“Pure unmitigated greed.”
Those were the words that U.S. District Judge Nancy E. Brasel used on Aug. 6 when she sentenced Abdiaziz Shafii Farah to 28 years in prison for his role in the Feeding Our Future case, which federal officials described as one of the largest Covid-19 fraud schemes in the country.
“You achieved successes here in the United States and yet you’ve shown utter and flagrant disregard for the laws of the United States,” Brasel told Farah, who was born in Somalia. “The repercussions of your crime will be felt in Minnesota and in your community—the refugee community—for a long time.”
The scheme involved the theft of more than $250 million in federal child-nutrition funds intended to feed low-income children. Nearly 100 people have been charged across various investigations and at least 60 have been convicted.
The case ignited a broader, politically charged debate in Minnesota and prompted the Trump Administration to increase federal law enforcement actions in the state, administration officials said.
The move has sparked protests, and tensions soared even higher following the fatal shooting of Renee Good, a 37-year-old Minneapolis resident and mother of three, by a U.S. Immigration and Customs Enforcement (ICE) agent.
Farah was convicted of a list of crimes, including conspiracy, wire fraud, and 11 counts of money laundering.
The case highlights the threat of money laundering, which analysts say is on the rise, as criminals exploit new technologies like artificial intelligence, cryptocurrency, and social media.
U.S. financial institutions reported a 168% spike in detected money laundering accounts in the first half of 2025 compared with the year prior, according to the cybersecurity company BioCatch.
The IRS Criminal Investigation Division’s report for fiscal year 2025 identified over $10.59 billion in financial crimes, up 15.7% from FY 2024 results, with significant surges in tax fraud and cyber-related cases.
Criminals are using AI in their money laundering efforts.
Dealing with deepfakes
Anti-money laundering efforts are a key element of corporate compliance, and companies that fail in these efforts can face serious penalties.
Goldman Sachs paid $2.9 billion in 2020 for enabling money laundering linked to Malaysia’s 1MDB fund, a government-owned sovereign wealth fund that became the center of a massive international financial scandal.
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TD Bank paid out a record $3.1 billion in 2024 for its “staggering and pervasive money-laundering failures” that enabled international drug traffickers and other criminals to launder more than $670 million through the nation’s 10th-largest bank.
And just on Jan. 6, Wilfredo Aquino, a former TD Bank employee, pleaded guilty to facilitating a money laundering network’s movement of hundreds of millions of dollars through the bank’s accounts.
Among other steps, TD Bank replaced all its U.S. AML leadership, terminated employees involved in misconduct, and committed to rebuilding its AML compliance framework, including hiring up to 700 specialists and investing heavily in new technology and monitoring.
One of the most popular uses of AI by bad actors is creating so-called “deepfakes” to commit fraud, the law firm Duane Morris said. These AI-generated pieces of media range from celebrity impersonation to voice clones used to bypass voice-based authentication methods.
“AI has upped the game in more traditional scams and schemes in business e-mail compromises, ransomware attacks, imposter scams, investment club scams, new account fraud, account takeovers, and market manipulation,” the firm said.
In one case, a finance worker at a multinational firm was tricked into paying out $25 million to fraudsters using deepfake technology to pose as the company’s chief financial officer in a video conference call, CNN reported in 2024.
Using AI to combat fraud
“The technological capabilities of AI allow for creation of content that is increasingly difficult to distinguish realistic deepfakes to what appear to be real events and real people,” Duane Morris said.
Hong Kong police said the worker was duped into attending a video call with what he thought were several other members of staff, but all of whom were, in fact, deepfake recreations.
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Criminals also use AI to systematically break large sums of money into thousands of small, time-distributed transactions — a practice known as structuring — across multiple accounts to avoid triggering anti-money laundering (AML) red flags.
“In this rapid-fire digital transaction world, fraud is the new mugging, complete with racketeering and slave labor farms,” said Rio Miner, founder and CEO of Financial Crimes Intelligence Tradecraft.
Cartels, underground banking networks, and legitimate businesses now collaborate — sometimes unwittingly — to launder money by moving value through mirror-trade commodity flows and cryptocurrency, merging legal trade with illegal profits.
AI is also being used to combat money laundering.
“AI-driven transaction monitoring is essential for financial institutions to stay ahead of evolving criminal tactics and regulatory demands,” the accounting and auditing firm EY said in a Nov. 12 research note.
“Traditional systems, though foundational, must be complemented with advanced technologies like AI and machine learning to improve efficiency, accuracy and adaptability.”
And machine learning models can sift through vast datasets to detect patterns and anomalies indicative of suspicious activities.
“These systems can continuously learn and adapt based on new data, allowing for more dynamic and responsive monitoring,” EY said.
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