Banks have various services offered to their customers, one of which is credit. Credit is a service that provides loans to customers with payment terms and agreements. In the process, these services often experience problems in terms of the repayment process. So, this study aims to look at customer profile data and classify it into two groups, namely customers who have and do not have the opportunity to apply for the loans. This process is carried out with the help of two algorithms, namely the Decision Tree and Artificial Neural Network (ANN), by identifying the customer profile criteria obtained from the prediction of credit offers to customers. Both algorithms are evaluated using three parameters, namely gain ratio, confusion matrix, and Receiver Operating Characteristic (ROC). The Decision Tree produces the highest accuracy performance value of 99,34%, and AUC of 0, 998. Meanwhile, the ANN algorithm produces the highest accuracy of 99,13%, and AUC of 0.969. From each of these algorithms, it can be known about the effect of the treatment carried out on each parameter, and the effect on the performance results obtained.