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Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes. (Khashman, Adnan,)
Bibliographical information (record 268805)
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Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes.
Author:
 Khashman, Adnan, Search Author in Amazon Books

Publisher:
Pergamon-Elsevier Science Ltd.,
ISBN:
 0957-4174
Edition:
 2010.
Classification:
 QA76
URL:
http://library.neu.edu.tr:2048/login?url=http://dx.doi.org/10.1016/j.eswa.2010.02.101
Detailed notes
    - This paper describes a credit risk evaluation system that uses supervised neural network models based on the back propagation learning algorithm. We train and implement three neural networks to decide whether to approve or reject a credit application. Credit scoring and evaluation is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. The neural networks are trained using real world credit application cases from the German credit approval datasets which has 1000 cases; each case with 24 numerical attributes; based on which an application is accepted or rejected. Nine learning schemes with different training-to-validation data ratios have been investigated, and a comparison between their implementation results has been provided. Experimental results will suggest which neural network model, and under which learning scheme, can the proposed credit risk evaluation system deliver optimum performance; where it may be used efficiently, and quickly in automatic processing of credit applications. (C) 2010 Elsevier Ltd. All rights reserved.
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Section
EOL-1673
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NEU Grand LibraryOnline (QA76 .N48 2010)
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