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A loan default discrimination model using cost-sensitive support vector machine improved by PSO

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Abstract

This study proposes a novel PSO–CS-SVM model that hybridizes the particle swarm optimization (PSO) and cost sensitive support vector machine (CS-SVM) to deal with the problem of unbalanced data classification and asymmetry misclassification cost in loan default discrimination problem. Cost sensitive learning is applied to the standard SVM by integrating misclassification cost of each sample into standard SVM and PSO is employed for parameter determination of the CS-SVM. Meantime, the financial data are discretized by using the self-organizing mapping neural network. And the evaluation indices are reduced without information loss by genetic algorithm for decreasing the complexity of the model. The effectiveness of integrated model of CS-SVM and PSO is verified by three experiments comparing with traditional CS-SVM, PSO–SVM, SVM and BP neural network through real loan default data of companies in China. The corresponding results indicate that the accuracy rate, hit rate, covering rate and lift coefficient are improved dramatically by the developed approach. The proposed method can control the different types of errors distribution with various cost of misclassification accurately, reduce the total misclassification cost largely, and distinguish the loan default problems effectively.

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Acknowledgments

The authors would like to thank the anonymous referees for their valuable comments and suggestions. Their comments helped to improve the quality of the paper immensely. This work is partially supported by NSFC (60804047), the Science and Technology Project of Jiangsu province, China (BE2010201), Ministry of education, humanities and social sciences research project (11YJCZH005), Jiangsu provincial department of education philosophy and social science project (2010SJB790025) and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Jie Cao & Weiwei Wang

  2. School of Economics and Management, Southeast University, Nanjing, 210096, China

    Hongke Lu

  3. Jiangsu Jinnong Information Co., Ltd., Nanjing, 210019, China

    Jian Wang

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  1. Jie Cao
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  2. Hongke Lu
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  3. Weiwei Wang
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  4. Jian Wang
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Correspondence to Jie Cao.

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Cao, J., Lu, H., Wang, W. et al. A loan default discrimination model using cost-sensitive support vector machine improved by PSO. Inf Technol Manag 14, 193–204 (2013). https://doi.org/10.1007/s10799-013-0161-1

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