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Reading: Greed, deepfakes & soiled Cash: Monetary crime enters the AI age
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Asolica > Blog > Finance > Greed, deepfakes & soiled Cash: Monetary crime enters the AI age
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Greed, deepfakes & soiled Cash: Monetary crime enters the AI age

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Last updated: January 13, 2026 6:55 pm
Admin
4 weeks ago
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Greed, deepfakes & soiled Cash: Monetary crime enters the AI age
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“Pure unmitigated greed.”

Contents
  • Coping with deepfakes
  • Utilizing AI to fight fraud

These have been the phrases that U.S. District Decide Nancy E. Brasel used on Aug. 6 when she sentenced Abdiaziz Shafii Farah to twenty-eight years in jail for his function within the Feeding Our Future case, which federal officers described as one of many largest Covid-19 fraud schemes within the nation.  

“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 informed 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 concerned the theft of greater than $250 million in federal child-nutrition funds meant to feed low-income youngsters. Almost 100 folks have been charged throughout numerous investigations and not less than 60 have been convicted.

The case ignited a broader, politically charged debate in Minnesota and prompted the Trump Administration to extend federal regulation enforcement actions within the state, administration officers stated.

The transfer has sparked protests, and tensions soared even greater following the deadly taking pictures of Renee Good, a 37-year-old Minneapolis resident and mom of three, by a U.S. Immigration and Customs Enforcement (ICE) agent.

Farah was convicted of an inventory of crimes, together with conspiracy, wire fraud, and 11 counts of cash laundering.

The case highlights the specter of cash laundering, which analysts say is on the rise, as criminals exploit new applied sciences like synthetic intelligence, cryptocurrency, and social media. 

U.S. monetary establishments reported a 168% spike in detected cash laundering accounts within the first half of 2025 in contrast with the yr prior, based on the cybersecurity firm BioCatch.

The IRS Prison Investigation Division’s report for fiscal yr 2025 recognized over $10.59 billion in monetary crimes, up 15.7% from FY 2024 outcomes, with important surges in tax fraud and cyber-related instances.


Criminals are utilizing AI of their cash laundering efforts.

Coping with deepfakes

Anti-money laundering efforts are a key aspect of company compliance, and corporations that fail in these efforts can face severe penalties.

Goldman Sachs paid $2.9 billion in 2020 for enabling cash laundering linked to Malaysia’s 1MDB fund, a government-owned sovereign wealth fund that grew to become the middle of a large worldwide monetary scandal.

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TD Financial institution paid out a file $3.1 billion in 2024 for its “staggering and pervasive money-laundering failures” that enabled worldwide drug traffickers and different criminals to launder greater than $670 million via the nation’s Tenth-largest financial institution. 

And simply on Jan. 6, Wilfredo Aquino, a former TD Financial institution worker, pleaded responsible to facilitating a cash laundering community’s motion of a whole lot of thousands and thousands of {dollars} via the financial institution’s accounts.

Amongst different steps, TD Financial institution changed all its U.S. AML management, terminated workers concerned in misconduct, and dedicated to rebuilding its AML compliance framework, together with hiring as much as 700 specialists and investing closely in new know-how and monitoring.

Some of the widespread makes use of of AI by dangerous actors is creating so-called “deepfakes” to commit fraud, the regulation agency Duane Morris stated. These AI-generated items of media vary from movie star impersonation to voice clones used to bypass voice-based authentication strategies.

“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 agency stated.

In a single case, a finance employee at a multinational agency was tricked into paying out $25 million to fraudsters utilizing deepfake know-how to pose as the corporate’s chief monetary officer in a video convention name, CNN reported in 2024.

Utilizing AI to fight 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 stated.

Hong Kong police stated the employee was duped into attending a video name with what he thought have been a number of different members of workers, however all of whom have been, in reality, deepfake recreations.

Associated: How cyber warfare is turning into the primary strike in fashionable conflicts

Criminals additionally use AI to systematically break massive sums of cash into 1000’s of small, time-distributed transactions — a apply generally known as structuring — throughout a number of accounts to keep away from triggering anti-money laundering (AML) purple flags.

“In this rapid-fire digital transaction world, fraud is the new mugging, complete with racketeering and slave labor farms,” stated Rio Miner, founder and CEO of Monetary Crimes Intelligence Tradecraft. 

Cartels, underground banking networks, and bonafide companies now collaborate — typically unwittingly — to launder cash by shifting worth via mirror-trade commodity flows and cryptocurrency, merging authorized commerce with unlawful income.

AI can also be getting used to fight cash laundering.

“AI-driven transaction monitoring is essential for financial institutions to stay ahead of evolving criminal tactics and regulatory demands,” the accounting and auditing agency EY stated in a Nov. 12 analysis word. 

“Traditional systems, though foundational, must be complemented with advanced technologies like AI and machine learning to improve efficiency, accuracy and adaptability.”

And machine studying fashions can sift via huge datasets to detect patterns and anomalies indicative of suspicious actions. 

“These systems can continuously learn and adapt based on new data, allowing for more dynamic and responsive monitoring,” EY stated.

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