Research Article

The Excellent Method of Novel Uncertainty Predictor for Risk Assessment in E-Financial Institutions

Alireza Miremadi
Sharif University of Technology-International Campus, Iran
Omidreza Ghanadiof
University of Central Missouri, USA
* Corresponding author
DOI icon 10.24018/ejsocial.2021.1.4.94

Read Counter
301

Downloads
345

Citations

Share

Article Sidebar

Submitted 2021-07-06
Published 2021-07-28

Read counter = 301 times

Table of Contents

Article Main Content

Financial institutions are always engaged in risk assessment. In fact, the birth of finance in Europe was through dealing with risk. Risk is a situation that we have more than a possible future with different probabilities. Assessing different futures with different level of probabilities is not that easy to formulate. Neural network is a topology developed for dealing with cases in which formulating the problem is not easy due to model flexibility which is required by the conditions of risks we are dealing with in E-finance. E-finance is providing financial services over electronic devices and cyber space. With the prominent growth of E-finance, the need for developing new models for assessing risk associated with this kind of business seems inevitable. Continuous growth of E-finance brings on new issues such as E-trust; consequently, the need for developing a model of total risk assessment is the base of our study. The presented model is a prototype, future models should be developed specifically for different kind of risk E-finance provider are dealing with.

Keywords: Back propagation, E-Finance, E-Trust, Neural Networks, Predictive Controller, Risk Assessment

