Neural nets versus conventional techniques in credit scoring in Egyptian banking

H. Abdou, J. Pointon, A. El-Masry

Research output: Contribution to journalArticle

83 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1275-1292
Number of pages17
JournalExpert Systems with Applications
Volume35
Issue number3
Early online date10 Aug 2007
DOIs
Publication statusPublished - Oct 2008
Externally publishedYes

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Neural networks
Data privacy
Discriminant analysis
Analysis of variance (ANOVA)
Logistics

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Neural nets versus conventional techniques in credit scoring in Egyptian banking. / Abdou, H.; Pointon, J.; El-Masry, A.

In: Expert Systems with Applications, Vol. 35, No. 3, 10.2008, p. 1275-1292.

Research output: Contribution to journalArticle

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