[1]钟世钦.基于卷积神经网络的网贷违约风险模型研究[J].计算机技术与发展,2022,32(05):123-129.[doi:10. 3969 / j. issn. 1673-629X. 2022. 05. 021]
　ZHONG Shi-qin.Research on Prediction Model of Internet Loan Default Based on CNN[J].,2022,32(05):123-129.[doi:10. 3969 / j. issn. 1673-629X. 2022. 05. 021]
点击复制

基于卷积神经网络的网贷违约风险模型研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年05期
页码:
123-129
栏目:
应用前沿与综合
出版日期:
2022-05-10

文章信息/Info

Title:
Research on Prediction Model of Internet Loan Default Based on CNN
文章编号:
1673-629X(2022)05-0123-07
作者:
钟世钦1; 2
1. 合肥工业大学 管理学院,安徽 合肥 230009;
2. 合肥工业大学 过程优化与智能决策教育部重点实验室,安徽 合肥 230009
Author(s):
ZHONG Shi-qin1; 2
1. School of Management,Hefei University of Technology,Hefei 230009,China;
2. The Ministry of Education Key Laboratory of Process Optimization and Intelligent Decision-making,Hefei University of Technology,Hefei 230009,China
关键词:
卷积神经网络; 网贷风险; 深度学习; 特征提取; 模型组合
Keywords:
convolutional neural networks; risk of internet loan; deep learning; extracted features; model combination
分类号:
TP391. 1
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 05. 021
摘要:
自互联网金融诞生以来,网络贷款得到了快速发展,但是借贷双方的信息不对称增加了网贷的违约风险,同时随着互联网技术的发展网贷用户数据也表现出高维化的趋势,数据处理面临更大的挑战,因此亟需应对该问题并在网贷违约风险上进行准确、稳定的预测。 该文提出了卷积神经网络( CNN) 和一般机器学习模型结合的预测模型,利用 CNN 在数据特征提取上的优势来处理高维的网贷用户信息。 首先采用数值图形化思想对网贷用户数据进行处理并与 CNN 对接,其次调整其超参数选择合适的网络模型,然后基于三种一般机器学习模型与 CNN 的组合进行网贷风险预测测试,最后在真实数据集上使用最优的网贷违约风险预测模型进行预测。 实验结果验证了组合模型的显著性以及 CNN 对一般机器学习模型性能提升的能力,为网贷风险预测提供了一种新思路。
Abstract:
Since the birth of Internet financial,loans developed fast,but information asymmetry between the two sides increased default risk. With the development of Internet technology,net credit user data also show the trend of high dimension,the data processing arefaced bigger challenge,? ? so we need to solve the problem and make the net credit default risk prediction more accurately and stably. Weput forward a prediction model combining convolutional neural network ( CNN) and general machine learning model which takes advantage of CNN in data feature extraction? ? to deal with high-dimensional network credit user information. First we connect CNN and data by numerical graphical ideas,second we adjust? ? the parameters to choose the appropriate network model,and then we test net credit risk prediction based on the combination of three kinds of general machine learning model and CNN,finally we use the real data to predict by the best combined model. The experimental results show the significance of the combined model and the ability of CNN to improve the performance of the general machine learning model,which provides a new idea for the risk prediction of network lending.

相似文献/References:

[1]崔凤焦.表情识别算法研究进展与性能比较[J].计算机技术与发展,2018,28(02):145.[doi:10．3969/j．issn．1673－629X．2018．02．031]
　CUI Feng-jiao.Ｒesearch and Performance Comparison of Facial Expression Ｒecognition Algorithm[J].,2018,28(05):145.[doi:10．3969/j．issn．1673－629X．2018．02．031]
[2]张丹丹,李雷. 基于PCANet-RF的人脸检测系统[J].计算机技术与发展,2016,26(02):31.
　ZHANG Dan-dan,LI Lei. Face Detection System Based on PCANet-RF[J].,2016,26(05):31.
[3]陈强锐,谢世朋.基于深度学习的肺部肿瘤检测方法[J].计算机技术与发展,2018,28(04):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
　CHEN Qiang-rui,XIE Shi-peng.Lung Cancer Detection Method Based on Deep Learning[J].,2018,28(05):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
[4]郭子琰,舒心,刘常燕,等.基于ReLU 函数的卷积神经网络的花卉识别算法[J].计算机技术与发展,2018,28(05):154.[doi:10．3969/j．issn．1673－629X．2018．05．035]
　GUO Ziyan,SHU Xin,LIU Changyan,et al.A Recognition Algorithm of Flower Based on Convolution Neural Network with ReLU Function[J].,2018,28(05):154.[doi:10．3969/j．issn．1673－629X．2018．05．035]
[5]缪宇杰,吴智钧,宫 婧.基于3D 卷积的视频错帧筛选方法[J].计算机技术与发展,2018,28(05):179.[doi:10.3969/ j. issn.1673-629X.2018.05.040]
　MIAO Yu-jie,WU Zhi-jun,GONG Jing.A Wrong Temporal-order Frames Identification Method Based on 3D Convolution[J].,2018,28(05):179.[doi:10.3969/ j. issn.1673-629X.2018.05.040]
[6]吴玉枝,吴志红,熊运余.基于卷积神经网络的小样本车辆检测与识别[J].计算机技术与发展,2018,28(06):1.[doi:10.3969/ j. issn.1673-629X.2018.06.001]
　WU Yu-zhi,WU Zhi-hong,XIONG Yun-yu.Vehicle Detection and Recognition of a Few Samples Based on Convolutional Neural Network[J].,2018,28(05):1.[doi:10.3969/ j. issn.1673-629X.2018.06.001]
[7]李相桥,李晨,田丽华,等.卷积神经网络并行训练的优化研究[J].计算机技术与发展,2018,28(08):12.[doi:10.3969/ j. issn.1673-629X.2018.08.003]
　LI Xiang-qiao,LI Chen,TIAN Li-hua,et al.Research on Optimization of Parallel Training for Convolution Neural Network[J].,2018,28(05):12.[doi:10.3969/ j. issn.1673-629X.2018.08.003]
[8]邓宗平,赵启军,陈虎. 基于深度学习的人脸姿态分类方法[J].计算机技术与发展,2016,26(07):11.
　DEND Zong-ping,ZHAO Qi-jun,CHEN Hu. Face Pose Classification Method Based on Deep Learning[J].,2016,26(05):11.
[9]河海大学 计算机与信息学院,江苏 南京 0098.卷积网络的无监督特征提取对人脸识别的研究[J].计算机技术与发展,2018,28(06):17.[doi:10.3969/ j. issn.1673-629X.2018.06.004]
　DU Bai-sheng.Research on Unsupervised Feature Extraction Based on Convolutional Neural Network for Face Recognition[J].,2018,28(05):17.[doi:10.3969/ j. issn.1673-629X.2018.06.004]
[10]高翔,陈志,岳文静,等.基于视频场景深度学习的人物语义识别模型[J].计算机技术与发展,2018,28(06):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]
　GAO Xiang,CHEN Zhi,YUE Wen-jing,et al.Human Semantic Recognition Model Based on Video Scene Deep Learning[J].,2018,28(05):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]

更新日期/Last Update: 2022-05-10