Credit Scoring is the use of decision models and their techniques that aid lenders in advancing consumer credit. These models assess credit worthiness of applicants for a loan by forecasting their probability of default or non-default. It has been seen historically that banks developed their own system of credit risk to their customers, and because they are privileged to such information, this makes it difficult to know how to measure the risk involved. We therefore, in order to answer questions like: How to reduce the default risk? Which client qualities for the credit? What loan limit should we approve? among others, decided to propose a credit scoring model that attempts to answer those basic questions, test its robustness and carry out its validity. The major reason is that there exist differences in socio-economic behavior and economies and this may lead to making the wrong decision which results into the unsound distribution of the banks available resources. Therefore, there is a need to have a model based on the uniqueness of an economy, which includes variables not considered in the existing models currently in use. With the Logistic Regression model, the choice of x0 is determined how best this model works. Receiver Operating Characteristic curves, Correct Classification Matrix and Gini coefficients were used as evaluation criteria to compare their performances and the Kolmogorov-Smirnov curve as a robustness test. The results demonstrated that an improvement in terms of predicting power from 30.0% default cases under the current system, to 12.4% based on the best scoring model, namely Logistic Regression could be achieved. The predictive capabilities of all models are rated as at least very good using the Gini coefficient; and rated excellent using the Receiver Operating Characteristic curve for Logistic Regression. Our robustness test confirmed these results. Owing to the advantageous accuracy gain of over 85% using the best model, it’s clear that this would lead to an improvement in the decision-making process in retail banking.