Title: Rethinking AML Strategy: The Potential and Limitations of On-Chain Analytics
The traditional approach to anti-money laundering (AML) regulations has been to prevent illicit funds from entering the financial system. However, after years of tightening regulations, organized crime and tax evasion continue to thrive. This has led some experts to question whether a new approach is needed.
One such approach, proposed by fintech author David G.W. Birch, suggests allowing criminals into the system and monitoring their activities. This can be achieved through on-chain analytics, which involves tracking transactions on the blockchain to identify suspicious activity.
On-chain analytics firms use a variety of methods to gather and analyze data from the blockchain. This includes mapping addresses, identifying entities, and clustering UTXO associated with the same entity. While on-chain analytics is not an exact science, it can provide valuable insights into the movement of funds.
However, there are limitations to on-chain analytics. Methods used by different firms vary, and false positives can occur. It is important for firms to be transparent about their methods and accuracy rates to ensure reliability.
Despite these limitations, on-chain analytics can be a powerful tool for tracking illicit activity on the blockchain. Techniques such as chain hopping and mixers can be traced, and criminals often leave traces that can be followed. Law enforcement agencies have successfully used on-chain analytics to uncover criminal networks and seize illicit funds.
In conclusion, while there is room for improvement in on-chain analytics, it is already an effective tool for tracking illicit activity on the blockchain. It may be time to reconsider our approach to AML and embrace the potential of on-chain analytics in the fight against financial crime.
