US Treasury Seeks Public Input on Tools to Detect Crypto Money Laundering

The U.S. Treasury Department is seeking public feedback on innovative methods to detect crypto money laundering, following requirements under the recently enacted GENIUS Act.

The 60-day comment period, ending October 17, focuses on artificial intelligence, blockchain monitoring, digital identity verification, and application programming interfaces as potential tools for regulated financial institutions to combat illicit digital asset activities.

The request comes as crypto criminals accelerated their operations in 2025, with $3 billion stolen in 119 separate incidents during the first half alone.

Treasury Secretary Scott Bessent praised the GENIUS Act implementation as “essential” to securing American digital asset leadership while expanding dollar access globally through regulated stablecoin frameworks.

The U.S. Treasury is calling on the public for feedback on how financial institutions can prevent crypto risks as part of the GENIUS Act. #Treasury #GENIUSActhttps://t.co/7Bu5ExndQt

Recent blockchain analytics reveal the staggering speed advantage that crypto criminals maintain over traditional security responses.

Global Ledger’s comprehensive study found that hackers moved funds in just four seconds following the fastest recorded attack, approximately 75 times faster than average exchange alert systems can respond.

In over 68% of cases, attackers moved stolen funds before the incidents became publicly known, with one in four hacks completely laundering assets before any public statements or alerts were issued.

The fastest complete laundering process from initial breach to final deposit took just 2 minutes 57 seconds, faster than typical laptop screen timeouts.

Speaking with Cryptonews, Mitchell Amador, CEO of security platform Immunefi, has previously emphasized the economic incentive imbalance.

“Most hackers today realize that keeping stolen crypto is more trouble than it’s worth due to better on-chain forensics and very real reputational and legal risks of holding marked funds,” he said.

However, prevention remains critical as recovery rates continue to be dismally low.

Only 4.2% of stolen funds were recovered during the first half of 2025, with sophisticated actors like North Korea’s Lazarus group planning movements to coincide with normal transaction activity around noon when organizations experience staff transitions and reduced vigilance.

Artificial intelligence and machine learning emerge as crucial weapons in the anti-money laundering arsenal.

Earlier this year, researchers from Elliptic, IBM Watson, and MIT successfully developed deep learning models that detect money laundering patterns by analyzing “subgraphs” – chains of transactions representing Bitcoin laundering activities across over 200 million transactions.

New Elliptic research released today explores how #AI can be leveraged to detect money laundering and other financial crime on the blockchain. The research applies new techniques to a dataset containing 200m+ transactions, which is now publicly available. https://t.co/k3GdjWJ08P

“Unlike traditional finance, where transaction data is typically siloed making it challenging, blockchain provides transparency to apply these techniques,” Elliptic noted in their breakthrough research that focuses on multi-hop laundering processes rather than specific illicit actor behaviors.

Similarly, automated recovery systems are revolutionizing incident response timelines.

For instance, Circuit’s technology embeds pre-signed fallback transactions that execute automatically upon threat detection, moving assets to secure vaults before attackers can complete their operations.

“Circuit changes this timeline by embedding automatically executable recovery into a platform’s infrastructure,” explained Harry Donnelly, founder and CEO of Circuit.

“Before any breach, users create pre-signed fallback transactions with precise recovery instructions that broadcast instantly while attackers are still in motion.“

Traditional security approaches face fundamental limitations in decentralized environments.

Amador identified three critical blind spots: “Static audits that rely on one-time checks, ignoring incentives that underestimate Web3’s open-ledger attack appeal, and lack of Web3 expertise missing composability or oracle risks.“

The Treasury’s focus on application programming interfaces, artificial intelligence, and blockchain monitoring aligns with industry recognition that “security swarms” – automated defense networks – represent the future of crypto protection.

These systems compress intervention windows from hours to seconds, fundamentally shifting the balance toward defenders.

Notably, oracle manipulation has emerged as an under-discussed attack vector that industry experts believe deserves greater attention.

“Attackers can exploit weak data feeds to trick contracts, draining funds or destabilizing stablecoins,” warned Amador.

“Protocols need multi-oracle redundancy and targeted bounties, but many overlook this critical single point of failure.”

The GENIUS Act’s regulatory framework provides legal clarity that executives across the industry consider transformative.

Ian De Bode, Chief Strategy Officer at Ondo Finance, has earlier called the legislation “the beginning of a new regulatory era,” noting that “the clearer the rules, the faster adoption will follow.”

Looking forward, Treasury’s aim to collect public input on anti-money laundering technologies stems from the crypto industry’s ongoing arms race, where criminal innovation consistently outpaces defensive capabilities.

As a result, advanced AI detection and automated response systems are becoming essential for protecting the growing digital asset ecosystem.

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