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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Main Authors: Ha Van-Sang, Nguyen Ha-Nam
Format: Article
Language:English
Published: EDP Sciences 2016-01-01
Series:MATEC Web of Conferences
Online Access:http://dx.doi.org/10.1051/matecconf/20165405004
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spelling 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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