Credit scoring has been regarded as a core appraisal tool of banks during the last few
decades, and has been widely investigated in the area of finance, in general, and
banking sectors, in particular. In this thesis, the main aims and objectives are: to identify
the currently used techniques in the Egyptian banking credit evaluation process; and to
build credit scoring models to evaluate personal bank loans. In addition, the subsidiary
aims are to evaluate the impact of sample proportion selection on the Predictive
capability of both advanced scoring techniques and conventional scoring techniques, for
both public banks and a private banking case-study; and to determine the key
characteristics that affect the personal loans' quality (default risk).
The stages of the research comprised: firstly, an investigative phase, including an early
pilot study, structured interviews and a questionnaire; and secondly, an evaluative
phase, including an analysis of two different data-sets from the Egyptian private and
public banks applying average correct classification rates and estimated
misclassification costs as criteria. Both advanced scoring techniques, namely, neural
nets (probabilistic neural nets and multi-layer feed-forward nets) and genetic
programming, and conventional techniques, namely, a weight of evidence measure,
multiple discriminant analysis, probit analysis and logistic regression were used to
evaluate credit default risk in Egyptian banks. In addition, an analysis of the data-sets
using Kohonen maps was undertaken to provide additional visual insights into cluster
groupings.
From the investigative stage, it was found that all public and the vast majority of private
banks in Egypt are using judgemental approaches in their credit evaluation. From the
evaluative stage, clear distinctions between the conventional techniques and the
advanced techniques were found for the private banking case-study; and the advanced
scoring techniques (such as powerful neural nets and genetic programming) were
superior to the conventional techniques for the public sector banks. Concurrent loans
from other banks and guarantees by the corporate employer of the loan applicant, which
have not been used in other reported studies, are identified as key variables and
recommended in the specific environment chosen, namely Egypt. Other variables, such
as a feasibility study and the Central Bank of Egypt report also play a contributory role
in affecting the loan quality.
Date of Award | 2009 |
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Original language | English |
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Awarding Institution | |
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Supervisor | John Pointon (Other Supervisor) |
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Credit scoring models for Egyptian banks : neural nets and genetic programming versus conventional techniques
Abdou, H. A. H. (Author). 2009
Student thesis: PhD