Bank Deposit and Credit Policy Management in the Field of Individual Customer Service
Bibliographic record
Abstract
The article is devoted to the problem of individual customer service quality improvement in the field of provided deposit and credit services. This problem is a priority in determining any commercial bank strategy, since both deposit and credit policies determine the effectiveness of a credit institution development, which largely depends on the level of customer satisfaction. This study aims to determine the management aspects of the deposit and credit policy improvement in the field of commercial bank customer servicing. In the course of the work, they used the elements of system analysis, statistical research methods (summary and grouping, calculation of average values), and SWOT analysis. To assess customer satisfaction, the authors studied the Internet reviews of the largest regional bank of the Primorsky Territory of Russia - PJSC SKB of Primorye "Primsotsbank". The use of the indicated methods in the study made it possible to assess the quality of services provided to clients, identify the problems in their service sector, and develop the measures for their elimination. The results obtained are the basis for making managerial decisions to improve the deposit and credit policy of the studied bank and can be used in commercial banks' practice.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".