Abstract: [Objective] This paper analyzes customer loan information, and extracts their characteristics, aiming to more effectively predict customer defaults of online loans. [Methods] First, we collected customer credit data from Lending Club. Then, we integrated the characteristic variables from four aspects of customer information
and created a grayscale map. Finally, we established a customer credit evaluation model based on convolutional neural networks. [Results] The proposed model had specificity of 99. 4%, sensitivity of 68. 7%, G-mean value of 82. 7%, F1 value of 81. 4% and AUC value of 99. 5%. The performance of our new model was much better than those credit models based on feature processing. [Limitations] We only investigated the performance of a few
models. More research is needed to study the impacts of unbalanced data. [Conclusions] The proposed model effectively predicts probability of customer defaults.
Keywords: Convolutional Neural Networks Indicator Imaging Credit Evaluation Information Value PCA