NDLI: National student loans credit risk assessment based on GABP algorithm of neural network

Content Provider	IEEE Xplore Digital Library
Author	Zhenyu Zhao Wei Zhang Yayue Zhou
Copyright Year	2011
Description	Author affiliation: Department of Law, Ningbo University, China (Zhenyu Zhao; Yayue Zhou) \|\| Department of Management, Harbin Institute of Technology, China (Wei Zhang)
Abstract	Policy of national student loans accelerates the reform of higher education in China and the process of market mechanism of talents training in a very great degree, and provides the important guarantee for the poor college students. However, at present, high default rate makes commercial bank which provides student loans bear the risk of bad debt, and affects the policy of national student loan to develop smoothly to a certain extent. This paper uses the improved GABP algorithm of neural network to construct national student loans credit risk assessment model by which identify credit risk level. This paper will effectively reduce national student loans credit risk and promote the healthy development policy of national student loan.
Starting Page	2196
Ending Page	2199
File Size	214680
Page Count	4
File Format	PDF
ISBN	9781457705359
e-ISBN	9781457705366
DOI	10.1109/AIMSEC.2011.6010910
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Predictive models Educational institutions GABP Credit risk Neural network Risk management Mathematical model National student loans Genetic algorithms
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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