The neural network model of individuals credit rating
| Autor | Ilyas I. Ismagilov - Linar A. Molotov - Alexey S. Katasev - Dina V. Kataseva |
| Cargo | Kazan Federal University. - 1Kazan Federal University. - Kazan National Research Technical University named after A.N. Tupolev. e-mail: molotov.linar@mail.ru. Tel.: +7 9377703937. - Kazan National Research Technical University named after A.N. Tupolev. e-mail: molotov.linar@mail.ru. Tel.: +7 9377703937. |
| Páginas | 349-358 |
Periódico do Núcleo de Estudos e Pesquisas sobre Gênero e Direito
Centro de Ciências Jurídicas - Universidade Federal da Paraíba
V. 8 - Nº 06 - Ano 2019 – Special Edition
ISSN | 2179
-
7137 | http://periodicos.ufpb.br/ojs2/index.php/ged/index
349
THE NEURAL NETWORK MODEL OF INDIVIDUALS CREDIT
RATING
Ilyas I. Ismagilov1
Linar A. Molotov2
Alexey S. Katasev3
Dina V. Kataseva4
Abstract: This article solves the
problem of constructing and evaluating a
neural network model to determine the
creditworthiness of individuals. It is
noted that the most important part of the
modern retail market is consumer
lending. Therefore, an adequate and
high-quality assessment of the
creditworthiness of an individual is a key
aspect of providing credit to a potential
borrower. The theoretical and practical
aspects of assessing the creditworthiness
of individuals are considered. To solve
this problem, the need for the use of
intelligent modeling technologies based
on neural networks is being updated. The
construction of a neural network model
required the receipt of initial data on
1 1Kazan Federal University.
2 1Kazan Federal University.
3 2Kazan National Research Technical University named after A.N. Tupolev. e-mail:
molotov.linar@mail.ru. Tel.: +7 9377703937.
4 2Kazan National Research Technical University named after A.N. Tupolev. e-mail:
molotov.linar@mail.ru. Tel.: +7 9377703937.
borrowers. Using correlation analysis,
14 input parameters were selected that
most significantly affect the output. The
training and test data samples were
generated to build and evaluate the
adequacy of the neural network model.
Training and testing of the neural
network model was carried out on the
basis of the analytical platform
“Deductor”. Analysis of contingency
tables to assess the accuracy of the neural
network model in the training and test
samples showed positive results. The
error of the first kind on the data from the
training sample was 0.45%, and the error
of the second kind was 1.39%.
Accordingly, the error of the first kind
was not observed on the data from the
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