In this paper, we focus on the application of machine learning algorithms to identify credit risk customers. The main objective is to firstly, identify the most important factors that may be associated with “good credit” customers, and to compare good credit customers with bad credit customers. The logistic regression model was used to score customers on their likelihood of being a “good credit” customer. The logistic regression model was highly accurate in predicting credit risk with a sensitivity and specificity of 74% and 75% respectively. Therefore, by following the classifications of the logistics regression model results, the bank was able to minimize losses of -$88 000 to a profit of $26 000.