Chasing assets abroad: Ideas for more effective asset tracing and recovery in <scp>cross‐border</scp> insolvency
Bibliographic record
Abstract
Abstract Asset tracing and recovery (ATR) has become highly challenging in the digital age, where, with the touch of computer keys, assets can be shifted through multiple jurisdictions within minutes, creating significant challenges for recovering value. While many countries have tools to enable ATR, these tools differ from jurisdiction to jurisdiction and often are not recognized across borders in a manner that keeps pace with the need for rapid ATR, particularly during insolvency. This article takes stock of the myriad ATR tools available in domestic systems to discern parameters of key ATR tools that have common objectives, features, and safeguards, and that can form the basis of more standardized understanding and application of such tools. It also explores the extent to which cross‐border ATR is aided by the leading frameworks for global, cross‐border insolvency—the UNCITRAL Model Laws on Cross‐Border Insolvency, insolvency‐related judgments, and enterprise groups—in the process, revealing gaps and uncertainties. Such uncertainties can result in losses to stakeholders affected by insolvencies of different business sizes but can be particularly detrimental in small and medium enterprise (SME) cross‐border insolvencies where there are typically more limited resources to chase assets. Against this backdrop, this article proposes ideas for the enhancement of the cross‐border insolvency framework, to allow for effective cross‐border access to information held abroad, the freezing of assets in cross‐border cases, and the cross‐border recovery of assets.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".