NDLI: Artificial Neural Network approach to credit risk assessment

Content Provider	IEEE Xplore Digital Library
Author	Dissananayake, B.M.M. Hendahewa, C.H. Karunananda, A.S.
Copyright Year	2007
Description	Author affiliation: Fac. of Inf. Technol., Univ. of Moratuwa, Moratuwa (Dissananayake, B.M.M.)
Abstract	With the wide use of credit facilities utilized by the corporate customers, the accuracy of predicting credit worthiness is of utmost importance to the financial institutions. This paper reports on a novel approach to credit risk assessment which incorporates the impact of financial, industry and economic factors specific to a particular industry, using artificial neural network technology. For the prototype, Credentials, the hotel industry of Sri Lanka was selected. The input parameters consider growth rates on a quarterly basis to determine the gearing position. Two neural networks are being trained for cashflow growth and debt growth prediction respectively using backpropagation learning rule. The predicted gearing growth rate for the next four consecutive quarters is generated as the output. The modular design incorporates Web services to export hotel and economic data and Hebbian based classification. According to the evaluation, the best accuracy levels were obtained for Colombo region five star hotels, where cashflow neural network and debt neural network yielded 75.0% and 62.5% success rates respectively. Overall, the evaluator panel was satisfied with the concept, approach, technical aspects, prototype and the reporting facilities of Credentials.
Starting Page	301
Ending Page	306
File Size	609272
Page Count	6
File Format	PDF
ISBN	9781424411511
DOI	10.1109/ICIINFS.2007.4579192
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-09
Publisher Place	Sri Lanka
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Industrial economics Industrial relations Neural networks Prototypes Artificial neural networks Probability Economic forecasting Risk management Environmental economics Business
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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