Application of adaptive support vector machines method in credit scoring

Credit scoring has attracted lots of research interests in the literature. The credit scoring manager often evaluates the consumer's credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant's credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This article introduces support vector machines (SVM), to the problem in attempt to provide a model with better explanatory power. We used backpropagation neural network (BNN) as a benchmark and obtained prediction accuracy around 80% for both BNN and SVM methods for the Australian and German credit datasets from UCI.

[1]  Kyung-shik Shin,et al.  An application of support vector machines in bankruptcy prediction model , 2005, Expert Syst. Appl..

[2]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[3]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[4]  Ralf Stecking,et al.  Support vector machines for classifying and describing credit applicants: detecting typical and critical regions , 2005, J. Oper. Res. Soc..

[5]  Hyun-Chul Kim,et al.  Constructing support vector machine ensemble , 2003, Pattern Recognit..

[6]  Zhi-HuaZhou,et al.  Adapt Bagging to Nearest Neighbor Classifiers , 2005 .

[7]  David West,et al.  Neural network credit scoring models , 2000, Comput. Oper. Res..

[8]  Francis Eng Hock Tay,et al.  Modified support vector machines in financial time series forecasting , 2002, Neurocomputing.

[9]  Sheng-De Wang,et al.  Fuzzy support vector machines , 2002, IEEE Trans. Neural Networks.

[10]  Fernando José Von Zuben,et al.  Hybridizing mixtures of experts with support vector machines: Investigation into nonlinear dynamic systems identification , 2007, Inf. Sci..

[11]  Kin Keung Lai,et al.  Credit risk assessment with a multistage neural network ensemble learning approach , 2008, Expert Syst. Appl..

[12]  Kin Keung Lai,et al.  A new fuzzy support vector machine to evaluate credit risk , 2005, IEEE Transactions on Fuzzy Systems.

[13]  Hewijin Christine Jiau,et al.  Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem , 2006 .

[14]  Young-Chan Lee,et al.  Application of support vector machines to corporate credit rating prediction , 2007, Expert Syst. Appl..

[15]  R. Malhotra,et al.  Evaluating Consumer Loans Using Neural Networks , 2001 .

[16]  Peter E. Kennedy,et al.  Combining Qualitative Forecasts Using Logit , 1998 .

[17]  Tai-Yue Wang,et al.  Fuzzy support vector machine for multi-class text categorization , 2007, Inf. Process. Manag..

[18]  Yan-Shi Dong,et al.  A comparison of several ensemble methods for text categorization , 2004, IEEE International Conference onServices Computing, 2004. (SCC 2004). Proceedings. 2004.

[19]  Gert Cauwenberghs,et al.  Incremental and Decremental Support Vector Machine Learning , 2000, NIPS.

[20]  Johan A. K. Suykens,et al.  Benchmarking state-of-the-art classification algorithms for credit scoring , 2003, J. Oper. Res. Soc..

[21]  Maggi Kelly,et al.  Support vector machines for predicting distribution of Sudden Oak Death in California , 2005 .

[22]  Johan A. K. Suykens,et al.  Weighted least squares support vector machines: robustness and sparse approximation , 2002, Neurocomputing.