A NOVEL FIVE-CATEGORY LOAN-RISK EVALUATION MODEL USING MULTICLASS LS-SVM BY PSO

Jie Cao (), Hongke Lu (), Weiwei Wang and Jian Wang
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Jie Cao: School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing 210044, China
Hongke Lu: School of Economics & Management, Southeast University, Nanjing 210096, China
Weiwei Wang: School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing 210044, China
Jian Wang: Jiangsu Jinnong Information Co., Ltd., Nanjing 210019, China

International Journal of Information Technology & Decision Making (IJITDM), 2012, vol. 11, issue 04, 857-874

Abstract: Five-category loan classification (FCLC) is an international financial regulation approach. Recently, the application and implementation of FCLC in the Chinese microfinance bank has mostly relied on subjective judgment, and it is difficult to control and lower loan risk. In view of this, this paper is dedicated to researching and solving this problem by constructing the FCLC model based on improved particle-swarm optimization (PSO) and the multiclass, least-square, support-vector machine (LS-SVM). First, LS-SVM is the extension of SVM, which is proposed to achieve multiclass classification. Then, improved PSO is employed to determine the parameters of multiclass LS-SVM for improving classification accuracy. Finally, some experiments are carried out based on rural credit cooperative data to demonstrate the performance of our proposed model. The results show that the proposed model makes a distinct improvement in the accuracy rate compared with one-vs.-one (1-v-1) LS-SVM, one-vs.-rest (1-v-r) LS-SVM, 1-v-1 SVM, and 1-v-r SVM. In addition, it is an effective tool in solving the problem of loan-risk rating.

Keywords: Particle-swarm optimization; least-squares support-vector machine; credit risk; five-category classification (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1142/S021962201250023X

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