On the Introduction of the Foreign Trust Reporting Rule in Korea
Bibliographic record
Abstract
Foreign trusts can be used as a means of tax avoidance to hide foreign－source income from being tracked by tax authorities. It is difficult for authorities to obtain information on the establishment, operation, and termination of foreign trusts, as both the assets and the trustee are outside their jurisdictions. In addition, Korea currently does not operate a system that collects, identifies, and directly regulates information on the use of offshore trusts by residents and domestic corporations at the government level. Due to this policy loophole, there is a room for Korean residents and domestic corporations to hide their offshore assets and to avoid tax obligations in Korea by using foreign trusts. In addition, as foreign economic activities increase and domestic financial and tax advisory businesses are advanced, the use of offshore tax havens increases, and tax avoidance cases using foreign trusts are expected to increase. Therefore, this study examines the risk of using foreign trusts for tax avoidance and discusses the necessity and the guideline to introduce the foreign trust reporting rule in Korea. Specifically, this study examines the domestic rules related to foreign trusts, the current status of Korea’s foreign financial account reporting system, the domestic and foreign tax avoidance cases using foreign trusts, and the details of foreign trust reporting systems in major countries such as the United States, France, and Canada. This study also summarizes policy implications for the introduction of the foreign trust reporting rule. In addition, this paper proposes a plan to make it mandatory to report foreign trusts by expanding the foreign financial account reporting system under the International Tax Adjustment Act.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".