Abstract—Credit Scoring studies are very important for any financial house. Both traditional statistical and modern data mining/machine learning tools have been evaluated in the credit scoring problem. But very few of the studies facilitate the comparison of majority of the commonly employed tools in single comprehensive study. All the tools such as LDA (Linear Discriminant Analysis), SVM (support vector machines), Kernel density estimation, LR (logistic regression), GP(genetic programming), K neighborhood, which are available in SAS enterprise miner 6.2. The results revealed that support vector machine and genetic programming are superior tools for the purpose of classifying the loan applicant as their misclassification rates were least as compared to others.
Index Terms—Credit risk, credit scoring, machine learning, predictive modeling
Authors are with Department of Computer Science & Engineering, Thapar University, Patiala –147004 India (email: ravinder1bhatia@gmail.com, raggarwal@thapar.edu).
Cite: Ravinder Singh and Rinkle Rani Aggarwal, "Comparative Evaluation of Predictive Modeling Techniques on Credit Card Data," International Journal of Computer Theory and Engineering vol. 3, no. 5, pp. 598-603, 2011.
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