Cryptocurrency-crime Investigation: Fraudulent use of Bitcoin in a Divorce Case
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bitcoin and cryptocurrency adoption has increased significantly over the past few years. The significant growth in the industry has been matched by growth of crimes in this domain; not only in scams and dark-web illegal trading, but also in white-collar crimes with fraud and perjury occurring increasingly. With blockchain technology, the world of financial infidelity has become increasingly sophisticated. There is a common belief that blockchain and cryptocurrency provide means of hiding funds from the public or close associates who may not be familiar with the technology. The rise of cryptocurrency has also led to spouses hiding digital assets during divorce settlements. This study presents a use case of a couple in the midst of a divorce where one of the spouses was accused of perjury for failure to declare bitcoin holdings, obtained via Bitcoin mining, and possibly other forms of cryptocurrency and digital assets to the court. The plaintiff is entitled to fifty percent of all assets. While property, stocks, bonds, and bank accounts can easily be traced, cryptocurrency assets are more complex to trace but it is not impossible. This paper illustrates how such a case can be investigated by following the flow of funds on the blockchain, using tools such as Maltego and QLUE. The paper thus presents an investigative process that can be followed for a new category of forensic investigation.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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