Credit application scoring for consumers without credit history

Abstract

Credit scoring is a tool that is used to either qualify or disqualify credit applicants by quantifying the risk factors relevant to classify them to high risk or low risk. Due to a demand in credit inclusion, financial institutions, especially banks must come up with a way of screening and scoring applicants. In most cases, applicants are required to have credit history, or risk being denied credit because these institutions cannot charge high interest rates, mainly because they are obliged legally not to do so on the repayment of the loans due to a lack of the applicant’s credit history. In this study, the concept and application of credit scoring is explained. The steps necessary to develop a credit scoring model are outlined with the focus on data that do not have any credit history. Literature is reviewed discussing the background information regarding the performance of the logistic regression model and other statistical models in classifying consumers. Datasets, statistical models, methodology and variables were reviewed and used to assist in building the scorecard. Secondary data collected from the General Household Survey (GHS) is used to classify credit applicants into two groups of high risk and low risk. Binary logistic regression is used to identify the variables that best predict these two groups. The forward selection technique is used in determining variables that are significant. The developed model is tested for prediction accuracy and thereafter, this is followed by key findings and recommendations. In conclusion, the developed model is found to be fitting the data well.