Credit scoring models for Egyptian banks : neural nets and genetic programming versus conventional techniques

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.