16
4.2 資料來源 17
4.3 判別結果 20
4.4 變數分析 21
第五章　結論 23
參考文獻 24

Akbani, R., & Kwek, S. (2004). Applying support vector machines to imbalanced datasets. Machine Learning: ECML 2004 (pp. 39-50). Berlin, Germany. Retrieved from http://www.springerlink.com/index/PA57EAM5T5DKEM4H.pdf
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589. JSTOR. doi:10.2307/2978933
Arminger, G., Enache, D., & Bonne, T. (1997). Analyzing credit risk data: a comparison of logistic discrimination, classification tree analysis, and feedforward neural networks. Computational Statistics, 12(2), 293-310. Retrieved from citeulike-article-id:648863
Atiya, A. F. (2001). Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE transactions on neural networks, 12(4), 929-35. doi:10.1109/72.935101
Baesens, B., Van Gestel, T., Stepanova, M., Van den Poel, D., & Vanthienen, J. (2005). Neural network survival analysis for personal loan data. Journal of the Operational Research Society, 56(9), 1089-1098. doi:10.1057/palgrave.jors.2601990
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society, 54(6), 627-635. doi:10.1057/palgrave.jors.2601545
Banasik, J., Crook, J. N., & Thomas, L. C. (1999). Not if but when will borrowers default. Journal of the Operational Research Society, 50(12), 1185-1190. doi:10.1057/palgrave.jors.2600851
Berger, A. N., Frame, W. S., & Miller, N. H. (2005). Credit scoring and the availability, price, and risk of small business credit. Journal of Money, Credit and Banking, 37(2), 191-222.
Bernhardsen, E. (2001). A model of bankruptcy prediction. Citeseer. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.139.6764&rep=rep1&type=pdf
Bonfim, D. (2009). Credit risk drivers: evaluating the contribution of firm level information and of macroeconomic dynamics. Journal of Banking & Finance, 33(2), 281-299. Elsevier B.V. doi:10.1016/j.jbankfin.2008.08.006
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140. doi:10.1007/BF00058655
Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39(3), 3446-3453. Elsevier Ltd. doi:10.1016/j.eswa.2011.09.033
Chawla, N. V., Bowyer, K. W., & Hall, L. O. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357.
Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society. Series B (Methodological), 34(2), 187-220. Retrieved from http://www.jstor.org/stable/10.2307/2985181
DeYoung, R., Glennon, D., & Nigro, P. (2008). Borrower–lender distance, credit scoring, and loan performance: Evidence from informational-opaque small business borrowers☆. Journal of Financial Intermediation, 17(1), 113-143. doi:10.1016/j.jfi.2007.07.002
Domingos, P. (1999). Metacost: A general method for making classifiers cost-sensitive. Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 155–164). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=312220
Drummond, C., & Holte, R. C. (2003). C4.5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. Workshop on Learning from Imbalanced Datasets II (pp. 1-8). doi:10.1.1.68.6858
Duffie, D., Saita, L., & Wang, K. (2007). Multi-period corporate default prediction with stochastic covariates. Journal of Financial Economics, 83(3), 635-665. doi:10.1016/j.jfineco.2005.10.011
Dwyer, D. W., Kocagil, A. E., & Stein, R. M. (2004). The Moody’s KMV RiskCalc v3. 1 Model: Next-Generation Technology for Predicting Private Firm Credit Risk (pp. 1-36).
Eklund, T., Larsen, K., & Bernhardsen, E. (2001). Model for analysing credit risk in the enterprise sector. Norges Bank Economic Bulletin, Q3(01), 99–106.
Elandt-Johnson, R. C., & Johnson, N. L. (1999). Survival models and data analysis (p. 457). Wiley-Interscience. Retrieved from http://books.google.com/books?id=LCZsNo1ek6AC&pgis=1
Frame, W. S., & White, L. J. (2004). Empirical studies of financial innovation: lots of talk, little action? Journal of Economic Literature, 42(1), 116–144. JSTOR. Retrieved from http://www.jstor.org/stable/10.2307/3217038
Frame, W. S., Srinivasan, A., & Woosley, L. (2012). The effect of credit scoring or lending. Banking, 33(3), 813-825.
