Forecasting the Probability of Bankruptcy of Organizations Using the No-Code/Low-Code Platform for ETL Process Automation
DOI:
https://doi.org/10.52575/2687-0932-2025-52-4-851-860Keywords:
bankruptcy, probability, artificial intelligence technologies, artificial neural networks, model, no-code/low-code platformsAbstract
An analytical review of existing research in the field of bankruptcy probability forecasting has revealed the lack of representation of artificial intelligence models, in particular adaptive neural networks, in this area.
The aim of the study is to present a variant of assessing the probability of bankruptcy applying a neural network model developed by the authors. The model takes into account financial and non-financial factors of bankruptcy and is based on a no-code/low-code platform for automating ETL processes. The key idea of the study is that there is no need to create complex systems for an effective (accurate and fast) assessment of the probability of bankruptcy: suffice it to use the free Russian no-code/low-code platform for automating ETL processes and have data on financial and non-financial bankruptcy factors. As a result of the research, the authors have developed a methodological approach and tools for predicting the probability of bankruptcy, which may be utilized for assessing the reliability of limited liability companies. The proposed approach and tools are characterized by their potential versatility in the field of forecasting economic indicators. The described results may be used in further research into forecasting and planning, as well as for evaluating the effectiveness of management decisions.
Downloads
References
Список источников
«Контур Фокус». Детальная проверка контрагентов // СКБ Контур. – URL: https://kontur.ru/lp/focus (дата обращения: 04.07.2025).
Метод BFGS или один из самых эффективных методов оптимизации // Habr. – URL: https://habr.com/ru/post/333356/ (дата обращения: 04.07.2025).
Официальный сайт Loginom // Loginom. – URL: https://loginom.ru/ (дата обращения: 21.08.2025).
Продукт: Loginom Аналитическая платформа // Tadviser. – URL: https://www.tadviser.ru/index.php/Продукт:Loginom_Аналитическая_платформа?erid=LjN8KUa33 (дата обращения: 21.08.2025).
ФНС России вводит официальную оценку бизнеса с 2026 года: официальное сообщение // Федеральная налоговая служба. – URL: https://www.nalog.gov.ru/rn46/news/activities_fts/16530839/ (дата обращения: 31.08.2025).
Новая методика оценки бизнеса: как ФНС будет проверять юрлиц и ИП с 2026 года // ИнфоСтарт. – URL: https://infostart.ru/journal/news/uchet-nalogi-pravo/novaya-metodika-otsenki-biznesa-kak-fns-budet-proveryat-yurlits-i-ip-s-2026-goda_2455036/ (дата обращения: 31.08.2025).
Постановление Правительства РФ от 25.06.2003 № 367 «Об утверждении Правил проведения арбитражным управляющим финансового анализа» // КонсультантПлюс. — URL: https://www.consultant.ru/-document/cons_doc_LAW_42901/ (дата обращения: 11.07.2025).
Приказ Минэкономразвития России от 14.03.2023 № 169 «Об утверждении Методики проведения анализа финансового состояния заинтересованного лица в целях установления угрозы возникновения признаков его несостоятельности (банкротства) в случае единовременной уплаты этим лицом налога, сбора, страхового взноса, пеней, штрафов, процентов» // КонсультантПлюс. – URL: https://www.consultant.ru/cons/cgi/online.cgi?req=doc&base= LAW&n=443624 (дата обращения: 11.07.2025).
Савицкая Г.В. Анализ финансово-хозяйственной деятельности: учебник. 7-е изд., перераб. и доп. Москва: Инфра-М, 2025, 286 с.
Bernard M. Artificial Intelligence in Practice: textbook. – Wiley, 2019. – 605 p.
Gordon L.V. Springate Predicting the Possibility of Failure in a Canadian Firm: A Discriminant Analysis. – Burnaby: Simon Fraser University, 1978. – 164 p.
Pattanayak S. Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python: textbook. – Apress, 2019. – 480 p.
Список литературы
Апатова Н.В., Попов В.Б. 2020. Прогнозирования банкротства предприятий с использованием искусственного интеллекта. Научный вестник: Финансы, банки, инвестиции, 2: 113–120.
