Credit Scoring Models for Egyptian Banks: Neural Nets and Genetic Programming versus Conventional Techniques

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  author =       "Hussein Ali Hussein Abdou",
  title =        "Credit Scoring Models for Egyptian Banks: Neural Nets
                 and Genetic Programming versus Conventional
  school =       "Plymouth Business School, University of Plymouth",
  year =         "2009",
  address =      "UK",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  URL =          "",
  size =         "452 pages",
  abstract =     "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.",
  notes =        "Supervisor John Pointon",

Genetic Programming entries for Hussein A Abdou