Developing a laundered funds destination theory: applying the Walker–Unger gravity model to US-based money launderer country preference from 2000 to 2020
Bibliographic record
Abstract
Purpose Although economists and academics have studied money laundering for several decades, there continues to be gaps in the research due to a lack of reliable data on money laundering activity, and a lack of detailed sources and methods of collection in government-based reporting. The purpose of this study is to apply the Walker-Unger gravity model and examine US-based money launderer preference for the 2000-2020 time frame. This paper then compares those results with previous applications of the model and identifies trends, which may serve as the foundations of a money launderer preference theory. The results of the investigation ranked countries by preference of US-based money launderers and determined that there was consistency in country destination preference even during recessionary periods. Design/methodology/approach The Walker–Unger gravity model as applied by Roman et al. (2021) is used to conduct the investigation, to maintain consistency in the application of the Walker–Unger model and further the objective of validating the attractiveness simulation. The model tests the predictive capability of the independent variables to establish the degree of attractiveness each country represents for the funds of US-based money launderers. A score is generated by the model, which is then used to analyze and interpret its significance in relation to all sampled countries. Findings Model results reveal the countries with the highest attractiveness for US-based money launderers during 2000–2020 were Australia, the Bahamas, Bermuda, Canada, Cayman Islands, Norway, Monaco, Puerto Rico, Switzerland and the USA. Model results show that over the two decades the proportion of money flow scores changed but not to a degree that would alter the country preference of US-based money launderers. US-based money launderers tended to use the same countries for their illicit financial activities, regardless of the state of the legitimate economy. Research limitations/implications One of the limitations of the model is that it does not show the effect of money laundering on legitimate economic activity. Practical implications The model results will give insight into the preferred destination of US-based money launderers and therefore frame one component of money laundering activities in the USA for the examined time period. Social implications A secondary objective of this study is to evaluate if any changes to US-based money launderer preferences occurred during the three most recent periods of economic downturn in the USA. Originality/value The model results will give insight into the preferred destination of US-based money launderers and therefore frame one component of money laundering activities in the USA for the examined time period. A secondary objective of this study is to evaluate if any changes to US-based money launderer preferences occurred during the three most recent periods of economic downturn in the USA. The periods chosen are the 2001 9/11 terrorist attacks, the 2007/08 global financial crisis and the COVID-19 pandemic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".