A Comparison of Different Soft Computing Models for Credit Scoring
It has become crucial over the years for nations to
improve their credit scoring methods and techniques in light of the
increasing volatility of the global economy. Statistical methods or
tools have been the favoured means for this; however artificial
intelligence or soft computing based techniques are becoming
increasingly preferred due to their proficient and precise nature and
relative simplicity. This work presents a comparison between Support
Vector Machines and Artificial Neural Networks two popular soft
computing models when applied to credit scoring. Amidst the
different criteria-s that can be used for comparisons; accuracy,
computational complexity and processing times are the selected
criteria used to evaluate both models. Furthermore the German credit
scoring dataset which is a real world dataset is used to train and test
both developed models. Experimental results obtained from our study
suggest that although both soft computing models could be used with
a high degree of accuracy, Artificial Neural Networks deliver better
results than Support Vector Machines.
[1] Hajek, P.: Municipal credit rating modelling by neural networks.
Decision Support Systems. 51, 108--118 (2011)
[2] Hong,S.K., Sohn, S.Y.: Support vector machines for default prediction
of SMEs based on technology credit. European Journal of Operational
Research. 201, 838--846 (2010)
[3] Paleologo, G., Elisseeff, A., Antonini, G.: Subagging for Credit Scoring
Models. European Journal of Operational Research. 201, 490--499
(2010)
[4] Khandani, A.E., Kim, A.J., Lo, A.W.: Consumer credit-risk models via
machine-learning algorithms. Journal of Banking & Finance 34, 2767--
2787 (2010)
[5] Wang, G., Hao, J., Ma,J., and Jiang, H.: A comparative assessment of
ensemble learning for credit scoring. Expert Systems with Applications
38, 223-230 (2011)
[6] Zhang,D., Zhou, X., Leung, S. C.H., Zheng, J.: Vertical bagging
decision trees model for credit scoring. Expert Systems with
Applications 37, 7838-7843(2010)
[7] Zhou, L., Lai, K.K. Yu,L.: Least squares support vector machines
ensemble models for credit scoring. Expert Systems with Applications
37,127-133 (2010)
[8] Tsai, C.F., Chen, M.L.: Credit rating by hybrid machine learning
techniques. Applied Soft Computing 10, 374--380 (2010)
[9] Chen, F.L., Li. F.C.: Combination of feature selection approaches with
SVM in credit scoring. Expert Systems with Applications 37, 4902--
4909 (2010)
[10] Asuncion, A., Newman, D.J.: UCI Machine Learning Repository
[http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA:
University of California, School of Information and Computer Science.
(2007)
[11] Chang, C.C., Lin, C.J. : LIBSVM: a library for support vector machines.
Software available at: /http://www.csie.ntu.edu.tw/cjlin/libsvm. (2001)
[1] Hajek, P.: Municipal credit rating modelling by neural networks.
Decision Support Systems. 51, 108--118 (2011)
[2] Hong,S.K., Sohn, S.Y.: Support vector machines for default prediction
of SMEs based on technology credit. European Journal of Operational
Research. 201, 838--846 (2010)
[3] Paleologo, G., Elisseeff, A., Antonini, G.: Subagging for Credit Scoring
Models. European Journal of Operational Research. 201, 490--499
(2010)
[4] Khandani, A.E., Kim, A.J., Lo, A.W.: Consumer credit-risk models via
machine-learning algorithms. Journal of Banking & Finance 34, 2767--
2787 (2010)
[5] Wang, G., Hao, J., Ma,J., and Jiang, H.: A comparative assessment of
ensemble learning for credit scoring. Expert Systems with Applications
38, 223-230 (2011)
[6] Zhang,D., Zhou, X., Leung, S. C.H., Zheng, J.: Vertical bagging
decision trees model for credit scoring. Expert Systems with
Applications 37, 7838-7843(2010)
[7] Zhou, L., Lai, K.K. Yu,L.: Least squares support vector machines
ensemble models for credit scoring. Expert Systems with Applications
37,127-133 (2010)
[8] Tsai, C.F., Chen, M.L.: Credit rating by hybrid machine learning
techniques. Applied Soft Computing 10, 374--380 (2010)
[9] Chen, F.L., Li. F.C.: Combination of feature selection approaches with
SVM in credit scoring. Expert Systems with Applications 37, 4902--
4909 (2010)
[10] Asuncion, A., Newman, D.J.: UCI Machine Learning Repository
[http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA:
University of California, School of Information and Computer Science.
(2007)
[11] Chang, C.C., Lin, C.J. : LIBSVM: a library for support vector machines.
Software available at: /http://www.csie.ntu.edu.tw/cjlin/libsvm. (2001)
@article{"International Journal of Engineering, Mathematical and Physical Sciences:60631", author = "Nnamdi I. Nwulu and Shola G. Oroja", title = "A Comparison of Different Soft Computing Models for Credit Scoring", abstract = "It has become crucial over the years for nations to
improve their credit scoring methods and techniques in light of the
increasing volatility of the global economy. Statistical methods or
tools have been the favoured means for this; however artificial
intelligence or soft computing based techniques are becoming
increasingly preferred due to their proficient and precise nature and
relative simplicity. This work presents a comparison between Support
Vector Machines and Artificial Neural Networks two popular soft
computing models when applied to credit scoring. Amidst the
different criteria-s that can be used for comparisons; accuracy,
computational complexity and processing times are the selected
criteria used to evaluate both models. Furthermore the German credit
scoring dataset which is a real world dataset is used to train and test
both developed models. Experimental results obtained from our study
suggest that although both soft computing models could be used with
a high degree of accuracy, Artificial Neural Networks deliver better
results than Support Vector Machines.", keywords = "Artificial Neural Networks, Credit Scoring, SoftComputing Models, Support Vector Machines.", volume = "5", number = "6", pages = "867-6", }