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Kredi Kartı Taleplerinin Değerlendirilmesinde Grup ve Bireysel Kredi Puanlama Modellerinin Karşılaştırılmalı Bir Analizi

Year 2010, Volume: 4 Issue: 1, 75 - 90, 01.06.2010

References

  • Bodur, C. ve Teker, S.. (2005). Credit Scoring of Companies: Application to the ISEM Companies.İTÜ Dergisi/b, 2(1): 25-36.
  • Breiman, L., Friedman, J. H., Olshen, R. A. ve Stone, C. J.. (1984). Classificati- on and Regression Trees. Wadsworth and Brooks/Cole, Montery.
  • Breiman, L.. (1996). Bagging Predictors. Machine Learning, 24 (3):123-140.
  • Chang, C. L. ve Chen, C. H.. (2008). Applying Decision Tree and Neural Net- work to Increase Quality of Dermatologic Diagnosis. Expert Systems with App- lications, 3(6): 4035-4041.
  • Chen, M. S., Han, J. ve Yu, P. S.. (1996). Data Mining: An Overview From a Database Perspective. IEEE Trans. Knowledge Data Engineering, 8(6): 866–883.
  • Chen, S. Y. ve Liu, X.. (2004). The Contribution of Data Mining to Informati- on Science. Journal of Information Science, 30(6): 550-558.
  • Çinko, M.. (2006). Kredi Kartı Değerlendirme Tekniklerinin Karflılafltırılması. İs- tanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 5 (9): 143-153.
  • Crook, J. ve Banasik, J.. (2004). Does Reject Inference Really Improve the Per- formance of Application Scoring Models? Journal of Banking and Finance, 28: 857-874.
  • ki, R., Newton, J., Parzen, E. ve Winkler, R.. (1982). The Accuracy of Extrapola
  • tion (time series) Methods: Results of a Forecasting Competition. Journal of Fo
  • recasting, 1 (2): 111-153.
  • Malhotra, R. ve Malhotra, D. K.. (2002). Differentiating Between Good Credits and Bad Credits Using Neuro-Fuzzy Systems. European Journal of Operational Research, 136(1): 190–211.
  • Martens, D., Baesens, B., Van Gestel, T. ve Vanthienen, J.. (2007). Comprehen- sible Credit Scoring Models Using Rule Extraction From Support Vector Machi- nes. European Journal of Operational Research, 183(3): 1466 - 1476.
  • Olmeda, I. ve Fernandez, E.. (1997). Hybrid Classifiers for Financial Multicriteri- a Decision Making: The Case of Bankruptcy Prediction. Computational Econo- mics, 10: 317–335.
  • Ong, C. S., Huang, J. J. ve Tzeng, G. H.. (2005). Building Credit Scoring Models Using Genetic Programming.Expert Systems with Applications, 29(1): 41–47.
  • Oza, N. C.. (2006). Ensemble Data Mining Methods, Encyclopedia of Data Wa- rehousing and Mining. Idea Group Reference, pp.448-452.
  • Quinlan, J. R.. (1993). C4.5: Programs for machine learning. Morgan Kaufman, San Francisco, CA.
  • Pal, M.. (2007). Ensemble Learning with Decision Tree for Remote Sensing Clas- sification. Proceedings of World Academy of Science Engineering and Techno- logy, 26(December): 735-737.
  • Pelikan, E., De Groot, C. ve Wurtz, D.. (1992). Power Consumption in West-Bo- hemia: Improved Forecasts Decorrelating Connectionist Networks. Neural Net- work World, No.2, 701-712.
  • Perrone, M. P. ve Cooper, L. N. (1993). When Networks Disagree: Ensemble Methods for Hybrid Neural Netwoks. Neural Networks for speech and Image Processing, Chapman Hall, 126–142.
  • Schapire, R. E.. (1990). The Strength of Weak Learnability. Machine Learning, 5(2): 197-227.
  • Shen, A., Tong, R. ve Deng, Y.. (2007). Application of Classification Models on Credit Card Fraud Detection. School of Management, Graduate University of the Chinese Academy of Sciences, China, 1-4
  • Seval, B.. (1990). Kredilendirme Süreci ve Kredi Yönetimi. İ.Ü. İflletme Fakülte- si, Muhasebe Enstitüsü Yayın No.59: İstanbul.
  • Thomas, L. C.. (2000). A Survey of Credit and Behavioral Scoring: Forecasting Financial Risk of Lending to Consumers.International Journal of Forecasting, 16: 149–172.
  • Tsai, C. F. ve Wu, J. W.. (2008). Using Neural Network Ensembles for Ban- kruptcy Prediction and Credit Scoring. Expert Systems with Applications, 34(4): 2639–2649.
  • Tso, K. F. G. ve Yau, K. K. W.. (2007). Predicting Electricity Energy Consump- tion: A Comparison of Regression Analysis, Decision Tree and Neural Networks. Energy, 32: 1761–1768.
  • Vellido, A., Lisboa, P. J. G. ve Vaughan, J.. (1999). Neural Networks in Business: A Survey of Applications (1992–1998). Expert Systems with Applications, 17: 51–70.
  • Vojtek, M. ve Koãenda, E.. (2006). Credit Scoring Methods. Finance a şvûr- Czech Journal of Economics and Finance, 56: 152-167.
  • West, D.. (2000). Neural Network Credit Scoring Models. Computers and Ope- rational Research, 27: 1131–1152.
  • West, D., Dellana, S. ve Qian, J.. (2005). Neural Network Ensemble Strategi- es for Financial Decision Applications. Computers & Operations Research, 32: 2543–2559.
  • Yang, Y.. (2007). Adaptive Credit Scoring with Kernel Learning Methods. Euro- pean Journal of Operational Research, 183(3): 1521-1536.
  • Yu, L., Wang, S. Y. ve Lai, K. K.. (2005). A Novel Non-Linear Ensemble Forecas- ting Model Incorporating GLAR and ANN for Foreign Exchange Rates. Compu- ters and Operations Research, 32 (10): 2523–2541.
  • Zhao, H.. (2007). A Multi-Objective Genetic Programming Approach to Develo- ping Pareto Optimal Decision Trees. Decision Support Systems, 43: 809–826.
  • Zhou, Z. H., Wu, J. ve Tang, W.. (2002). Ensembling Neural Networks: Many Could be Better Than All. Artificial Intelligence,113377 (1-2):239-263.

