UDC 347.75 UDC 004.93

ASSESSMENT OF LEGAL RISKS OF COUNTERPARTIES Assessment of legal risks of counterparties

Published in JOURNAL OF MONETARY ECONOMICS AND MANAGEMENT · Issue 8 · Pages 190–194 · Rubric: SCIENTIFIC ARTICLES
DOI: https://doi.org/10.26118/2782-4586-2026-190-194 · EDN: GYWCFK
Received: 17.08.2026 Accepted: 31.08.2026 Published: 31.08.2026
In today's business environment, characterized by a high degree of uncertainty and complexity of legal relations, assessing the legal risks of counterparties is becoming critically important to ensure the economic security and sustainability of the business. Neural networks used for the intelligent analysis of large amounts of structured and unstructured data offer an innovative approach to automation and qualitative improvement of the decision-making process in this area. This article is a comprehensive review of methods and practical research on the use of neural network technologies to analyze the legal risks of counterparties. The fundamental principles of operation of multilayer neural networks, including the perceptron architecture, data preprocessing and normalization methods, as well as the iterative learning process to identify hidden patterns, are discussed in detail. Specific examples of research and software implementations are given that demonstrate the high efficiency of neural networks in classifying and predicting risks based on the analysis of financial statements, court cases and other legally significant documents, which minimizes the impact of the subjective factor and significantly improves the accuracy of the assessment.
legal risk assessment, counterparties, software module, machine learning, data analysis, financial indicators, and court cases
Text References
Text (PDF)
Read Download

1. Sokolov, E. V. Osnovy yuridicheskoy informatiki. M.: Pravo i standarty, 2020.

2. Yakovlev, N. S. Razrabotka internet-resursov dlya yuridicheskih organov. M.: Pravo, 2022.

3. Nazarov, S. G. Sovremennye veb-tehnologii: teoriya i praktika. Ekaterinburg: Ural'skoe izdatel'stvo, 2021.

4. Ocenka riskov pri vybore kontragenta: sposoby i servisy [Elektronnyy resurs] // kontur.ru – URL: https://www.sekretariat.ru/article/210898-organizatsiya-dokumentooborota-v-organizatsii-19-m4 (data obrascheniya 10.03.2025).

5. Proverka kontragentov. Riski, kriterii, chek-list dlya buhgaltera [Elektronnyy resurs] // www.klerk.ru - URL: https://www.klerk.ru/blogs/lockobank/530663 (data obrascheniya 07.03.2025).

6. Upravlenie riskami: model' processa i kompetenciy [Elektronnyy resurs] // www.businessstudio.ru - URL: https://www.businessstudio.ru/articles/article/upravlenie_riskami_model_protsessa_i_kompetentsiy/, (data obrascheniya 05.03.2025).

7. Yakovlev, P.G., Finansovyy analiz i ocenka riskov (2019). Vestnik finansovogo universiteta, 78-92 s.

8. Raschet riskov i modelirovanie ugroz na predpriyatii | RTM Group [Elektronnyy resurs] // rtmtech.ru - URL: https://rtmtech.ru/articles/modelirovanie-ugroz-na-predpriyatii/, (data obrascheniya 05.03.2025).

9. Analiz biznes-processov: etapy i instrumenty | SILA Union [Elektronnyy resurs] // silaunion.ru - URL: https://silaunion.ru/articles/analiz-biznes-processov-etapy-i-instrumenty, (data obrascheniya 16.03.2025).

10. Grishkin, A.Yu., Upravlenie kreditnymi riskami: teoriya i praktika. - (2019). – 86-89 s.

11. Solov'ev, V.V., Analiz i upravlenie riskami. - (2020). – 75-66 s.

12. Tihomirov, A.A., Metody ocenki kreditnyh riskov - (2018). Finansovaya analitika: problemy i resheniya, 45-50 s.

13. Kuznecov, A.E., Riski i risk-menedzhment v biznese. - (2021). – 44-68s.