Financial Sustainability Assessment of the Largest Systemically Important Credit Institutions in the Context of the Global Instability
Bibliographic record
Abstract
The national banking system is the driver for the national economy that unites various types of credit organizations that operate within a single monetary mechanism. The banking system is a part of the economic “organism”, whose condition determines the stable development of society. The problems that currently exist in the banking sector reflect instability of the entire economic situation in the country. The reasons are a reduction in budget support for organizations and the inability of some of them to adapt to changing external conditions. In crisis conditions, it is of particular interest to assess the financial sustainability of the activity of the largest systemically important banks in the country, which are the “circulatory system” of the national economy. This article assesses the financial stability of PJSC “Sberbank of Russia” based on an analysis of the main groups of its performance indicators for 2007-2019: capital adequacy, asset quality, management efficiency, profitability and liquidity. According to the research results, it is revealed that during the period under review, the activity of Sberbank is stable with respect to such indicators as capital adequacy, profitability, management efficiency and liquidity. Bank activity is unstable relative to asset quality indicators. The high value of the asset quality ratio characterizes the increased degree of riskiness of operations conducted. The ratio of overdue debt is above the norm, which adversely affects the financial stability of the bank. The most important achievement of Sberbank of Russia in 2019 - the launch of a new digital platform of the bank. The use of artificial intelligence technologies has already become an important driver of Sberbank business. Due to the pandemic of COVID-19, the Russian banking sector may face a number of problems. By 2021-2022, the growth is expected only by those banks that will build an effective risk management system and will be able to adapt their business strategies to the new economic realities and tougher requirements of the regulator.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".