It is very important for financial institutions to develop credit rating systems to help them to decide whether to grant credit to consumers before issuing loans. In literature, statistical and machine learning techniques for credit rating have been extensively studied. Recent studies focusing on hybrid models by combining different machine learning techniques have shown promising results. However, there are various types of combination methods to develop hybrid models. It is unknown that which hybrid machine learning model can perform the best in credit rating. In this paper, four different types of hybrid models are compared by 'Classification + Classification', 'Classification + Clustering', 'Clustering + Classification', and 'Clustering + Clustering' techniques, respectively. A real world dataset from a bank in Taiwan is considered for the experiment. The experimental results show that the 'Classification + Classification' hybrid model based on the combination of logistic regression and neural networks can provide the highest prediction accuracy and maximize the profit. (C) 2009 Elsevier B.V. All rights reserved.
