MATEC Web Conf.
Volume 76, 201620th International Conference on Circuits, Systems, Communications and Computers (CSCC 2016)
|Number of page(s)||9|
|Published online||21 October 2016|
Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network
1 Faculty of Informatics and Computer science, The British University in Egypt, Cairo, Egypt
2 Department of Computers and Systems, Electronics Research Institute, Cairo, Egypt
Corresponding author: firstname.lastname@example.org
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.
Key words: Credit scoring / ANN / PCA / credit risk / German data
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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