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《Journal of Wuyi University(Natural Science Edition)》 2014-01
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A Study of Early Warning on Credit Risks of Student Loans Based on the Support Vector Machine(SVM)

LI Yun-meng;GUI Rao-gen;SHI An-an;School of Economics and Management,Wuyi University;  
Considering the small amounts,the tedious management process and the high default rate of student loans,this paper proposes a student loans credit risk warning system based on the support vector machine to effectively reduce the scope of attention for loans and improve their management efficiency. On the basis of an analysis of the factors contributing to defaulting and the characteristics of an early warning model,an early warning model based on the support vector machine(SVM) is built. Actual data tests show that the model has a high accuracy rate in the prediction of contract observance and breach and can provide a basis for banks to adjust loan management strategies.
【Fund】: 广东省哲学社会科学规划项目(GD11XGL20)
【CateGory Index】: F832.4;TP18
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