Law enforcement against investment fraud: a comparison study from the USA and Canada with a case study on binary options in Indonesia
Why this work is in the frame
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Bibliographic record
Abstract
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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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 | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it