With its historic Merge event in September, Ethereum has become a proof-of-stake blockchain. The mechanism now used to confirm transactions is based on validators staking their Ether (ETH). The March update of Ethereum, codenamed Shanghai, finally allowed participants to withdraw their locked Ether.
“Investment themes” of the Ethereum ecosystem have included a) decentralized finance (DeFi) b) stablecoins c) Bitcoin (via wrapped versions of BTC) and d) non-fungible tokens (NFTs). With the update, the network also began offering fixed income assets.
Currently, there are several ways that people make money using Ethereum. Broadly, they can be grouped into “investment themes”, which include: a) decentralized finance (DeFi); b) stablecoins; c) Bitcoin (BTC) (via wrapped versions of BTC); and d) non-fungible tokens (NFTs). After Shanghai, the network began to offer fixed income assets.
Risk-free rate
Performance is one of the fundamental pillars of traditional finance (TradFi). An increase or fall in yield leads to an increase or decrease in the perceived risk of other financial assets. Therefore, movements in the reference rate set by the US Federal Reserve provide the rationale behind investment decisions, in general.
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Accordingly, trends in the risk-free rate are used by compliance professionals to detect irrational movements of funds in the capital markets, as such flows of funds may be money laundering attempts. The reasoning here is that launderers of illicit funds do not actively seek financial gain like regular investors, since the sole purpose of money laundering is to obfuscate the dirty money trail.
Since Ethereum staking performance indicates the “risk-free rate” of the crypto ecosystem, the Shanghai update may have improved the state of crypto forensics.
TradFi Forensics Focuses on Activity – Crypto Forensics Focuses on Entities
The risk of financial crimes in TradFi is managed through automatic systems that alert institutions about the probable illicit use of financial assets. While data scientists design and implement models to generate red flags about suspicious transactions, investigative teams still need to evaluate the resulting leads and assess whether suspicious activity reports (SARs) are necessary.
An interesting point of contrast between forensics for TradFi and crypto is that the latter focuses more on the criminal entity than the activity itself. In other words, investigators analyze crypto wallet networks to identify criminal asset transfers.
Money laundering occurs in three stages: a) Placement: the proceeds of crime enter the financial system; b) Overlap: complex movement of funds to obscure the audit trail and cut the link with the original crime; and c) Integration: criminal proceeds are now fully absorbed into the legal economy and can be used for any purpose.
For crypto assets, it is convenient to design solutions to detect the placement of illicit assets. This is because the majority of money laundered originates from crypto-native crimes such as ransomware attacks, DeFi bridge hacks, smart contract exploits, and phishing schemes. In all of these crimes, the perpetrator’s wallet addresses are readily available. Consequently, once a crime has been committed, the relevant wallets are monitored to analyze asset flows.
By contrast, forensic experts working for, say, a bank do not have any visibility into crime, such as human or drug trafficking, cybercrime, or terrorism, when criminal proceeds are injected into a bank’s ecosystem. . This makes detection extremely difficult. Therefore, most anti-money laundering (AML) solutions are designed to identify layers.
Ethereum staking rewards make it easy to spot unusual activity
In designing solutions to detect layers, it is imperative to think like criminals, who concoct complex flows of funds to obfuscate the money trail. The proven approach to exposing such activity is to detect irrational movement of assets. This is because money laundering is not intended to generate profit.
With the post-Shanghai Ether staking yields providing benchmark interest rates for cryptocurrencies, we can formulate benchmark risk-reward structures. Armed with this, researchers can systematically detect counterintuitive financial behavior from trends in the benchmark rate.
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To illustrate, there may be a pattern where an address or a group of addresses points to an entity that consistently takes high risk while earning below the risk-free rate. A situation like that would almost certainly be investigated at a bank.
For example, such transaction surveillance architecture can be used to detect NFT laundering trade. Here, multiple market participants collude to carry out numerous NFT transactions with the aim of accumulating criminal assets or manipulating prices. Since profit is not the intent behind the vast majority of these transactions, such activity will raise a red flag.
Similarly, in a situation where terrorist profits overlap via DeFi protocols, detection of irrational asset movements can provide investigators with substantial clues, even without knowledge of the actual crime.
Financial crimes and DeFi
Traditional capital markets are often used to covertly move funds to circumvent sanctions and finance terrorist activities. Similarly, DeFi ecosystems present an attractive target for financial crime due to the ability to move large sums of assets between jurisdictions using blockchain.
Also, there has been a significant shift in activity from centralized exchanges to decentralized exchanges due to recent fiascos like the FTX crash. This increase in DeFi volumes has made it easier for illegal flows to remain hidden.
Even more compelling is the introduction of better compliance checks by centralized crypto service providers, often mandated by regulators, which is likely prompting criminals to seek new channels for money laundering.
Consequently, illicit flows to DeFi could originate from an expanded set of crimes. This paradigm shift in crypto markets will require forensic teams to increase their capabilities to investigate complex fund flows through various protocols without prior knowledge of the origin of criminal assets.
Consequently, compliance efforts must revolve around discovering layer topologies. In fact, with the rapid progress in blockchain interoperability, systematic surveillance to detect criminal transfers has become even more crucial.
Our ability to detect suspicious activity in cryptocurrencies is less than ideal, in part due to the extreme volatility of cryptocurrency prices. Volatility makes static risk thresholds ineffective and can allow money laundering to go unnoticed. In this sense, as long as Ethereum establishes a benchmark rate, it will provide a means to establish baseline rationality for fund flows and therefore detect outliers.
Debanjan Chatterjee He has more than 17 years of experience analyzing trends in financial crime using data science, including more than 13 years at HSBC. She has a master’s degree in economics from India’s Delhi School of Economics.
This article is for general information purposes and is not intended to be and should not be taken as legal or investment advice. The views, thoughts and opinions expressed herein are those of the author alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.
