[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.
刘伟江,魏海,运天鹤. 基于卷积神经网络的客户信用评估模型研究*[J]. 数据分析与知识发现, 2020, 4(6): 80-90.
Liu Weijiang,Wei Hai,Yun Tianhe. Evaluation Model for Customer Credits Based on Convolutional Neural Network. Data Analysis and Knowledge Discovery, 2020, 4(6): 80-90.
( Jiang Hui, Ma Chaoqun, Xu Xuqing, et al. An EM-similar Imputation Algorithm for Multivariable Data Missing and Its Application in Credit Scoring[J]. Chinese Journal of Management Science, 2019,27(3):11-19.)
( Xiao Jin, Liu Dunhu, Gu Xin, et al. Dynamic Classifier Ensemble Selection Model for Bank Customer’s Credit Scoring[J]. Journal of Management Sciences in China, 2015,18(3):114-126.)
[3]
Altman E I. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy[J]. The Journal of Finance, 1968,23(4):589-609.
doi: 10.1111/j.1540-6261.1968.tb00843.x
[4]
Wiginton J C. A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior[J]. Journal of Financial Quantitative Analysis, 1980(15):757-770.
( Wu Chong, Xia Han. Study of Customer Credit Evaluation Under E-commerce Based on Support Vector Machine Ensemble[J]. Chinese Journal of Management Science, 2008,16(S1):362-367.)
[6]
Blanco A, Pino-Mejías R, Lara J, et al. Credit Scoring Models for the Microfinance Industry Using Neural Networks: Evidence from Peru[J]. Expert Systems with Applications, 2013,40(1):356-364.
doi: 10.1016/j.eswa.2012.07.051
[7]
Chen F L, Li F C. Combination of Feature Selection Approaches with SVM in Credit Scoring[J]. Expert Systems with Applications, 2010,37(7):4902-4909.
doi: 10.1016/j.eswa.2009.12.025
( Wu Xingze. Problems on Research of Predicting Financial Distress and Framework Reconstructure[J]. Accounting Research, 2011(2):59-65, 97.)
[10]
He K M, Zhang X Y, Ren S Q, et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification [C]// Proceedings of the 2015 IEEE International Conference on Computer Vision. 2015: 1026-1034.
[11]
Sun S N, Zhang B B, Xie L, et al. An Unsupervised Deep Domain Adaptation Approach for Robust Speech Recognition[J]. Neurocomputing, 2017,257:79-87.
doi: 10.1016/j.neucom.2016.11.063
[12]
Williamson D S, Wang D L. Time-frequency Masking in the Complex Domain for Speech Dereverberation and Denoising[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2017,25(7):1492-1501.
doi: 10.1109/TASLP.2017.2696307
pmid: 30112422
[13]
Zhang Y, Marshall I, Wallace B C. Rationale-augmented Convolutional Neural Networks for Text Classification [C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2016: 795-804.
( Li Hui, Chai Yaqing. Fine-Grained Sentiment Analysis Based on Convolutional Neural Network[J]. Data Analysis and Knowledge Discovery, 2019,3(1):95-103.)
[15]
Hosaka T. Bankruptcy Prediction Using Imaged Financial Ratios and Convolutional Neural Networks[J]. Expert Systems with Applications, 2019,117:287-299.
doi: 10.1016/j.eswa.2018.09.039
[16]
Lencun Y, Bottou L, Bengio Y, et al. Gradient-Based Learning Applied to Document Recognition[J]. Proceedings of the IEEE, 1998,86(11):2278-2324.
doi: 10.1109/5.726791
[17]
阚士行. 商业银行信用评级筛选财务指标方法效果对比与校验[D]. 济南:山东大学, 2010.
[17]
( Kan Shixing. Performance Comparison of Several Methods for Selecting Indices of Commercial Bank Credit Ranking[D]. Ji’nan: Shandong University, 2010.)
( Liu Dan, Li Zhanjiang, Zheng Xixi. Selection Model of Credit Index Combination Based on WOE-Probit Stepwise Regression and Its Application[J]. Mathematics in Practice and Theory, 2018,48(2):76-87.)
