The implementation of Ethereum’s Shanghai upgrade and deployment rewards had a significant impact on the detection of suspicious behavior by Ethereum (ETH) holders. The upgrade marked Ethereum’s transition to a proof-of-stake blockchain, where validators use their ETH to confirm transactions.
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The Shanghai upgrade, implemented in March, allowed stakers to withdraw their locked Ether, giving them more flexibility and accessibility
Several “investment themes” have emerged within the Ethereum ecosystem, including decentralized finance (DeFi), stablecoins, Bitcoin (via packaged versions of BTC), and non-fungible tokens (NFTs). The latest update also introduced fixed-income assets, expanding the ways individuals can make money or use Ethereum.
Yield plays a critical role in traditional finance (TradFi), where changes in yield affect the perceived risk of other financial assets. This relationship between yield and investment decisions can be observed through movements in benchmark interest rates set by institutions such as the Federal Reserve.
Compliance professionals in traditional finance rely on trends in risk-free interest rates to identify irrational money flows in capital markets that may indicate money laundering attempts. Money launderers do not actively seek financial gains like normal investors, as their main goal is to disguise the origin of illicit funds.
With Ethereum’s deployment yield acting as a “risk-free interest rate” for the crypto ecosystem, the Shanghai upgrade may have improved the state of crypto-forensics. In traditional finance, forensic analysis focuses on detecting suspicious activity, while crypto-forensics focuses on identifying entities involved in criminal behavior through the analysis of crypto-wallet networks.
How can the Shanghai upgrade help with money laundering, which typically occurs in three phases:
Placement, Layering, and Integration. For crypto-assets, detecting the placement of illicit funds is relatively easy, as many money laundering cases result from crypto-native crimes such as ransomware attacks, DeFi bridge hacks, smart contract exploits, and phishing attacks. The addresses associated with such crimes are often known, allowing investigators to monitor asset flows.
In contrast, traditional forensic experts lack insight into the crime itself when proceeds of crime enter a bank’s ecosystem. This makes detection much more difficult. As a result, most anti-money laundering (AML) solutions in traditional finance focus on identifying layering techniques.
Ethereum’s deployment premiums provide the ability to detect unusual activity by examining asset movements. Since AML is more about hiding the trail of money than making a profit, investigators can identify financial behavior that runs counter to benchmark interest rate trends. For example, persistently risky activities with returns below the risk-free rate may arouse suspicion and trigger investigations.
This approach can be applied to detect activities such as wash trading of NFTs, in which multiple participants collude to manipulate prices or pool criminal assets. Similarly, irregular asset movements in DeFi logs could indicate revenue sharing from terrorism. By identifying such anomalies, investigators can gather clues without first knowing about the specific criminal activity.
The rise of decentralized exchanges and the ability to use blockchain technology to move large amounts of assets between jurisdictions make DeFi ecosystems attractive targets for financial crime. Criminals, driven by increased compliance controls implemented by centralized crypto service providers, may seek new channels for money laundering. Therefore, layer type detection becomes critical to compliance efforts, especially as blockchain interoperability progresses.
Detecting suspicious activity in the crypto space is challenging due to extreme price volatility. Static risk thresholds are ineffective and leave money laundering undetected. Establishing reference rates within Ethereum would provide a basis for rational money flows, help identify outliers, and potentially improve the detection of illicit activity.
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First published in COIN KURIER, a third-party contributor translated and adapted the article from the original. In case of discrepancy, the original will prevail.
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