A Comparative Study on Data Mining Algorithms for Individual Credit Risk Evaluation

Abstract

Individual credit risk evaluation is an important and challenging data mining problem in financial analysis domain. This paper compares the effectiveness of four data mining algorithms - logistic regression (LR), decision tree (C4.5), support vector machine (SVM) and neural networks (NN) by applying them to two credit data sets. Experiment results show that the LR and SVM algorithms produced the best classification accuracy, and the SVM shows the higher robustness and generalization ability compared to the other algorithms. On the contrary, the neural networks algorithm performed poor relatively on the two credit data sets in our experiments. The computer simulation shows the C4.5 algorithm is sensitive to input data, and the classification accuracy is unstable, but it has the better explanatory.

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