Cryptocurrencies: Problem or Solution?

Cryptocurrencies often evoke a picture of risk-free, untraceable assets used to bypass government controls and facilitate illicit activities. The media has fuelled this narrative, frequently amplifying concerns around their use in criminal enterprises. However, this portrayal is not entirely accurate.

While cryptocurrencies can indeed be used in criminal activities, they offer a crucial advantage over traditional fiat currencies: traceability. Cryptocurrencies are built on blockchain technology, a decentralized, transparent system that records every transaction publicly. This feature, often overlooked in discussions about crypto’s illicit uses, makes digital currencies an unexpected tool for law enforcement in criminal investigations.

This post explores the role of exchange platforms in the crypto ecosystem, focusing on their responsibility in combatting money laundering (AML) and ensuring compliance with financial regulations. Specifically, we will examine how AI-driven traceability tools can help exchanges minimize their liability and adhere to judicial standards, addressing the fundamental question of whether such tools should be incorporated into exchange compliance structures.

Crypto, Crimes, and Exchange Platforms

Cryptocurrencies are appealing to criminals for several reasons: pseudo-anonymity, transparency, and decentralization. The belief that crypto is fully anonymous is a misconception. Most cryptocurrencies rely on public-private key encryption, which means transactions are recorded on the blockchain and can be traced back to public addresses. While these addresses are not inherently tied to personal identities, blockchain’s transparent nature makes it possible to trace the flow of funds, especially when combined with additional data sources.

However, the absence of direct identification is often exploited by criminals. Without linking a public address to a person, it’s difficult to prove whether illicit activity is involved. Even so, the blockchain serves as a permanent, accessible record, offering valuable information for investigators who can leverage advanced traceability tools.

Exchange platforms play a pivotal role in facilitating crypto transactions, particularly when criminals aim to launder money. These platforms provide a seamless interface between traditional financial systems and the decentralized world of cryptocurrencies. As such, they act as gateways for converting illicit funds into digital assets, making them crucial points for AML/CFT (anti-money laundering/counter-financing of terrorism) enforcement.

Centralized exchanges, which are often the most popular, allow users to buy, sell, and trade cryptocurrencies, either using fiat currency or other cryptocurrencies. These exchanges are regulated in various jurisdictions, but the complexity of their operations and the anonymity provided by crypto transactions mean they face significant challenges in complying with AML regulations.

In this context, the critical question arises: can exchange platforms effectively use traceability tools to ensure compliance and prevent illegal activity?

Traceability Tools in Practice

Simplifying Blockchain Analysis

The transparent nature of blockchain provides an open repository of transaction data, but manually analyzing this data is a monumental task. With millions of transactions occurring every day, it becomes impossible for human teams to monitor and track every wallet’s activity effectively. This is where AI-powered traceability tools come in.

Companies such as Chainalysis and Elliptic have developed advanced traceability services that allow exchanges to scan and monitor blockchain data for suspicious transactions. These tools automatically assess the risk associated with a public wallet address by comparing it against a vast database of flagged addresses associated with illicit activity. When a user deposits or withdraws funds, exchanges can quickly check if the associated address has been involved in illegal activities, providing a risk score for that transaction.

For example, when an exchange receives a deposit from a user, it can verify the public address using a traceability tool. If the address has been flagged for association with criminal activity, the platform can take immediate action, either blocking the transaction or initiating further investigation. This process is key to maintaining AML compliance and minimizing exposure to financial crimes.

However, while these tools are valuable, their costs can be prohibitive for smaller exchanges, and their accuracy can sometimes be questioned. Since they rely on algorithms to detect suspicious patterns, there is always a degree of uncertainty regarding how the tool arrived at a particular conclusion. This brings us to the next challenge: the lack of transparency and explainability in the AI-driven decision-making process.

