Estimating the destination of Mexican-based laundered funds: an application of the modified Walker-Unger model
Bibliographic record
Abstract
Purpose This study aims to apply the modified Walker-Unger model to show the degree of attractiveness of a country for Mexican-based money launderers to send their illicit funds for the 2000–2015 time period. Design/methodology/approach The modified Walker-Unger model is used to conduct the analysis, as it combines several independent variables related to an illicit financial activity. These allow the researcher to investigate the attractiveness of a market to money launderers and the possible economic effects of money laundering. In total, 13 categories of indicators were used, namely, gross national product per capita; banking secrecy; government attitude; society for worldwide interbank financial telecommunication membership; financial deposits; conflict; corruption; Egmont group membership; language; trade; culture, colonial background; and physical distance. Findings Model results suggest the preferred destinations for Mexican-based money launderers from 2000 to 2015 were Bermuda (i.e. from 2000–2004), Canada (i.e. in 2005 and 2006) and Monaco (i.e. from 2007–2015). Research limitations/implications Timing and availability of reliable data after 2015. Practical implications Aids in continuing to empirically validate the Walker-Unger model. There is little literature on models that quantify money laundering activity. Social implications May aid policymakers in targeting anti-money laundering policy to more relevant countries. Originality/value The first empirical investigation that looks to quantify money launderer activity in Mexico. Contributes to the limited literature of quantitative investigations on money laundering.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".