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Genetic programming for credit scoring: The case of Egyptian public sector banks

Abdou, Hussein (2009) Genetic programming for credit scoring: The case of Egyptian public sector banks. Expert Systems with Applications, 36 (9). pp. 11402-11417. ISSN 09574174

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Credit scoring has been widely investigated in the area of finance, in general, and banking sectors, in particular.
Recently, genetic programming (GP) has attracted attention in both academic and empirical fields,
especially for credit problems. The primary aim of this paper is to investigate the ability of GP, which was
proposed as an extension of genetic algorithms and was inspired by the Darwinian evolution theory, in the
analysis of credit scoring models in Egyptian public sector banks. The secondary aim is to compare GP with
probit analysis (PA), a successful alternative to logistic regression, and weight of evidence (WOE) measure,
the later a neglected technique in published research. Two evaluation criteria are used in this paper,
namely, average correct classification (ACC) rate criterion and estimated misclassification cost (EMC) criterion
with different misclassification cost (MC) ratios, in order to evaluate the capabilities of the credit scoring
models. Results so far revealed that GP has the highest ACC rate and the lowest EMC. However, surprisingly,
there is a clear rule for the WOE measure under EMC with higher MC ratios. In addition, an analysis of the
dataset using Kohonen maps is undertaken to provide additional visual insights into cluster groupings.

Item Type: Article
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HB Economic Theory
H Social Sciences > HJ Public Finance
Schools: The Business School
The Business School > Quantitative Analysis Research Group
Depositing User: Sara Taylor
Date Deposited: 17 Sep 2013 10:29
Last Modified: 03 Nov 2015 15:41


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