Ethereums Shanghai upgrade made it easier to detect criminals

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With its landmark Merge event in September, Ethereum became a proof-of-stake blockchain. The mechanism now used to confirm transactions relies on validators staking their Ether (ETH). The Ethereums March upgrade, codenamed Shanghai, finally allowed stakers to withdraw their locked Ether.

Investment themes of Ethereum ecosystems have included a) decentralized finance (DeFi) b) stablecoins c) Bitcoin (via wrapped versions of BTC) and d) non-fungible tokens (NFTs). With the upgrade, the network also started providing fixed income assets.

There are currently several ways to make money on or using Ethereum. Broadly, they can be grouped into investment themes, including: a) decentralized finance (DeFi); b) stable coins; c) Bitcoin (BTC) (via wrapped versions of BTC); and d) non-fungible tokens (NFTs). After Shanghai, the network started offering fixed income assets.

Risk-free rate

Yield is one of the pillars of traditional finance (TradFi). A rise or fall in yield leads to an increase or decrease in the perceived risk of other financial assets. Thus, movements in the benchmark rate set by the US Federal Reserve provide the rationale for investment decisions, in general.

Related: Ethereum will transform investing

Accordingly, compliance professionals use trends in the risk-free rate to detect irrational movements of funds in the capital markets, as these flows of funds could be attempts to launder money. The reasoning here is that illicit money launderers do not actively seek financial gain like regular investors, because the sole purpose of money laundering is to obscure the trail of dirty money.

With Ethereum’s staking yield indicating the risk-free rate of the crypto ecosystem, the Shanghai upgrade may have improved the state of crypto forensics.

TradFi forensics focuses on crypto business forensics focuses on entities

Financial crime risk in TradFi is managed using automatic systems that alert institutions to the likely misuse of financial assets. While data scientists design and deploy models to flag suspicious transactions, investigative teams have yet to assess the resulting leads and determine whether suspicious activity reports (SARs) should be filed.

An interesting contrast between forensics for TradFi and cryptography is that the latter focuses more on the criminal entity than on the activity itself. In other words, investigators scan crypto wallet networks to identify criminal asset transfers.

Money laundering takes place in three stages: a) Placement: the proceeds of crime enter the financial system; b) Overlay: complex movement of funds to obscure the audit trail and break the link to the original crime; and c) Integration: the proceeds of crime are now fully absorbed by 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. Indeed, most of the money laundered comes from crypto-native crimes such as ransomware attacks, DeFi bridge hacks, smart contract exploits, and phishing schemes. In all of these offences, the perpetrators’ wallet addresses are readily available. Therefore, once a crime has been committed, the affected wallets are monitored to analyze asset flows.

On the other hand, forensic experts working for, for example, a bank have no visibility on the offense such as human or drug trafficking, cybercrime or terrorism when the proceeds of crime are injected into an ecosystem. banking. This makes detection extremely difficult. Therefore, most anti-money laundering (AML) solutions are designed to identify stratification.

Ethereum staking rewards make it easier to spot unusual activity

To design solutions to detect stratification, it is imperative to think like criminals, who engineer complex cash flows to obscure the money trail. The proven approach to exposing such activity is to spot the irrational movement of assets. Indeed, money laundering is not intended to generate profits.

With Ether staking returns after Shanghai providing benchmark interest rates for crypto, we can formulate basic risk-reward structures. Armed with this, investigators can systematically spot financial behaviors that run counter to benchmark rate trends.

Related: Thanks to Ethereum, altcoin is no longer an insult

To illustrate, there may be a pattern where an address or group of addresses points to an entity that consistently takes high risk while earning below the risk-free rate. Such a situation would almost certainly be investigated at a bank.

For example, such a transaction monitoring architecture can be used to detect fictitious trading of NFTs. Here, several market participants conspire to perform numerous NFT transactions with the aim of layering criminal assets or manipulating prices. Since profit making is not the intention behind the vast majority of these trades, such activity will raise red flags.

Likewise, in a situation where proceeds of terrorism are superimposed via DeFi protocols, detection of irrational asset movements can provide substantial leads to investigators, even without knowledge of the actual crime.

Financial crime 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 vast sums of assets between jurisdictions using blockchain.

Additionally, there has been a significant shift in activity from centralized exchanges to decentralized exchanges due to recent fiascos like the collapse of FTX. This increase in DeFi volumes has made it easier for illegal flows to remain obscure.

Even more compelling is the introduction of better compliance checks by centralized crypto service providers, often mandated by regulators, which likely drives criminals to seek out new money laundering channels.

Therefore, illicit flows to DeFi could stem 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 source of criminal assets.

As a result, compliance efforts must revolve around discovering layered typologies. In fact, with rapid advances in blockchain interoperability, systematic monitoring to detect criminal transfers has become even more crucial.

Our ability to detect suspicious activity in cryptos is less than ideal, in part due to the extreme volatility in crypto prices. Volatility makes static risk thresholds ineffective and can allow money laundering to go unnoticed. In this sense, if and when Ethereum sets a benchmark rate, it will provide a way to establish a baseline rationality for fund flows and thus spot outliers.

Debanjan Chatterjee has over 17 years of experience analyzing financial crime trends using data science, including over 13 years at HSBC. He holds a master’s degree in economics from the Indias Delhi School of Economics.

This article is for general informational purposes and is not intended to be and should not be considered legal or investment advice. The views, thoughts and opinions expressed herein are the sole authors and do not necessarily reflect or represent the views and opinions of Cointelegraph.

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