Moving Illegal Proceeds: Opportunities Exist for Strengthening the Federal Government's Efforts to Stem Cross-Border Currency Smuggling
Bibliographic record
Abstract
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."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".