Gersbach, H., & Lipponer, A. (2003). Firm defaults and the correlation effect. European Financial Management, 9(3), 361-378. doi:10.1111/1468-036X.00225
Henderson, R., & Keiding, N. (2005). Individual survival time prediction using statistical models. Journal of medical ethics, 31(12), 703-6. doi:10.1136/jme.2005.012427
Huang, S.-chang. (2011). A new corporate credit scoring system using semi-supervised discriminant analysis. African Journal of Business Managenent, 5(22), 9355-9362.
Kocenda, E., & Vojtek, M. (2011). Default predictors in retail credit scoring: evidence from czech banking data. Emerging Markets Finance and Trade, 47(6), 80-98. doi:10.2753/REE1540-496X470605
Kumar, D. A., & Ravi, V. (2008). Predicting credit card customer churn in banks using data mining. International Journal of Data Analysis Techniques and Strategies, 1(1), 4. doi:10.1504/IJDATS.2008.020020
Li, Z., & Wang, W. (2011). A re-sampling method for class imbalance learning with credit data. Proceedings of the 2011 International Conference on Information Technology, Computer Engineering and Management Sciences (pp. 393-397). IEEE Computer Society, Los Alamitos, CA, USA. doi:10.1109/ICM.2011.34
Louzada, F., Anacleto-junior, O., Candolo, C., & Mazucheli, J. (2011). Expert systems with applications poly-bagging predictors for classification modelling for credit scoring. Expert Systems With Applications, 38(10), 12717-12720. Elsevier Ltd. doi:10.1016/j.eswa.2011.04.059
Merton, R. C. (1973). Theory of rational option pricing. The Bell Journal of Economics and Management Science, 4(1), 141. doi:10.2307/3003143
Mester, L. J. (1997). What’s the point of credit scoring? Federal Reserve Bank of Philadelphia Business Review, 3, 3–16. Federal Reserve Bank of Philadelphia.
Narain, B. (1992). Survival analysis and the credit granting decision. In L C Thomas, J. N. Crook, & D. B. Edelman (Eds.), Credit Scoring and Credit Control. Oxford: Clarendon Press. Retrieved from citeulike-article-id:651905
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81-106. doi:10.1007/BF00116251
Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. (M. B. Morgan, C. Leyba, & J. Hammett, Eds.) (1st ed., p. 302). San Mateo, CA: Morgan Kaufmann.
Roszbach, K. (2004). Bank lending policy, credit scoring, and the survival of loans. Review of Economics and Statistics, 86(4), 946-958. doi:10.1162/0034653043125248
Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business, 74(1), 101-124. The University of Chicago Press. doi:10.2139/ssrn.171436
Thomas, Lyn C. (2000). A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. International Journal of Forecasting, 16(2), 149-172. doi:10.1016/S0169-2070(00)00034-0
Tong, E. N. C., Mues, C., & Thomas, L. C. (2012). Mixture cure models in credit scoring: If and when borrowers default. European Journal of Operational Research, 218(1), 132-139. Elsevier B.V. doi:10.1016/j.ejor.2011.10.007
Tong, L.-I., Chang, Y.-C., & Lin, S.-H. (2011). Determining the optimal re-sampling strategy for a classification model with imbalanced data using design of experiments and response surface methodologies. Expert Systems with Applications, 38(4), 4222-4227. Elsevier Ltd. doi:10.1016/j.eswa.2010.09.087
Tudela, M., & Young, G. (2003). Predicting default among UK companies: a Merton approach. Bank of England Financial Stability Review, (June), 104-114. Citeseer. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.197.7163
Wang, G., Hao, J., & Ma, J. (2011). A comparative assessment of ensemble learning for credit scoring. Expert systems with applications, 38(1), 223-230. Elsevier Ltd. doi:10.1016/j.eswa.2010.06.048
Yobas, M. B., Crook, J. N., & Ross, P. (2000). Credit scoring using neural and evolutionary techniques. IMA Journal of Management Mathematics, 11(2), 111–125. IMA. Retrieved from http://imaman.oxfordjournals.org/content/11/2/111.short
Zhang, D., Zhou, X., Leung, S. C. H., & Zheng, J. (2010). Expert systems with applications vertical bagging decision trees model for credit scoring. Expert Systems With Applications, 37(12), 7838-7843. Elsevier Ltd. doi:10.1016/j.eswa.2010.04.054