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Credit scoring has been regarded as a critical topic and studied extensively in the finance field. Many artificial intelligence techniques have been used to solve credit scoring. The paper is to build a classification model based on a decision tree by learning historical data. Clustering algorithm and genetic algorithm are combined to further improve the accuracy of this credit scoring model. The clustering algorithm aims at removing noise data, while the genetic algorithm is used to reduce the redundancy attribute of data. The computational results on the two real world benchmark data sets show that the presented hybrid model is efficient.