Credit scoring with a feature selection approach based deep learning
In financial risk, credit risk management is one of the most important issues in financial decision-making. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. Deep learning is...
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2016-01-01
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Online Access: | http://dx.doi.org/10.1051/matecconf/20165405004 |
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doaj-1be7331451054f35b781c7f000d7b6d42021-08-11T14:29:26ZengEDP SciencesMATEC Web of Conferences2261-236X2016-01-01540500410.1051/matecconf/20165405004matecconf_mimt2016_05004Credit scoring with a feature selection approach based deep learningHa Van-Sang0Nguyen Ha-Nam1Department of Economic Information System, Academy of FinanceDepartment of Information Technology, University of Engineering and TechnologyIn financial risk, credit risk management is one of the most important issues in financial decision-making. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. Deep learning is a powerful classification tool which is currently an active research area and successfully solves classification problems in many domains. Deep Learning provides training stability, generalization, and scalability with big data. Deep Learning is quickly becoming the algorithm of choice for the highest predictive accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, reduce the running time, and improve the accuracy of classifiers. In this study, we constructed a credit scoring model based on deep learning and feature selection to evaluate the applicant’s credit score from the applicant’s input features. Two public datasets, Australia and German credit ones, have been used to test our method. The experimental results of the real world data showed that the proposed method results in a higher prediction rate than a baseline method for some certain datasets and also shows comparable and sometimes better performance than the feature selection methods widely used in credit scoring.http://dx.doi.org/10.1051/matecconf/20165405004 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ha Van-Sang Nguyen Ha-Nam |
spellingShingle |
Ha Van-Sang Nguyen Ha-Nam Credit scoring with a feature selection approach based deep learning MATEC Web of Conferences |
author_facet |
Ha Van-Sang Nguyen Ha-Nam |
author_sort |
Ha Van-Sang |
title |
Credit scoring with a feature selection approach based deep learning |
title_short |
Credit scoring with a feature selection approach based deep learning |
title_full |
Credit scoring with a feature selection approach based deep learning |
title_fullStr |
Credit scoring with a feature selection approach based deep learning |
title_full_unstemmed |
Credit scoring with a feature selection approach based deep learning |
title_sort |
credit scoring with a feature selection approach based deep learning |
publisher |
EDP Sciences |
series |
MATEC Web of Conferences |
issn |
2261-236X |
publishDate |
2016-01-01 |
description |
In financial risk, credit risk management is one of the most important issues in financial decision-making. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. Deep learning is a powerful classification tool which is currently an active research area and successfully solves classification problems in many domains. Deep Learning provides training stability, generalization, and scalability with big data. Deep Learning is quickly becoming the algorithm of choice for the highest predictive accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, reduce the running time, and improve the accuracy of classifiers. In this study, we constructed a credit scoring model based on deep learning and feature selection to evaluate the applicant’s credit score from the applicant’s input features. Two public datasets, Australia and German credit ones, have been used to test our method. The experimental results of the real world data showed that the proposed method results in a higher prediction rate than a baseline method for some certain datasets and also shows comparable and sometimes better performance than the feature selection methods widely used in credit scoring. |
url |
http://dx.doi.org/10.1051/matecconf/20165405004 |
work_keys_str_mv |
AT havansang creditscoringwithafeatureselectionapproachbaseddeeplearning AT nguyenhanam creditscoringwithafeatureselectionapproachbaseddeeplearning |
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1721210909877075968 |