Abstract
Neural nets have become one of the most important tools using in credit scoring. Credit scoring is regarded as a core appraised tool of commercial banks during the last few decades. The purpose of this paper is to investigate the ability of neural nets, such as probabilistic neural nets and multi-layer feed-forward nets, and conventional techniques such as, discriminant analysis, probit analysis and logistic regression, in evaluating credit risk in Egyptian banks applying credit scoring models. The credit scoring task is performed on one bank's personal loans' data-set. The results so far revealed that the neural nets-models gave a better average correct classification rate than the other techniques. A one-way analysis of variance and other tests have been applied, demonstrating that there are some significant differences amongst the means of the correct classification rates, pertaining to different techniques. © 2007 Elsevier Ltd. All rights reserved.
DOI
10.1016/j.eswa.2007.08.030
Publication Date
2008-10-01
Publication Title
Expert Systems with Applications
Volume
35
Issue
3
Publisher
Elsevier BV
ISSN
0957-4174
Embargo Period
2024-11-19
First Page
1275
Last Page
1292
Recommended Citation
Abdou, H., Pointon, J., & El-Masry, A. (2008) 'Neural nets versus conventional techniques in credit scoring in Egyptian banking', Expert Systems with Applications, 35(3), pp. 1275-1292. Elsevier BV: Available at: https://doi.org/10.1016/j.eswa.2007.08.030
