Аssessment of the convergence level of the cyber security system and counteraction of money laundering
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Notice bibliographique
Résumé
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.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle