Personal Credit Default Prediction Model Based on Convolution Neural Network
It has great significance for the healthy development of credit industry to control the credit default risk by using the information technology. For some traditional research about the credit default prediction model, more attention is paid to the model accuracy, while the business characteristics o...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2020-01-01
|
Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2020/5608392 |
id |
doaj-00b89ffb5fc24f1fb407b5d66e1c0443 |
---|---|
record_format |
Article |
spelling |
doaj-00b89ffb5fc24f1fb407b5d66e1c04432020-11-25T03:08:00ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472020-01-01202010.1155/2020/56083925608392Personal Credit Default Prediction Model Based on Convolution Neural NetworkXiang Zhou0Wenyu Zhang1Yefeng Jiang2School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, Liaoning, ChinaSchool of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, Liaoning, ChinaSchool of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, Liaoning, ChinaIt has great significance for the healthy development of credit industry to control the credit default risk by using the information technology. For some traditional research about the credit default prediction model, more attention is paid to the model accuracy, while the business characteristics of the credit risk prevention are easy to be ignored. Meanwhile, to reduce the complicity of the model, the data features need be extracted manually, which will decrease the high-dimensional correlation among the analyzing data and then result in the low prediction performance of the model. So, in the paper, the CNN (convolutional neural network) is used to establish a personal credit default prediction model, and both ACC (accuracy) and AUC (the area under the ROC curve) are taken as the performance evaluation index of the model. Experimental results show the model ACC (accuracy) is above 95% and AUC (the area under the ROC curve) is above 99%, and the model performance is much better than the classical algorithm including the SVM (support vector machine), Bayes, and RF (random forest).http://dx.doi.org/10.1155/2020/5608392 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiang Zhou Wenyu Zhang Yefeng Jiang |
spellingShingle |
Xiang Zhou Wenyu Zhang Yefeng Jiang Personal Credit Default Prediction Model Based on Convolution Neural Network Mathematical Problems in Engineering |
author_facet |
Xiang Zhou Wenyu Zhang Yefeng Jiang |
author_sort |
Xiang Zhou |
title |
Personal Credit Default Prediction Model Based on Convolution Neural Network |
title_short |
Personal Credit Default Prediction Model Based on Convolution Neural Network |
title_full |
Personal Credit Default Prediction Model Based on Convolution Neural Network |
title_fullStr |
Personal Credit Default Prediction Model Based on Convolution Neural Network |
title_full_unstemmed |
Personal Credit Default Prediction Model Based on Convolution Neural Network |
title_sort |
personal credit default prediction model based on convolution neural network |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2020-01-01 |
description |
It has great significance for the healthy development of credit industry to control the credit default risk by using the information technology. For some traditional research about the credit default prediction model, more attention is paid to the model accuracy, while the business characteristics of the credit risk prevention are easy to be ignored. Meanwhile, to reduce the complicity of the model, the data features need be extracted manually, which will decrease the high-dimensional correlation among the analyzing data and then result in the low prediction performance of the model. So, in the paper, the CNN (convolutional neural network) is used to establish a personal credit default prediction model, and both ACC (accuracy) and AUC (the area under the ROC curve) are taken as the performance evaluation index of the model. Experimental results show the model ACC (accuracy) is above 95% and AUC (the area under the ROC curve) is above 99%, and the model performance is much better than the classical algorithm including the SVM (support vector machine), Bayes, and RF (random forest). |
url |
http://dx.doi.org/10.1155/2020/5608392 |
work_keys_str_mv |
AT xiangzhou personalcreditdefaultpredictionmodelbasedonconvolutionneuralnetwork AT wenyuzhang personalcreditdefaultpredictionmodelbasedonconvolutionneuralnetwork AT yefengjiang personalcreditdefaultpredictionmodelbasedonconvolutionneuralnetwork |
_version_ |
1715297231443066880 |