Chinese National Pleads Guilty to Conspiracy to Commit Money Laundering for Drug Cartels

June 30, 2020 Updated: July 1, 2020

A Chinese national pleaded guilty on Monday to conspiracy to commit money laundering in connection with laundering more than $4 million in drug proceeds generated by large-scale cocaine trafficking in the United States.

Xueyong Wu, 40, had relationships with Latin American drug cartels and laundered their U.S.-based proceeds generated through the movement of cocaine or payment for cocaine to appear as if the money came from legitimate sources, the Department of Justice said in a statement.

Wu repatriated the funds illegally obtained by Latin American drug trafficking organizations to Mexico by passing them through a complex series of financial transactions to hide their criminal origin, according to the statement.

He was given a percentage of the money he laundered as compensation for organizing these shady dealings. Much of the money he handled came from cocaine trafficking within the Eastern District of Virginia, the department said.

U.S. District Judge Leonie Brinkema accepted Wu’s plea, according to a statement by U.S. Attorney of Eastern District of Virginia.

Wu is scheduled to be sentenced on Sept. 29, and faces a maximum penalty of 20 years in prison, said the U.S. Attorney’s statement. A federal district court judge will determine the actual sentence but sentences for federal crimes are typically less than the maximum penalties, according to this statement.

Wu, known as “Antonio,” was also indicted on participation in cocaine trafficking with actors from Mexico, Colombia, Guatemala, Belize, China, and the United States, according to court documents. However, these charges were dropped after Wu pleaded guilty to money laundering, reported Borderland Beat.

Money Laundering

Under the Bank Secrecy Act, banks are obligated to implement measures to detect and report to the government money laundering schemes, according to the Department of Treasury.

Information technology powered by artificial intelligence, machine learning, and big data provides effective tools to analyze financial transactions that can detect money laundering patterns.

Money laundering is typically a three-step process, as explained by the Financial Action Task Force, an international money laundering and terrorist financing watchdog established by G-7 countries.

The first step, called placement, is to put illicit profits into the legitimate financial system. It can be accomplished via bank deposits, money transfers, purchasing money orders, or funneled via a business owned by a criminal organization like a casino, for example.

Once the funds enter the financial systems, a technique called layering is used to hide the source of money. The placed funds can then be transferred several times between banks in several countries, or used for loans.

In the third phase, called integration, the money enters the legitimate economy through the purchase of luxury assets or investment.