Info-Communicative and Protective Function of the State as Combating Fraud using Sberbank Bank Cards
Bibliographic record
Abstract
Financial crimes are defined as unfair activities that have become widespread in banking structures. The activities of financial fraudsters often have negative consequences before public rules are created that prohibit them. Intensive transformation processes in financial markets, their automation and virtualisation, the spread of remote interaction between banks and their clients, the influence of unauthorised persons on the software and hardware systems of banks, an increase in the number of cases and trading volumes determine the relevance of clarifying the essence of this phenomenon and the peculiarities of its manifestation in banking structures. The novelty of the study is determined by the fact that financial violations can be represented both in the structure of the current activities of banks and the process of interaction with clients and in the structure of expanding the list of services provided. The leading method to study this problem is the method of analysis, which allows to identify and comprehensively consider ways to counter financial crimes in banks to improve the level of financial security. The authors show that structurally, one should take into account, first of all, countermeasures on the part of customers, which often serve as a source of obtaining personal data. In this case, the state function is considered only as a security function for the purpose of possible punishment for fraudulent actions. The practical significance of the study is determined by the possibilities of structural implementation of combating financial fraudulent actions in the context of the development of the information society.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 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".