Credit scoring is becoming one of the main topics in the banking field. Lending decisions are usually represented as a set of classification tasks in consumer credit markets. In this paper, we have applied a recently introduced rule generator classifier called CORER1 (Colonial competitive Rule-based classifiER) to improve the accuracy of credit scoring classification task. The proposed classifier works based on Colonial Competitive Algorithm (CCA). In order to approve the CORER capability in the field of credit scoring, Australian credit real dataset from UCI machine learning repository has been used. To evaluate our classifier, we compared our results with other related well-known classification methods, namely C4.5, Artificial Neural Network, SVM, Linear Regression and Naive Bayes. Our findings indicate superiority of CORER due to better performance in the credit scoring field. The results also lead us to believe that CORER may have accurate outcome in other applications of banking.