Credit Scoring Using the Hybrid Neural Discriminant Technique

doi:10.1016/S0957-4174(02)00044-1

淡江大學機構典藏 > 商管學院 > 管理科學學系暨研究所 > 期刊論文 > Item 987654321/64922

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题名:	Credit Scoring Using the Hybrid Neural Discriminant Technique
作者:	陳怡妃;Lee, T. S.;Chiu, C. C.;Lu, C. J.
贡献者:	淡江大學經營決策學系
关键词:	Credit scoring;Discriminant analysis;Neural networks;Model basis
日期:	2002-09
上传时间:	2011-10-20 16:10:46 (UTC+8)
摘要:	Credit scoring has become a very important task as the credit industry has been experiencing double-digit growth rate during the past few decades. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the decision of network's topology, importance of potential input variables and the long training process has often long been criticized and hence limited its application in handling credit scoring problems. The objective of the proposed study is to explore the performance of credit scoring by integrating the backpropagation neural networks with traditional discriminant analysis approach. To demonstrate the inclusion of the credit scoring result from discriminant analysis would simplify the network structure and improve the credit scoring accuracy of the designed neural network model, credit scoring tasks are performed on one bank credit card data set. As the results reveal, the proposed hybrid approach converges much faster than the conventional neural networks model. Moreover, the credit scoring accuracies increase in terms of the proposed methodology and outperform traditional discriminant analysis and logistic regression approaches.
關聯:	Expert Systems with Applications 23(3), pp.245-254
DOI:	10.1016/S0957-4174(02)00044-1
显示于类别:	[管理科學學系暨研究所] 期刊論文

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