Афанасьева Е.Ю., Примакова М.В., Сагдиллаева З.А. 2023. Прогнозирование вероятности банкротства в контексте антикризисного управления организациями. Вестник Полоцкого государственного университета. Серия D. Экономические и юридические науки, 3: 6–13. DOI: 10.52928/2070-1632-2023-65-3-6-13.
Когденко В.Г. 2025. Развитие экономического анализа: прогностическая аналитика, ESG-аналитика, анализ трансформационных процессов на мезо- и микроуровнях. Учет. Анализ. Аудит, 12(2): 14–28. DOI: 10.26794/2408-9303-2025-12-2-14-28.
Кочеткова В.В., Ефремова К.Д. 2017. Обзор методов кредитного скоринга. Juvenis scientia, 6: 22–25.
Петрова Е.В., Наговицына В.П. 2023. Диагностика вероятности банкротства: зарубежные и отечественные модели прогнозирования. Вектор экономики, 12(90). DOI: 10.51691/2500-3666_2023_12_3.
Altman E.I. 1968. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23(4): 589–609. DOI: 10.1111/j.1540-6261. 1968.tb00843. x.
Beaver W.H. 1966. Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4: 71–111. DOI: 10.2307/2490171.
Fulmer J.G., Moon J.E., Gavin T.A., Erwin M.J. 1984. A bankruptcy classification model for small firms. Journal of Commercial Bank Lending, 66(11): 25–37.
Mezhov S., Krayushkin M. 2022. Comparative Analysis of Methods of Forecasting the Consumer Price Index for Food Products (on the Example of the Altai Territory). Proceedings of International Conference on Applied Innovation in IT, 10(1): 119–124.
Taffler R.J. 1983. The Assessment of Company Solvency and Performance Using a Statistical Model. Accounting and Business Research, 13(52): 295–308. DOI: 10.1080/00014788.1983.9729767.
References
Apatova N.V., Popov V.B. 2020. Predicting bankruptcy of enterprises using artificial intelligence. Scientific Bulletin: Finance, Banks, Investments, 2: 113-120.
Afanasyeva E.Yu., Primakova M.V., Sagdillayeva Z.A. 2023. Forecasting the probability of bankruptcy in the context of crisis management of organizations. Bulletin of the Polotsk State University. Series D. Economic and Legal Sciences, 3: 6-13. DOI:10.52928/2070-1632-2023-65-3-6-13.
Kogdenko V.G. 2025. Development of economic analysis: predictive analytics, ESG analytics, analysis of transformation processes at the meso- and micro-levels. Accounting. Analysis. Audit, 12(2):14-28. DOI: 10.26794/2408-9303-2025-12-2-14-28.
Kochetkova V.V., Efremova K.D. 2017. An overview of credit scoring methods. Juvenis scientia, 6: 22–25.
Petrova E.V., Nagovitsyna V.P. 2023. Diagnosis of bankruptcy probability: foreign and domestic forecasting models. Vector of Economics, 12(90). DOI: 10.51691/2500-3666_2023_12_3.
Altman E.I. 1968. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23(4): 589–609. DOI: 10.1111/j.1540-6261. 1968.tb00843. x.
Beaver W.H. 1966. Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4: 71–111. DOI: 10.2307/2490171.
Fulmer J.G., Moon J.E., Gavin T.A., Erwin M.J. 1984. A bankruptcy classification model for small firms. Journal of Commercial Bank Lending, 66(11): 25–37.
Mezhov S., Krayushkin M. 2022. Comparative Analysis of Methods of Forecasting the Consumer Price Index for Food Products (on the Example of the Altai Territory). Proceedings of International Conference on Applied Innovation in IT, 10(1): 119–124.
Taffler R.J. 1983. The Assessment of Company Solvency and Performance Using a Statistical Model. Accounting and Business Research, 13(52): 295–308. DOI: 10.1080/00014788.1983.9729767.
Abstract views: 1
Share
Published
How to Cite
Issue
Section
Copyright (c) 2025 Economics. Information Technologies
This work is licensed under a Creative Commons Attribution 4.0 International License.
