As more institutions explore digital assets, the need for on-chain analytics platforms has never been higher.
Compliance experts, investigators and regulators use these blockchain analytical tools to better understand patterns and entities in cryptocurrency transactions.
To learn more about the tools and how they fit into broader cryptocurrency adoption, Cointelegraph sat down with Tom Robinson, the co-founder and chief scientist of analytics firm Elliptic; and Eray Akartuna, a senior cryptocurrency threat analyst at Elliptic.
Cointelegraph: What are the most common use cases you see for on-chain analytics for institutional clients?
Tom Robinson: Anti-Money Laundering (AML) and sanctions compliance for crypto exchanges and other businesses handling crypto assets: Our crypto transaction and wallet screening tools help businesses stay compliant with regulations and reduce fraud.
Due diligence of crypto businesses: Our Discovery product provides risk profiles of exchanges and other crypto services based on analysis of their blockchain transactions. It is used by crypto businesses and financial institutions to gain insights into the businesses they transact with.
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Investigate crypto transactions: Investigator — our blockchain investigation software — allows graphical exploration of crypto wallets and the transactions between them. It is used by law enforcement investigators to “follow the money” and link criminal activity to individuals. Crypto businesses also use it to investigate potential illegal activity among their customers.
CT: How does Anti-Money Laundering in crypto differ from mainstream AML within banks for fiat?
TR: The main difference is that most crypto transactions appear on the blockchain. It makes it easier to identify whether the funds come from criminal activity by tracking them using blockchain analysis tools.
CT: Do you see a role for artificial intelligence (AI) and machine learning to play within on-chain analytics? Especially within fraud prevention and AML?
Eray wants to: Yes, we already use machine learning within our blockchain analytics products. However, it is very important to ensure the accuracy of these techniques through extensive testing.
There are certain aspects of blockchain transactions where we can use machine learning to understand or detect specific patterns. The patterns found on the Bitcoin blockchain may not necessarily be the same as the patterns on the Ethereum blockchain; they work in slightly different ways. I will teach the use of heuristics.
There are some aspects of blockchain transactions where we have a common cost that helps us to know if the addresses are owned by an entity or not – if I want to distinguish illegal activities and illegal ones actor in a blockchain – and identify their wallet addresses.
For example, cyber hackers in North Korea use programmatic methods of laundering. The hack was done in 2018, where they used about 113 wallets to separate the funds from the original theft in an automated way. We can programmatically analyze the timestamps of individual transactions to understand how this automated software works.
When we analyze dark web markets or terrorist entities, etc., the use of heuristics helps us to determine if a wallet address is linked to a specific prohibited entity. We can use heuristics to understand what other wallet addresses may also belong to or be associated with that entity.
We have a risk score that matches the predictive analysis. If we look at the up and coming transactions of a bunch of wallets, we can finally see where they end up. Entities identified as belonging to an exchange, a terrorist group or a dark market can be found when they transact with the particular entities we target.
Let’s say about 50% of that crypto goes to a dark web market; we can actually use that to give a risk score of how dangerous the wallet is. The risk score is then used by exchanges and banks to decide whether they want to do business with these wallet holders or not.
CT: What are the most complex problems you’ve solved at Elliptic? Why is it complicated, and why is it important to solve it?
TR: The most complex and important problem that we have solved recently is how to identify the proceeds of crime in crypto, even if they are washed cross-asset and cross-chain. Criminals now transfer their profits between assets, using decentralized exchanges; and between blockchains, using cross-chain bridges.
We have developed holistic screening as a means of automatically tracking crypto funds between assets and blockchains. This unique ability is now absolutely essential; otherwise, money launderers will take advantage of the lack of businesses in their activities.
CT: How do you see banks adopting digital assets and that on-chain analytics? What has been done so far?
EA: We are seeing slow but steady adoption, but compliance is top of mind for banks. Blockchain analytics is seen as an important piece of the puzzle and a way to alleviate the concerns of regulators.
If institutions want to participate in the decentralized finance (DeFi) space and plan to invest clients’ funds, they need to know if the liquidity pool they are investing in is reliable and has the right risk profile. If the liquidity pool has illegal funds flowing in and out of it, there is a compliance issue. That’s a key use case for institutions looking to get involved in DeFi.
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Another use case is where some challenger banks like Revolut allow their customers to hold and sell cryptocurrencies. These banks will require compliance and AML capabilities before offering these products to customers.
CT: Do you have any interactions with regulators that affect how you serve the financial services industry, and what are the key areas of interest from a regulatory perspective?
TR: We have a constant dialogue with regulators around the world, many of whom use our products. It is important that they understand how our blockchain analytics solutions work so that they can have confidence in the compliance programs run by the exchanges and banks that use our products.