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Title: Classification methods of credit rating - A comparative analysis on SVM, MDA and RST
Authors: Hsu, C.F.;Hung, Hsufeng
Contributors: 企管系
Keywords: Bank credit;Classification accuracy;Classification methods;Classification models;Comparative analysis;Credit ratings;Customer relationship management;Data sets;Decision making support system;Discriminate analysis;Empirical analysis;Feature selection;Risk measurement;SVM model;Two classification;Artificial intelligence;Classification (of information);Computer software;Public relations;Rating;Risk assessment;Software architecture;Support vector machines;Decision making
Date: 2009-12
Issue Date: 2015-06-02 10:18:55 (UTC+8)
Abstract: The execution and the result of bank credit rating are closely linked with the bank's investment and loan policies which form the initial risk measurement. It is an important and a shouldn't ignored issue for bankers to set up a scientific, objective and accurate credit rating model in the field of customer relationship management. In this study, two classification methods, multiple discriminate analysis (MDA), CANDISC, and support vector machine (SVM) are applied to conduct a comparative empirical analysis using real world commercial loan data set. The result comes out that SVM model has reliable high classification accuracy under feature selection and therefore is suitable for bank credit rating. This study suggests the decision-making personnel to establish a decision-making support system to assist their judgment by using the classification model. ©2009 IEEE.
Relation: Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009,-
Data Type: conference
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Appears in Collections:[風險管理與保險學系 ] 會議論文

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