The Specifics and Patterns of Cybercrime in the Field of Payment Processing
Bibliographic record
Abstract
In the modern world, cybercrime in the field of payment processing as a phenomenon is developing rapidly. Highly developed, developing and least-developed states become victims of cyberattacks. The purpose of this study is to analyze the experience of the international community and a number of states in combating cybercrime in the field of payment processing. International and regional (on the example of the Council of Europe) legal regulation of the fight against this type of crime were analyzed. The data on the size of losses caused by cybercrime to the world economy are analyzed according to the latest report from the Center for Strategic and International Studies for 2018, the World Economic Forum for 2019, DLA Piper GDPR for the period January-April 2020. Besides, using the example of the Russian Federation, quantitative indicators of the growth of cybercrime and the level of its detection for the period from 2018 to April 2020 were investigated. Comparison of the experience of individual states and its analysis made it possible to single out the best possible measures to counter cybercrime in the field of financial processing. The necessity of interstate cooperation to counter cybercrime in the field of payment processing is indicated. However, due to the presence of significant differences in the legal systems of all states, it is proposed to interact within the framework of regional communities with gradual transfer to international interaction. The priority is given to precisely preventive measures to counter cybercrime in the field of payment processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".