References

  1. Bowersox, D. J., Closs, D., & Helferich, O. K. (1986). Logistics management. New York: Macmillan.
     Google Scholar
  2. P. Campolucci, A. Uncini, F. Piazza, B.D. Rao, On-line learning algorithms for locally recurrent neural networks, IEEE Transactions on Neural Networks 10 (2) (1999) 253–271.
     Google Scholar
  3. Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(1), 28 – 44.
     Google Scholar
  4. S. Nolfi, D. Floreano, Evolutionary Robotics, MIT Press, 2000.
     Google Scholar
  5. D. Hein, M. Hild, R. Berger, Evolution of biped walking using neural oscillators and physical simulation, in: U. Visser, et al.et al. (Eds.), RoboCup 2007, vol.5001, LNAI, 2008, pp. 433–440.
     Google Scholar
  6. Mohajerani, P., & Miremadi, A. (2013). Exploring two main perspectives towards customer satisfaction in hotel industry: managers and customers. International Journal of Academic Research in Business and Social Sciences, 3(9), 245.
     Google Scholar
  7. Ghanadiof, O., Sanayei, A., & Emami, M. (2021). Effect of Customer Perception on Salesperson Owned Commitment in Customer-Salesperson Relationship. European Journal of Business & Management Research. https://doi.org/10.24018/ejbmr.2021.6.4.964
     Google Scholar
  8. Oh S-K, Pedrycz W, Park H-S. Rule-based Multi-FNN identification with the aid of evolutionary fuzzy granulation. J Knowl-Based Syst 2004;17(1):1 – 13.
     Google Scholar
  9. Cordon O, Gomide F, Herrera F, Hoffmann F, Magdalena L. Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets Syst 2004;141(1):5 – 31.
     Google Scholar
  10. Fardhosseini, M. S., Soltaninejad, M., Karji, A., Ghorbani, Z. & Ghanadiof, O. (2021). Qualitative Evaluation of 5S Application Considering the Experience of Electrical Construction Experts. American Journal of Applied Sciences, 18(1), 51-60. https://doi.org/10.3844/ajassp.2021.51.60.
     Google Scholar
  11. S.W. Wilson, State of XCS classifier system research, Second International Workshop on Learning Classifier Systems during GECCO99, 1999.
     Google Scholar
  12. Yeh, C. C., Chi, D. J., & Hsu, M. F. (2010). A hybrid approach of DEA, rough set and support vector machines for business failure prediction. Expert Systems with Applications, 37, 1535–1541.
     Google Scholar
  13. Miremadi, A., & Ghanadiof, O. (2021). The Ultimate Influences of Brand Equity Dimensions on Consumer Decision in Hi-Tech Market. Academic Journal of Research and Scientific Publishing. doi. org/10.52132/Ajrsp. e.
     Google Scholar
  14. A. Campo, J. Santos, Evolution of adaptive center-crossing continuous time recurrent neural networks for biped robot control, in: Proceedings European Symposium on Artificial Neural Networks (ESANN 2010), Bruges (Belgium),2010, pp. 535–540.
     Google Scholar
  15. A.S. Weigend, Time series analysis and prediction using gated experts with application to energy demand forecast, Applied Artificial Intelligence 10 (1996) 583–624.
     Google Scholar
  16. X. Yao, Evolving articular neural networks, Proceedings IEEE 87 (1999) 1423-1447.
     Google Scholar
  17. Oh S-K, Pedrycz W, Park H-S. Hybrid identification in fuzzy-neural networks. Fuzzy Sets Syst 2003;138(2):399 – 426.
     Google Scholar
  18. R.A. Jacobs, M.I. Jordan, S.J. Nowlan, G.E. Hinton, Adaptive mixtures of local experts, Neural Computation 3 (1991) 79–87.
     Google Scholar
  19. R. Beer, Parameter space structure of continuous recurrent neural networks, Neural Computer. 18 (2006) 3009–3051.
     Google Scholar
  20. Miremadi, A., Kenar Roudi, J., & Ghanadiof, O. (2021). Evaluation on Role of Electronic Word of Mouth (EWOM) Ads in Customers’ Emotions and Choices in E-Shops. International Journal of Industrial Marketing, 6(1). https://doi.org/10.5296/ijim.v6i1.
     Google Scholar
  21. Cherkassky V, Gehring D, Mulier F. Comparison of adaptive methods for function estimation from samples. IEEE Trans Neural Netw 1996; 7:969– 84.
     Google Scholar
  22. Miremadi, A., Golchobian, M. M. A., & Ghanadiof, O. (2021). Requirement and Architecture of Organization Development. European Journal of Business and Management Research, 6(4), 55-64. https://doi.org/10.24018/ejbmr.2021.6.4.932.
     Google Scholar
  23. Gupta, M. M., & Qi, J. (1991). On fuzzy neuron models. Proceedings of international joint conference on neural networks, Seattle, II, 4312– 4436.
     Google Scholar
  24. Miremadi, A., & Ghanadiof, O. (2021). CRM Competitive Strategy in Financial Institutions. European Journal of Business and Management Research, 6(3), 111-117. https://doi.org/10.24018/ejbmr.2021.6.3.867.
     Google Scholar
  25. de Jager, C. P. C., van Wijk, P. T. L., Mathoera, R. B., de Jongh-Leuvenink, J., van der Poll, T., & Wever, P. C. (2010). Lymphocytopenia and neutrophil-lymphocyte count ratio predict bacteriemia better than conventional infection markers in an emergency care unit. Critical Care, 14(R192), 108–115.
     Google Scholar
  26. Miremadi, A., & Khoei, R. (2013). The art of visual merchandising on consumer buying behavior. International Journal Contemporary Business Studies, 4(6), 34-50.
     Google Scholar
  27. L.R. Rabiner, B.H. Juang, An introduction to Hidden Markov models, IEEE ASSP Magazine3 (1) (1986) 4–16.
     Google Scholar
  28. S. Wischmann, F. Pasemann, From passive to active dynamic 3D bipedal walking — an evolutionary approach, in: M. Armada, P. Gonzalez de Santos (Eds.), Climbing and Walking Robots: Proceedings of the 7th International Conference (CLAWAR 2004), Springer-Verlag, 2004, pp. 737–744.
     Google Scholar
  29. G. McHale, P. Husbands, GasNets and other evolvable neural networks applied to bipedal locomotion, in: From Animals to Animats 8— Proceedings of the Eighth International Conference on the Simulation of Adaptive Behavior, 2004, pp. 163–172.
     Google Scholar
  30. Ghanadiof, O. (2021). Customer loyalty and powerful brand in heavy machinery industry. European Journal of Business and Management Research, 6(3). https://doi.org/10.24018/ejbmr.2021.6.3.903.
     Google Scholar
  31. Handfield, R. B., & Nichols, E. L., Jr (1999). Introduction to supply chain management. Englewood Cliffs, NJ: Prentice Hall.
     Google Scholar