摘要：With the development of economic globalization and financial liberalization, credit assessment plays an important role in maintaining the normal relationship of social economy. Personal credit assessment requires establishing calibration models with statistic methods. The mono-method-based models are not capable to simultaneously hold the robustness, interpretation and prediction accuracy of the models. In this paper, back-propagation neural network (BPNN) was used to generate a new comprehensive variable for logistic regression (LR) by tuning the number of hidden nodes. The optimal back-propagation neural network-logistic regression combination model (BPNN-LR) was established with 5 input nodes, 7 hidden nodes and 1 output node. The model performance was slightly improved. The prediction accuracy was raised up to 86.33% and 87.96% for the training samples and the test samples, respectively. Results showed that the BPNN-LR model had higher classification accuracy than the LR model. It is concluded that the outcome performance provides technical reference for the corporation’s decision making.
关键词：Personal Credit Assessment;Back-Propagation Neural Network;Logistic Regression;Information Value;Weight of Evidence
