The Impact of Digitalization on Performance Indicators of Russian Commercial Banks in 2021
Bibliographic record
Abstract
One of the main trends in the development of the financial sector around the world is digitalization. The purpose of this study is to analyze the interdependence between the level of digitalization and the key performance indicators of commercial banks, as well as the prospects for further development of digital technologies and their implementation in the activities of commercial banks. Based on the analysis of statistical data, it was confirmed that the digitalization of the Russian banking sector has significant potential. A correlation analysis of the data of 100 Russian commercial banks for 2021, grouped by assets, was performed. The presence of the influence of the level of digitalization on the individuals’ transactions and on the net commission income was confirmed. Hypotheses about the existence of a close relationship between the level of digitalization and the volume of transactions with legal entities, as well as profitability, have not been confirmed. According to the results of the study, it was noted that digitalization currently has the greatest impact on large Russian banks. It was concluded that currently, for the largest and big banks, a high level of digital maturity is a competitive advantage. This research contributes to the development of the theory of modern banking. The results obtained will be useful for researchers of the impact of digitalization on various aspects of banks’ activities, for banks, and for public authorities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".