%A Liu Weijiang,Wei Hai,Yun Tianhe %T Evaluation Model for Customer Credits Based on Convolutional Neural Network %0 Journal Article %D 2020 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2019.1285 %P 80-90 %V 4 %N 6 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4861.shtml} %8 2020-06-25 %X

[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.