| dc.description.abstract | Credit Scoring has been a key undertaking of lenders over the last few decades. However, the most common use of credit
scoring has been as a credit application appraisal tool rather than as a credit monitoring tool for existing credit holdings.
This study, in the literature, investigated the different sets of sophisticated and classical credit scoring techniques used in
the areas of classification and prediction of customers defaulting on credit repayments. The main aim of this study was to
build a behavioral credit scoring model from a provided dataset using Logistic Regression. The study also aimed to use
data mining concepts to develop a prototype to automate the modeling process and calibrate predictions so as to
formulate score cards. Our review of literature indicated the non-existence of an overall superior method for credit
scoring since modeling objectives always differ and are thus suited for diverse methods. Model validation and
assessments methods, variable selection and interpretation of validation results were investigated. The results of the
study indicated the possibility of use of validation methods from related fields, the formulation of model deployment
frameworks and formulation of a guide to trade-off between model hit-rate and false alarms. Transformation variables
were also found to often offer better predictive power over raw data variables whilst the Gini-Index was shown to be a
good indicator of model over-fitting. Finally, this study suggests possible future research endeavors in the effort to
eliminate the requirements of statistical knowledge in the Credit Scoring process. | en |
