ETH’s Shanghai upgrade made it easier to detect criminals
As a result of the Merge event in September, Ethereum has become a proof-of-stake blockchain. In the current system, validators stake their Ether (ETH) in order to confirm transactions. The March upgrade, codenamed Shanghai, finally allowed stakes to withdraw locked ether.
The “investment themes” of the Ethereum ecosystem have included a) decentralized finance (DeFi), b) stablecoins, c) Bitcoin (through wrapped versions of BTC), and d) non-fungible tokens (NFTs). With the upgrade, the network started offering fixed-income assets.
Risk-free rate
Yield is a key component of traditional finance (TradFi). The perceived risk of other financial assets rises or falls in response to changes in yield. Thus, variations in the benchmark rate set by the Federal Reserve of the United States give the justification for investment decisions in general.
As a result, compliance experts employ risk-free rate trends to detect irrational fund movement in capital markets, as such fund flows may constitute attempts to launder money. The explanation is that money launderers do not actively seek financial returns like regular investors because the main objective of money laundering is to conceal the trail of dirty money. The Shanghai upgrade may have improved the state of crypto forensics, with Ethereum’s staking yield reflecting the “risk-free rate” of the crypto ecosystem.
TradFi forensics focuses on activity — crypto forensics focuses on entities
In TradFi, financial crime risk is controlled using automated systems that notify institutions of the unlawful use of financial assets. While data scientists create and implement algorithms to detect suspicious transactions, investigative teams must still review leads and determine whether Suspicious Activity Reports (SARs) are required.
One important difference between TradFi and crypto forensics is that the latter focuses on the illegal entity rather than the behavior itself. In other words, investigators examine crypto wallet networks to identify criminal asset transactions.
Ethereum’s staking rewards make it easier to detect unusual activity
To create ways to identify layering, designers must think like criminals, who create intricate flows of funds to hide the money trail. The tried-and-true method for detecting such conduct is to look for irrational asset movement. This is because the purpose of money laundering is not to make money.
We can create baseline risk-reward structures using Ether’s post-Shanghai staking yields as benchmark interest rates for crypto. With this information, investigators can systematically identify financial behaviour that runs counter-intuitively to patterns in the benchmark rate.
Financial crime and DeFi
Traditional capital markets are frequently utilised to move funds clandestinely in order to avoid sanctions and finance terrorist activity. DeFi networks, similarly, present an appealing target for financial crime due to the potential to move large sums of assets between states utilising blockchain.
Furthermore, as a result of recent disasters such as the demise of FTX, there has been a major movement in activity from centralized exchanges to decentralized exchanges. The rise in DeFi volumes has made it simpler for illegal flows to go undetected.
Even more persuasive is the implementation of improved compliance procedures by centralized crypto service providers, which are frequently enforced by regulators and are likely forcing criminals to seek out new money-laundering methods.
As a result, unlawful flows to DeFi could stem from a broader range of offenses. Because of the paradigm shift in crypto markets, forensics teams will need to improve their ability to investigate complex money movements across multiple protocols without prior knowledge of the source of illegal assets.
As a result, compliance activities must center on the identification of layering typologies. Indeed, with the rapid advancement of blockchain interoperability, systematic monitoring to detect illegal transfers has become even more critical.
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