Abstract - A Comparative Analysis of Individual and Ensemble Credit Scoring Techniques in Evaluating Credit Card Loan Applications

Year 2010, Volume: 4 Issue: 1, 75 - 90, 01.06.2010

References

  • Bodur, C. ve Teker, S.. (2005). Credit Scoring of Companies: Application to the ISEM Companies.İTÜ Dergisi/b, 2(1): 25-36.
  • Breiman, L., Friedman, J. H., Olshen, R. A. ve Stone, C. J.. (1984). Classificati- on and Regression Trees. Wadsworth and Brooks/Cole, Montery.
  • Breiman, L.. (1996). Bagging Predictors. Machine Learning, 24 (3):123-140.
  • Chang, C. L. ve Chen, C. H.. (2008). Applying Decision Tree and Neural Net- work to Increase Quality of Dermatologic Diagnosis. Expert Systems with App- lications, 3(6): 4035-4041.
  • Chen, M. S., Han, J. ve Yu, P. S.. (1996). Data Mining: An Overview From a Database Perspective. IEEE Trans. Knowledge Data Engineering, 8(6): 866–883.
  • Chen, S. Y. ve Liu, X.. (2004). The Contribution of Data Mining to Informati- on Science. Journal of Information Science, 30(6): 550-558.
  • Çinko, M.. (2006). Kredi Kartı Değerlendirme Tekniklerinin Karflılafltırılması. İs- tanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 5 (9): 143-153.
  • Crook, J. ve Banasik, J.. (2004). Does Reject Inference Really Improve the Per- formance of Application Scoring Models? Journal of Banking and Finance, 28: 857-874.
  • ki, R., Newton, J., Parzen, E. ve Winkler, R.. (1982). The Accuracy of Extrapola
  • tion (time series) Methods: Results of a Forecasting Competition. Journal of Fo
  • recasting, 1 (2): 111-153.
  • Malhotra, R. ve Malhotra, D. K.. (2002). Differentiating Between Good Credits and Bad Credits Using Neuro-Fuzzy Systems. European Journal of Operational Research, 136(1): 190–211.
  • Martens, D., Baesens, B., Van Gestel, T. ve Vanthienen, J.. (2007). Comprehen- sible Credit Scoring Models Using Rule Extraction From Support Vector Machi- nes. European Journal of Operational Research, 183(3): 1466 - 1476.
  • Olmeda, I. ve Fernandez, E.. (1997). Hybrid Classifiers for Financial Multicriteri- a Decision Making: The Case of Bankruptcy Prediction. Computational Econo- mics, 10: 317–335.
  • Ong, C. S., Huang, J. J. ve Tzeng, G. H.. (2005). Building Credit Scoring Models Using Genetic Programming.Expert Systems with Applications, 29(1): 41–47.
  • Oza, N. C.. (2006). Ensemble Data Mining Methods, Encyclopedia of Data Wa- rehousing and Mining. Idea Group Reference, pp.448-452.
  • Quinlan, J. R.. (1993). C4.5: Programs for machine learning. Morgan Kaufman, San Francisco, CA.
  • Pal, M.. (2007). Ensemble Learning with Decision Tree for Remote Sensing Clas- sification. Proceedings of World Academy of Science Engineering and Techno- logy, 26(December): 735-737.
  • Pelikan, E., De Groot, C. ve Wurtz, D.. (1992). Power Consumption in West-Bo- hemia: Improved Forecasts Decorrelating Connectionist Networks. Neural Net- work World, No.2, 701-712.
