Аssessment of the convergence level of the cyber security system and counteraction of money laundering
Bibliographic record
Abstract
The growth of financial and cyber fraud leads to the destabilization of the country's financial sector and negatively affects the development of their economy, which requires the development and implementation of effective tools and measures at the level of public administration. The convergence of the cybersecurity system and counteraction of money laundering and terrorist financing is a promising area in the fight against financial fraud. The subject of research in the article is a scientific and methodological approach to forming integrated indicators for assessing the state of various systems, which is based on the Harrington - Mencher function. The aim is to determine the level of potential convergence of the cybersecurity system and counteraction of money laundering and terrorist financing based on the definition of their integrated indicators and the application of the Harrington-Mencher function. Objectives: to form a base of factors for evaluation; to carry out their normalization by applying nonlinear normalization; to transform the normalized values of the selected indicators of the research base to the dimensionless scale of Harrington's desirability; identify the function type of the dependence of the intermediate indicator value to assess the level of convergence of the cybersecurity system and combating financial fraud, from their actual values; calculate indicators to formalize the Harrington-Mencher transformation; to determine weight indicators using canonical analysis; to calculate integrated indicators that characterize the level of development of the cybersecurity system and counteraction to money laundering, as well as to determine the level of systems convergence. The article uses general scientific methods: system analysis - to determine the factors that characterize cybersecurity systems and combat financial fraud; Harrington-Mencher method of preference and function during integrated evaluation. The following results were obtained: in terms of cybersecurity, the highest scores are given to economically developed countries - European countries, the United States, Canada, Australia, New Zealand, Japan. Other countries have many problems in this area, as evidenced by their assessments of "very poor", "poor" and "satisfactory". The level of opposition to money laundering has shown that this area is critical for countries with high levels of crime, terrorism, military conflicts and high levels of financial secrecy, making them potential actors in money laundering. It is also established that due to the convergence of the two systems, the country's level of development will increase. Conclusions: the results of the study should be taken into account in the process of developing a strategy for the convergence of the cybersecurity system and combating financial fraud at the macro level.
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 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".