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 |
| Published: |
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 |
| Summary: | 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. |
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| ISSN: | 2261-236X |