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October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.
October 7, 2026
‘Our Team Consists of True Leaders in Their Respective Academic Disciplines
The HSE International Centre of Decision Choice and Analysis studies a wide range of methods for analysing decision-making and possible scenarios for the development of natural, socio-economic, and political phenomena using various mathematical models. The application of advanced mathematical methods to forecasting helps to prevent negative outcomes and avoid erroneous decisions. The HSE News Service spoke to the centre’s director, Prof. Fuad Aleskerov, about its work.
October 6, 2026
International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod Brings Together Scientists from Russia and Serbia
The International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod’ was held at the Nizhny Novgorod House of Scientists from September 23 to 26. The event was organised by HSE University–Nizhny Novgorod and the Nizhny Novgorod House of Scientists, with the participation of Sberbank and the Institute of Physics Belgrade. The symposium was held for the second time: the first conference took place in 2025 and attracted considerable interest from the academic community.

 

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О применении методов машинного обучения для оценки рисков лизинговой компании

Социальные и экономические системы. 2024. № 3(53). С. 152–169.
Чудинова О. С., Ramenskaya A.

 The article examines the practical application of machine learning methods to address the issue of assessing the lessee solvency. The first stage comprises BPMN 2.0 business process modelling for leasing transactions. The second stage envisages the construction of two interpretable models (logistic regression and classification tree) to assess the lessee solvency by means of Python libraries. The models were built taking into account the amount and duration of the leasing agreement, the type of equipment, the legal form of the lessee economic activity and the existence of other contracts. The paper compares the quality of the models and interprets the results. The classification tree model outperforms in terms of quality criteria, whereas logistic regression allows quantifying the impact of the lessee characteristics and predicting the probability of lessee arrears. Based on the findings, the article provides recommendations for the practical application of machine learning models to assess the risk of overdue lease payments. 

Research target: Economics and Management Mathematics
Language: Russian
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Keywords: лизингмашинное обучениеоценка рискалогистическая регрессиядерево решений
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