Modelling Loan-Defaulting Tendencies among Customers of A Local Ghanaian Bank

Abstract

In Ghana, some local banks lack a comprehensive credit risk management framework that includes the use of credit scoring; hence, the purpose of this study is to develop a credit scoring tool derived from a binary logistic regression model to reduce credit risk exposure for banks in general and local banks in particular. A review of the literature on credit scoring models or classifiers revealed that the specificity and sensitivity of the developed models are not explored further to reveal insights into the optimal cut-off point of the model. This study seeks to fill this gap by further exploring the specificity and sensitivity of the developed model and offers explanations and insights about the variations of the optimal cut-off point. The study makes a case for using the optimal cut-off point as a practical decision point for financial institutions. Secondary data on borrowers were obtained from a local bank and examined to identify its retail customers' demographic and behavioural characteristics based on the minimum Know Your Customer (KYC) required by the central bank. The binary logistic regression model developed from the data had an overall classification accuracy of 93% at a cut-off point of 0.5; however, the sensitivity measure was barely 23%. Typically, resampling techniques are employed to deal with the imbalance, however, in this study a plot of sensitivity and specificity against the probability of default is used to derive an optimal cut-off point. The performance of the logistic regression model at the derived optimal cut-off point was found to be similar to other binary models’ performance that used resampling techniques.