Applications of classification trees to consumer credit scoring methods in commercial banks

Abstract

Based on the theory of classification trees, the samples that are taken out from one commercial bank of China are put into classification tree models. When comparing results of models, it is considered that the model size, the structure of the sample and error cost could influence model's error rates. The optimized classification tree model is produced out based on the comparisons. It is concluded that classification trees are more suitable than logistic regression for present domestic credit scoring because of characters of the samples, through comparing classification trees and logistic regression.

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