Banking Services Improvement through the Development of Service Technologies
Bibliographic record
Abstract
The authors analyze the qualitative changes in the nature and orientation of modern society, which led to theemergence of the phenomenon of “customization” and analyze the features of a modern “service of civilization”.The authors use the technique of ABC-and XYZ-analysis to assess the assortment policy in banks. The articledefines the external factors and the theoretical and methodological background of innovative changes in thebanking market. The article also discusses the most promising innovative service technologies in the bankingmarket and the examples of their implementation of Russian and foreign banks. To identify the maincharacteristics of banking products to help meet customer demand, the authors are guided by the model N. Cano,who formulated the “theory of attractive quality” and highlighted the major kinds of needs. Based on analysis oftrends in the global economy, the authors concluded the growing role of the Internet in the development ofservice technology in the banking market and the need for further use of its opportunities for improvement ofbanking services. This paper investigates the key challenges and prospects for the development of servicetechnologies and their impact on the development of banking services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".