Abstract. In the conditions where the information representing the creditworthiness of bank customers has a large volume and features of uncertainty, it is necessary to identify the hidden relationship between the data, the specific laws of predicting the course of the processes, classification, intellectualization of the studied process are important issues of analysis. Therefore, it is important to determine and evaluate the creditworthiness of bank customers through neural network algorithms. The principles and methods of intelligent data analysis, prediction, evaluation, and object-oriented programming were used in the research process. In the research work, a model for assessing the creditworthiness of bank customers is proposed; it is proposed to build a classification and evaluation model of intellectual analysis of creditworthiness of bank customers based on a multi-layer neural network algorithm; an algorithm for determining and evaluating the creditworthiness of bank customers was developed using multilayer neural networks.
References:
1. V.V. Kruglov, M.I. Dli, and R.Yu. Fuzzy logic and artificial neural networks. - M.: Fizmatlit, 2001.
2. Zagidullin BI, Nagaev IA, Zagidullin N.Sh., Zagidullin Sh.Z. _ A neural network model for the diagnosis of myocardial infarction. // Russian Journal of Cardiology. 2012; (6): 51-54.
3. Han Y., Lam W., Ling C.X. Customized classification learning based on query projections, Information Sciences 177 (2007) 3557–3573.
4. Jie Lu, Guangquan Zhang Da Ruan, Fendjie Wu.Multi-objective group decision Making. Imperial College Press, London, 2007, 390.
5. Mukhamedieva D.T., Egamberdiev N.A., Zokirov J.Sh. Mathematical support for solving the classification problem using neural network algorithms // Turkish Journal of Computer and Mathematics Education. Vol.12 No.10 (2021)
6. Oyang Y.J., Hwang S.C., Ou Y.Y., Chen C.Y., Chen Z.W. Data classification with the radial basis function network based on a novel kernel density estimation algorithm, IEEE Transactions on Neural Networks 16 (1) (2005) 225–236.
7. Peng L., Yang B., et al. (2009). "Data gravitation based classification." Inf. Sci. 179(6): 809-819.
8. Tozan H., Vayvay O. Analyzing Demand Variability Through SC Using Fuzzy Regression and Grey GM(1,1) Forecasting Models, Information Sciences 2007, World Scientific, 2007, pp. 1088-1094.
9. Vityaev E.E., Lapardin K.A., Khamicheva I.V., Proskura A.L. Transcription factor binding site recognition by regularity matrices based on the natural classification method. Intellegent Data Analysis. Special issue: “New Methods in Bioinformatics. Presented at the fifth International Conference on Bioinformatics of Genom Regulation and Structure” eds. Evgenii Vityaev and Nikolai Kolchanov. v.12(5), IOS Press, 2008 pp. 495-512.
10. Алиев Р.А., Алиев Р.Р. Теория интеллектуальных систем и ее применение. - Баку, Изд-во Чашыоглы, 2001. -720 с.
11. Egamberdiyev N.A., Muhamediyeva D.T., Jurayev Z.Sh. Qualitative analysis of mathematical models based on Z-number // Proceedings of the Joint International Conference STEMM: Science – Technology – Education – Mathematics – Medicine. May 16-17, 2019, Tashkent, pp.42-43.
12. Egamberdiev N., Mukhamedieva D. and Khasanov U. Presentation of preferences in multi-criterional tasks of decision-making // IOP Conf. Series:Journal of Physics: Conference Series 1441 (2020) 012137. DOI: https://doi.org/10.1088/1742-6596/1441/1/012137
13. Muhamediyeva D.T. and Egamberdiyev N.A. Algorithm and the Program of Construction of the Fuzzy Logical Model //2019 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, Uzbekistan, 2019, pp. 1-4.
14. Muhamediyeva D.T., Egamberdiyev N., Bozorov A. Forecasting risk of non-reduction of harvest //Proceedings of the 2nd International Scientific and Practical Conference “Scientific community: Interdisciplinary research”. - Hamburg, Germany. 26-28.01.2021. Pp.694-699.
15. Muhamediyeva D.T.,Egamberdiyev N., Xushboqov I.U. Formulation of the problem particle swarm method for solving the global optimization // Proceedings of the 7th International Scientific and Practical Conference “Scientific horizon in the context of social crises”. -Tokyo, Japan. 6-8.02.2021. Pp.1076-1082.
16. Mirzayan K., Dilnoz M., Barno S. (2021) The Problem of Classifying and Managing Risk Situations in Poorly Formed Processes. // In: Aliev R.A., Yusupbekov N.R., Kacprzyk J., Pedrycz W., Sadikoglu F.M. (eds) 11th World Conference “Intelligent System for Industrial Automation” (WCIS-2020). WCIS 2020. Advances in Intelligent Systems and Computing, vol 1323. Springer, Cham. Pp 280-286.
SCIENCE & INNOVATION