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A Comparison of Different Soft Computing Models for Credit Scoring

Authors: Nnamdi I. Nwulu, Shola G. Oroja

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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1077579

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References:


[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)