  • Perrone, M. P. ve Cooper, L. N. (1993). When Networks Disagree: Ensemble Methods for Hybrid Neural Netwoks. Neural Networks for speech and Image Processing, Chapman Hall, 126–142.
  • Schapire, R. E.. (1990). The Strength of Weak Learnability. Machine Learning, 5(2): 197-227.
  • Shen, A., Tong, R. ve Deng, Y.. (2007). Application of Classification Models on Credit Card Fraud Detection. School of Management, Graduate University of the Chinese Academy of Sciences, China, 1-4
  • Seval, B.. (1990). Kredilendirme Süreci ve Kredi Yönetimi. İ.Ü. İflletme Fakülte- si, Muhasebe Enstitüsü Yayın No.59: İstanbul.
  • Thomas, L. C.. (2000). A Survey of Credit and Behavioral Scoring: Forecasting Financial Risk of Lending to Consumers.International Journal of Forecasting, 16: 149–172.
  • Tsai, C. F. ve Wu, J. W.. (2008). Using Neural Network Ensembles for Ban- kruptcy Prediction and Credit Scoring. Expert Systems with Applications, 34(4): 2639–2649.
  • Tso, K. F. G. ve Yau, K. K. W.. (2007). Predicting Electricity Energy Consump- tion: A Comparison of Regression Analysis, Decision Tree and Neural Networks. Energy, 32: 1761–1768.
  • Vellido, A., Lisboa, P. J. G. ve Vaughan, J.. (1999). Neural Networks in Business: A Survey of Applications (1992–1998). Expert Systems with Applications, 17: 51–70.
  • Vojtek, M. ve Koãenda, E.. (2006). Credit Scoring Methods. Finance a şvûr- Czech Journal of Economics and Finance, 56: 152-167.
  • West, D.. (2000). Neural Network Credit Scoring Models. Computers and Ope- rational Research, 27: 1131–1152.
  • West, D., Dellana, S. ve Qian, J.. (2005). Neural Network Ensemble Strategi- es for Financial Decision Applications. Computers & Operations Research, 32: 2543–2559.
  • Yang, Y.. (2007). Adaptive Credit Scoring with Kernel Learning Methods. Euro- pean Journal of Operational Research, 183(3): 1521-1536.
  • Yu, L., Wang, S. Y. ve Lai, K. K.. (2005). A Novel Non-Linear Ensemble Forecas- ting Model Incorporating GLAR and ANN for Foreign Exchange Rates. Compu- ters and Operations Research, 32 (10): 2523–2541.
  • Zhao, H.. (2007). A Multi-Objective Genetic Programming Approach to Develo- ping Pareto Optimal Decision Trees. Decision Support Systems, 43: 809–826.
  • Zhou, Z. H., Wu, J. ve Tang, W.. (2002). Ensembling Neural Networks: Many Could be Better Than All. Artificial Intelligence,113377 (1-2):239-263.
There are 34 citations in total.

Details

Primary Language Turkish
Journal Section Research Article
Authors

Hüseyin İnce This is me

Bora Aktan This is me

Publication Date June 1, 2010
Published in Issue Year 2010 Volume: 4 Issue: 1

Cite

APA İnce, H., & Aktan, B. (2010). Kredi Kartı Taleplerinin Değerlendirilmesinde Grup ve Bireysel Kredi Puanlama Modellerinin Karşılaştırılmalı Bir Analizi. BDDK Bankacılık Ve Finansal Piyasalar Dergisi, 4(1), 75-90.