Developing a laundered funds destination theory: applying the Walker–Unger gravity model to US-based money launderer country preference from 2000 to 2020
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».