The AI Mechanism

AI-powered traceability tools utilize sophisticated algorithms, often based on machine learning, to analyze blockchain data. The tools rely on neural networks, which simulate the way the human brain processes information. These networks are composed of nodes, or “neurons,” that assess the relationships between different data points. In the case of blockchain analysis, the input data might include the transaction history of a wallet, the IP address of the sender, and the timing of transactions.

When an AI model is trained, it adjusts its internal parameters—referred to as weights—based on the data it processes. The model uses an activation function to determine whether a neuron should “fire” based on the sum of its inputs. If the sum exceeds a certain threshold, the neuron activates and sends a signal forward, eventually leading to an output decision, such as classifying a wallet as “suspicious” or “safe.”

While the AI model can identify patterns that human analysts might miss, it is not always clear how the model arrives at its conclusions. This lack of explainability is a significant challenge in the compliance space, especially when exchanges face legal scrutiny. If a traceability tool flags a wallet, compliance officers need to be able to explain why the tool made that decision—especially if the exchange is involved in a legal investigation. The challenge, however, is balancing the need for explainability with the protection of proprietary algorithms that underpin these tools.

Regulation and Compliance Challenges

The use of traceability tools must align with a complex regulatory landscape. In Europe, the GDPR (General Data Protection Regulation) governs the collection and processing of personal data, which is particularly relevant when using AI to analyze blockchain transactions. While blockchain transactions themselves are public, the use of AI to gather additional data—such as IP addresses or geolocation data—raises privacy concerns.

The EU’s AI Act also classifies AI tools based on their risk level. Traceability tools could be considered high-risk systems, which would subject them to additional regulatory oversight. Moreover, the Markets in Crypto-Assets (MiCA) regulation, which aims to create a comprehensive regulatory framework for cryptocurrencies in the EU, requires crypto-asset service providers (CASPs) to ensure robust AML measures are in place. This includes implementing effective traceability tools to identify suspicious activity.

The AML Package, which consists of new directives and regulations, further expands the scope of AML obligations for CASPs, making it imperative for exchanges to adopt traceability tools as part of their compliance infrastructure. Additionally, criminal codes across the EU criminalize money laundering activities, ensuring that financial institutions, including crypto exchanges, must take necessary steps to prevent the re-entry of illicit funds into the legal economy.

Lessons and Looking Ahead

While cryptocurrencies are used in criminal cases, they are not the ultimate vehicle for illegal activity. Traditional financial systems—banks, credit cards, and money transfer services—remain the primary means of facilitating money laundering. However, cryptocurrencies’ unique characteristics, such as transparency and decentralization, require specific solutions tailored to combat financial crime effectively.

Exchange platforms are central to this effort, as they control access to crypto markets. Their responsibility goes beyond simply providing trading services; they must act as gatekeepers, ensuring that criminal funds do not enter the crypto ecosystem. Compliance with AML regulations is mandatory, but it must be practical and scalable, especially for smaller exchanges.

AI-driven traceability tools offer a significant advantage in identifying illicit activity on the blockchain. By analyzing transaction patterns and wallet histories, these tools help exchanges flag suspicious transactions. However, these tools are not a silver bullet. Their effectiveness is limited by the cost of implementation, the complexity of their algorithms, and the challenges surrounding explainability.

Looking ahead, regulatory frameworks will continue to evolve, and exchanges will need to adapt accordingly. AI-driven traceability tools will play a key role in maintaining compliance, but exchanges must also invest in transparent, explainable systems to ensure accountability. The future of AML compliance in the crypto world depends on a balanced approach that embraces innovation while addressing legal and ethical challenges.

About the author:

Manuel Santos Mailland is a lawyer and holds a Master’s degree in Criminal Law from Universidad Austral. He recently completed a second Master’s in Law, Data, and AI, for which he was awarded a full Erasmus Mundus+ scholarship.

With nearly a decade of experience in white-collar crime, he specializes in crypto-assets and money laundering. Manuel has also published extensively and taught undergraduate and postgraduate courses at various universities and research centres in Argentina.