Using R language to create credit scorecard model using a supervised learning technique by experimenting with 3 algorithms (Naive Bayes, Random Forest and Artificial Neural Network). Solve the issues of data imbalance by applying SMOTE algorithm together with principal component analysis (PCA) to reduce the dimension of the dataset. Then run an experiment to see the impact on the performance of dataset with SMOTE & PCA. The result showed that there is an increase performance of the model with SMOTE & PCA treatment and Artificial Neural Network (ANN) worked best among other models that has been developed.