Crypto Asset Insurance for Physical Trading of Crypto Assets on the Crypto Asset Futures Exchange
Bibliographic record
Abstract
Crypto assets are now recognized as commodities on the Crypto Asset Futures Exchange, and in creating a system to enable trading of crypto assets, the Commodity Futures Trading Regulatory Agency has provided certain guidelines for the parties involved to ensure that trading can be carried out without problems. However, the Commodity Futures Trading Supervisory Agency does not provide specifications regarding crypto asset insurance, which means that insurance companies must make crypto asset insurance in accordance with existing laws and regulations, namely the Commercial Law Book, Law Number 40 of 2014 concerning Insurance , as well as the Financial Services Authority Regulations as the body that regulates insurance. Thus, this paper aims to find out how crypto asset insurance will be regulated, as well as how it will be implemented, by assessing existing insurance laws and the implementation of crypto asset insurance in the United States, United Kingdom, and Canada. This paper uses normative legal research because it will mostly be based on doctrine, existing laws and other legal documents. Before being marketed, the insurance product itself must meet the requirements set out in Financial Services Authority Regulation Number 23 of 2015 and Financial Services Authority Circular Letter Number 13 of 2016 concerning Insurance Product Reports for Insurance Companies
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".