Personal credit scoring based on Logistic and RBF combined model

被引：0
|
作者：
Jiang, Ming-Hui [1 ]
Xie, Xing-Heng [2 ]
Wang, Shu-Lin [1 ]
Wen, Xiao [3 ]
机构：
[1] School of Management, Harbin Institute of Technology, Harbin 150001, China
[2] Ningbo University of Technology, Ningbo 315016, China
[3] Institute of Interational Studies, School of Humanities and Social Sciences, Tsinghua University, Beijing 100084, China
来源：
Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology | 2007年 / 39卷 / 07期
关键词：
Combined forecasting - Logistic regression - Personal credit scoring;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Aiming at the insufficiencies of single models in personal credit scoring, this paper presents a method for personal credit scoring by using combining forecast. Based on the advantages of single method, this paper chose typical Logistic regression and RBF neural network to construct two single models and then constructed a combining forecast model. Using the constructed models to classify the consumer credit data from one commercial bank, the application result indicates that the combining forecast model increases the accuracy effectively as well as model's stability which presents more applicable for commercial banks to keep away from consumer credit risks.
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页码：1128 / 1130
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