Credit risk remains the most critical challenge facing bank’s management as it adversely affects the profitability and stability of the bank. However, despite the rise in loan delinquency and the serious competition in the banking market, loan application evaluations at the Jordanian commercial banks are subjective in nature. Additionally, the ability to discriminate between ‘good’and ‘bad’risk applications is critical. Rejecting a good application might cause loss of future potential profit while approving a bad application might cause loss of principal money and interest. The current research aims to develop credit decision support using linear discriminant analysis (LDA), multi-layer perceptron (MLP) and CART decision trees for the protection against credit risk. A pooled data set of personal loans from Jordanian commercial banks was used to build the decision models. The discriminative power of the developed models was assessed using average correct classification rate (ACC) and the estimated misclassification cost (EMC). The results showed that the MLP model achieved the highest ACC as well as the lowest EMC.