Law enforcement against investment fraud: a comparison study from the USA and Canada with a case study on binary options in Indonesia
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Notice bibliographique
Résumé
Purpose This study aims to propose a law enforcement strategy for investment fraud through comparative studies in the United States of America (USA), Canada and Indonesia, and to identify the factors that cause weak law enforcement on investment fraud with the object of a binary options case study in Indonesia. Design/methodology/approach This research is a type of legal research, namely, research based on legal materials (library-based). The legal materials used include primary legal materials and secondary legal materials. The approaches used are the statute approach, the case approach and the comparative approach. The data collection technique used in this research is a literature study. The analysis was carried out qualitatively by using an interactive model. Findings In 2022, the Indonesian Financial Services Authority (OJK) recorded that the total value of public losses because of investment fraud in Indonesia reached 117.4tn IDR. Weak law enforcement is the reason investment fraud thrives in society. Strategies that can be implemented to prevent investment fraud include early detection of new investment fraud modes through the whistleblower program, mutual legal assistance in criminal matters, criminal restitution and improvement of public financial literacy. Research limitations/implications This study examines the problems of law enforcement against investment fraud with a case study of binary options in Indonesia. A law enforcement strategy is built on identifying issues and adopting law enforcement policies against investment fraud in Canada and the USA. Practical implications For individuals, the results of this research can be used as reading material to increase their understanding of investment fraud. For the government, the results of this study can be a reference in an effort to eradicate the rise of investment fraud cases more effectively and create a safe digital economic space for investors. Social implications The results of this study are expected to be useful in providing recommendations for strategies to strengthen law enforcement against the problems of investment fraud cases so as to form a conducive investment climate in the sense of being safe, comfortable and profitable. Originality/value Legal frameworks to prevent investment fraud are rarely discussed. The rise in binary options cases that occur is an indication of weak law enforcement in the investment sector. Therefore, an in-depth study of law enforcement strategies to prevent investment fraud is needed, with comparative studies in the USA, Canada and Indonesia.
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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 | 0,001 | 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 | 0,003 | 0,001 |
| 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