Bibliographic record
Abstract
The purpose of the article is to study trends in transactions with payment cards issued by Ukrainian banks during a period when the country's economy is surviving in conditions of full-scale hostilities and to identify directions for the further development of payments by payment cards. Methodology of research. The goal set in the article was achieved using such research methods as analysis and synthesis, comparison, summarization and grouping, scientific abstraction, and economic and mathematical forecasting. Findings. An analysis of scientific opinion was conducted, and the conclusion was made that the obvious advantages of payment cards for all entities of monetary circulation led to the active development of the market for these payment instruments in most countries, including Ukraine. The dynamics of the number of issued and active payment cards for 2023 - 1st quarter of 2025, transactions using cards in Ukraine and abroad during this period were studied, and the main directions in which payments using this payment instrument are developing today were identified. An analysis of the volume of payment card transactions showed a 43% increase in the first quarter of 2025 compared to the first quarter of 2023, which was facilitated by the increase in financial literacy of the population, which realized the advantages of card payments and the further development of digitalization of the banking sector of Ukraine. It is noted that the main trend in the development of the payment card market in Ukraine over the period under review is the constant growth of non-cash card payments. It is explained that a significant number of Ukrainians continue to be forced to stay outside the country and therefore the volume of payment card transactions outside Ukraine is increasing. It was determined that payments with contactless and tokenized payment cards are growing particularly rapidly due to the development of digital technologies in banking. The main directions in which payments using this payment instrument are developing today have been identified. It has been concluded that with the development of digitalization of banking technologies, the volume of non-cash payments using payment cards is increasing, with the number of contactless and tokenized cards gradually increasing. Practical value. Using formulas developed by software tools that predict the number of contactless and tokenized payment cards, it is possible to calculate the volume of payment card payments for 2025 - 2026.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".