Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network
Credit risk assessment for bank customers has gained increasing attention in recent years. Several models for credit scoring have been proposed in the literature for this purpose. The accuracy of the model is crucial for any financial institution’s profitability. This paper provided a high accuracy...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2016-01-01
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| Series: | MATEC Web of Conferences |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1051/matecconf/20167602039 |
| _version_ | 1818622341060493312 |
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| author | Hamdy Abeer Hussein Walid B. |
| author_facet | Hamdy Abeer Hussein Walid B. |
| author_sort | Hamdy Abeer |
| collection | DOAJ |
| description | Credit risk assessment for bank customers has gained increasing attention in recent years. Several models for credit scoring have been proposed in the literature for this purpose. The accuracy of the model is crucial for any financial institution’s profitability. This paper provided a high accuracy credit scoring model that could be utilized with small and large datasets utilizing a principal component analysis (PCA) based breakdown to the significance of the attributes commonly used in the credit scoring models. The proposed credit scoring model applied PCA to acquire the main attributes of the credit scoring data then an ANN classifier to determine the credit worthiness of an individual applicant. The performance of the proposed model was compared to other models in terms of accuracy and training time. Results, based on German dataset showed that the proposed model is superior to others and computationally cheaper. Thus it can be a potential candidate for future credit scoring systems. |
| first_indexed | 2024-12-16T18:23:37Z |
| format | Article |
| id | doaj.art-87a8a4d03ebe410384bc04c61ab7f573 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-16T18:23:37Z |
| publishDate | 2016-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-87a8a4d03ebe410384bc04c61ab7f5732022-12-21T22:21:29ZengEDP SciencesMATEC Web of Conferences2261-236X2016-01-01760203910.1051/matecconf/20167602039matecconf_cscc2016_02039Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural NetworkHamdy AbeerHussein Walid B.0Faculty of Informatics and Computer science, The British University in EgyptCredit risk assessment for bank customers has gained increasing attention in recent years. Several models for credit scoring have been proposed in the literature for this purpose. The accuracy of the model is crucial for any financial institution’s profitability. This paper provided a high accuracy credit scoring model that could be utilized with small and large datasets utilizing a principal component analysis (PCA) based breakdown to the significance of the attributes commonly used in the credit scoring models. The proposed credit scoring model applied PCA to acquire the main attributes of the credit scoring data then an ANN classifier to determine the credit worthiness of an individual applicant. The performance of the proposed model was compared to other models in terms of accuracy and training time. Results, based on German dataset showed that the proposed model is superior to others and computationally cheaper. Thus it can be a potential candidate for future credit scoring systems.http://dx.doi.org/10.1051/matecconf/20167602039Credit scoringANNPCAcredit riskGerman data |
| spellingShingle | Hamdy Abeer Hussein Walid B. Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network MATEC Web of Conferences Credit scoring ANN PCA credit risk German data |
| title | Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network |
| title_full | Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network |
| title_fullStr | Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network |
| title_full_unstemmed | Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network |
| title_short | Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network |
| title_sort | credit risk assessment model based using principal component analysis and artificial neural network |
| topic | Credit scoring ANN PCA credit risk German data |
| url | http://dx.doi.org/10.1051/matecconf/20167602039 |
| work_keys_str_mv | AT hamdyabeer creditriskassessmentmodelbasedusingprincipalcomponentanalysisandartificialneuralnetwork AT husseinwalidb creditriskassessmentmodelbasedusingprincipalcomponentanalysisandartificialneuralnetwork |