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Issue:Assessment of credit risk in SMEs financing using neural networks and intuitionistic fuzzy estimations: Difference between revisions

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  | issue          = [[Notes on Intuitionistic Fuzzy Sets/20/4|"Notes on IFS", Volume 20, 2014, Number 4]],  
  | issue          = [[Notes on Intuitionistic Fuzzy Sets/20/4|"Notes on IFS", Volume 20, 2014, Number 4]], pages 47–52
pages 47–52
  | file            = NIFS-20-4-47-52.pdf
  | file            = NIFS-20-4-47-52.pdf
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Revision as of 14:02, 24 February 2016

[Category:Publications on intuitionistic fuzzy sets|Assessment of credit risk in SMEs financing using neural networks and intuitionistic fuzzy estimations]]

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http://ifigenia.org/wiki/issue:nifs/20/4/47-52
Title of paper: Assessment of credit risk in SMEs financing using neural networks and intuitionistic

fuzzy estimations

Author(s):
Evdokia Sotirova
Intelligent Systems Laboratory, “Prof. Asen Zlatarov” University, Burgas–8000, Bulgaria
esotirova@btu.bg
Magdalena Georgieva
School of Mathematics and Natural Sciences “Acad. Nikola Obreshkov”, Burgas–8000, Bulgaria
magdalenam.georgieva@gmail.com
Ivaylo Mihaylov
Intelligent Systems Laboratory, “Prof. Asen Zlatarov” University, Burgas–8000, Bulgaria
iv_mihaylov@yahoo.com
Published in: "Notes on IFS", Volume 20, 2014, Number 4, pages 47–52
Download:  PDF (131  Kb, File info)
Abstract: In the paper is presented a methodology for credit risk assessment in Small and medium-

sized enterprises (SMEs) using theories of the Neural Networks and Intuitionistic Fuzzy Sets. In this way the

procedure for credit assessment can be unified and optimized.

Keywords: Credit risk assessment, Neural networks, Intuitionistic fuzzy sets.
AMS Classification: 03E72
References:
  1. Atanassov K. Intuitionistic Fuzzy Sets: Theory and Applications, Springer Physica-Verlag, Berlin, 1999.
  2. Atanassov K. On Intuitionistic Fuzzy Sets Theory, Studies in Fuzziness and Soft Computing, Vol 283, Springer, Berlin, 2012.
  3. Bishop, C. M. Neural Network for Pattern Recognition. Oxford University Press, Oxford, 1995.
  4. Dimitrova, L., K. Petkowa, Risk assessment in agile software development using bayesian networks, Science & Technologies Volume III, Number 3, 2013, Natural & Mathematical science, 92-97 (in bulgarian).
  5. Hagan, M., H. Demuth, M. Beale, Neural Network Design, Boston, MA, PWS Publishing, 1996.
  6. Haykin, S., Neural Network – A Comprehensive Foundation. Macmillan College Publication Company, New York, 1994.
  7. Jokipii, T., A. Milne, The cyclical behaviour of European bank capital buffers. Bank of Finland Research, Discussion Papers Vol. 17, 2006, page 13.
  8. Kacprzyk, A., I. Mihailov, Intuitionistic fuzzy estimation of the liquidity of the banks. A generalized net model, Proc. of 13th Int. Workshop on Generalized Nets, London, 29 October 2012, 34–42.
  9. KPMG. Managing Credit Risk. Beyond Basel II, http://us.kpmg.com/microsite/FSLibrary/docs/FRM_ManagingCreditRisk_WP.pdf
  10. Mihaylov, I., E. Sotirova, Evaluation of credit risk in SMEs financing using index matrices and intuitionistic fuzzy estimations, Notes on Intuitionistic Fuzzy Sets, Vol. 19, 2013, No. 2, 42–52.
  11. Shahpazov, G., L. Doukovska, K. Atanassov, Generalized net model of the methodology for analysis of the creditworthiness and evaluation of credit risk in smes financing, Proc. of the 3rd Int. Symposium on Buisness Modeling and Software Design, Noordwijkerhout, The Netherlands, 2013, 292–297.
  12. Shahpazov, G., L. Doukovska, K. Atanassov, Generalized net model of internal structural unit functionality, focused on SME financing, Proc. of the 12th Int. Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, Warsaw, Vol II: Applications, 2013, 83–92
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