Moving Illegal Proceeds: Opportunities Exist for Strengthening the Federal Government's Efforts to Stem Cross-Border Currency Smuggling
Notice bibliographique
Résumé
Testimony issued by the Government Accountability Office with an abstract that begins "This testimony discusses federal efforts to stem currency smuggling across our nation's borders. Mexican drug-trafficking organizations, terrorist organizations, and other groups with malevolent intent finance their operations by moving funds into or out of the United States. For example, a common technique used for taking proceeds from drug sales in the United States to Mexico is a method known as bulk cash smuggling. The National Drug Intelligence Center (NDIC) has stated that proceeds from drug trafficking generated in this country are smuggled across the southwest border and it estimates that the proceeds total from $18 billion to $39 billion a year. NDIC also estimates that Canadian drug-trafficking organizations smuggle significant amounts of cash across the northern border from proceeds of drugs sold in the United States. In addition to bulk cash smuggling, 21st century methods and technologies of laundering money have emerged. In 2009, NDIC stated that new financial products and technologies present unique opportunities for money launderers as well as unprecedented challenges to the intelligence, law enforcement, and regulatory communities. NDIC and others cited the use of prepaid cards or gift cards that are loaded with currency or value--also called stored value--as presenting a compact and easily transportable method to move money into and out of the United States. U.S. Customs and Border Protection (CBP)--a major component in the Department of Homeland Security (DHS)--is the lead federal agency in charge of securing our nation's borders. In March 2009, the Secretary of Homeland Security called on CBP to help stem the flow of bulk cash and weapons moving south by inspecting travelers leaving the United States for Mexico--an effort called outbound operations. In addition, the Financial Crimes Enforcement Network (FinCEN)--a bureau in the Department of the Treasury (Treasury)--seeks to deter and detect criminal activity and safeguard the financial system from the risk that terrorists and other criminals may fund their operations through financial institutions in the United States. Among other things, FinCEN is responsible for administering laws aimed at preventing criminals from abusing U.S. financial systems. This testimony is based on our October 2010 report on cross-border currency smuggling and updated information on bulk cash seizures and the status of one our recommendations. Like the report, it will cover the following three issues: (1) the actions CBP has taken to stem the flow of bulk cash leaving the country through land ports of entry and the challenges that remain, (2) the regulatory gaps that exist for cross-border reporting and other anti-money laundering requirements involving the use of stored value, and (3) the extent to which FinCEN has taken action to address these regulatory gaps."
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 | 0,007 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,014 | 0,004 |
| Communication savante | 0,009 | 0,005 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,019 | 0,020 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,003 |
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 ».