Affordable Access

Publisher Website

Credit scoring using the hybrid neural discriminant technique

Authors
Journal
Expert Systems with Applications
0957-4174
Publisher
Elsevier
Publication Date
Volume
23
Issue
3
Identifiers
DOI: 10.1016/s0957-4174(02)00044-1
Keywords
  • Credit Scoring
  • Discriminant Analysis
  • Neural Networks
  • Model Basis
Disciplines
  • Computer Science
  • Design
  • Mathematics

Abstract

Abstract 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.

There are no comments yet on this publication. Be the first to share your thoughts.