DocumentCode
1843783
Title
Artificial metaplasticity: An approximation to credit scoring modeling
Author
Marin-de-la-Barcena, Amparo ; Marcano-Cedeño, Alexis ; Jimenez-Trillo, Juan ; Piñuela, Juan A. ; Andina, Diego
Author_Institution
Group for Autom. in Signals & Commun., Tech. Univ. of Madrid, Madrid, Spain
fYear
2010
fDate
7-10 Nov. 2010
Firstpage
2817
Lastpage
2822
Abstract
Risk Management improvement and credit risk evaluation are turning core areas of concern within the financial and banking industries. Specifically credit scoring, as one of the key analytical techniques in credit risk evaluation is envisioned as an arena in which the application of Artificial Intelligence (IA) and Neural systems has high potential for development. This paper contributes by presenting a novel Neural based approach to enhance credit scoring modeling inspired by the biological metaplasticity property of neurons.
Keywords
artificial intelligence; finance; neural nets; risk management; artificial intelligence; artificial metaplasticity; credit risk evaluation; credit scoring modeling; neural systems; risk management; Artificial intelligence; Artificial neural networks; Biological system modeling; Neurons; Risk management; Training;
fLanguage
English
Publisher
ieee
Conference_Titel
IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society
Conference_Location
Glendale, AZ
ISSN
1553-572X
Print_ISBN
978-1-4244-5225-5
Electronic_ISBN
1553-572X
Type
conf
DOI
10.1109/IECON.2010.5675097
Filename
5675097
Link To Document