Journal of Applied Research

Журнал прикладных исследований

2712-7516 2949-1878

113605

10.26118/7235.2025.12.88.046

ПЕДАГОГИКА. ПЕДАГОГИЧЕСКИЕ НАУКИ.

PEDAGOGY. PEDAGOGICAL SCIENCES.

ПЕДАГОГИКА. ПЕДАГОГИЧЕСКИЕ НАУКИ.

Risk models in banking

Риск-модели в банкинге

Яворский

Станислав Станиславович

Yavorsky

Stanislav Stanislavovich

Московская международная академия Москва Россия Moscow International Academy Moscow Russian Federation

23 01 2026

344-351

21 01 2026

https://zhpi.ru/en/nauka/article/113605/view

Статья рассматривает банковскую деятельность как многоуровневую систему финансовых услуг для домохозяйств, корпоративных и публичных субъектов, что обусловливает сложный и взаимосвязанный профиль рисков. На основе анализа теоретических и нормативных источников обобщается классификация банковских рисков: кредитные, рыночные, нефинансовые и ликвидностные, а также их трансмиссионные связи, усиливающие уязвимость в стрессовых режимах. Особый акцент делается на ключевых параметрах моделей кредитного риска — вероятности дефолта заемщика, доле потерь при неплатежеспособности и величине невыбранных обязательств/кредитной экспозиции, на методах их оценки (от интерпретируемых статистических подходов до ансамблей методов интеллектуального анализа данных), а также на увязке с макроэкономическими сценариями для расчета ожидаемых и неожиданных потерь и определения требуемых капитальных резервов. Показано, что повышение точности прогнозов за счет методов автоматизированного обучения требует усиления процедур объяснимости, устойчивости и управления риском, связанным с применением моделей.

The article considers banking activity as a multi-level system of financial services for households, corporate and public entities, which determines a complex and interconnected risk profile. Based on the analysis of theoretical and regulatory sources, the classification of banking risks is summarized: credit, market, non-financial and liquidity risks, as well as their transmission links that increase vulnerability in stress modes. Special attention is paid to the core of credit risk modeling, including PD, LGD, and CCF/EAD parameters, as well as methods for their estimation (from interpretable statistical models to machine learning ensembles) and their integration with macroeconomic scenarios for calculating expected/unexpected losses and capital buffers. It is shown that improving the accuracy of forecasts through automated learning methods requires strengthening the procedures for explainability, sustainability, and risk management associated with model use.

банковская деятельность риск – моделирование банковские риски цикл разработки финансовые услуги

banking activities risk modeling banking risks development cycle and financial services

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