AI and on-chain analytics: how blockchain transactions are tracked

Every transaction on a public blockchain is permanently recorded and accessible to anyone. However, the availability of data alone does not mean it is easy to understand: blocks contain millions of addresses, amounts and timestamps with no names or context attached.

This is where on-chain analytics comes in, a discipline that turns this raw data into useful insights. In recent years, artificial intelligence (AI) has played an increasingly important role in it, as it can process volumes of data that would be out of reach for human analysts.

In this blog, we explain what on-chain analytics is, how AI is changing it, where this combination is already being used and what limitations should be kept in mind.

What is on-chain analytics?

On-chain analytics is the study of data recorded directly on the blockchain in order to understand the behaviour of the network, the market and its participants. Unlike traditional finance, where transaction data is kept within banks, here the entire history is public.

The following data is particularly important for security and regulatory compliance:

• Fund flows: the path funds take from address to address, including transfers to exchanges and other service providers.
• Links to known entities: whether addresses are directly or indirectly connected to exchanges, mixers, darknet markets or addresses flagged in fraud cases.
• Splitting and merging patterns: dividing amounts into many smaller transactions or consolidating funds from multiple addresses, which can be used to conceal the origin of money.
• Smart contract interactions: which protocols an address uses and whether any of them are compromised or high-risk contracts.
• General activity indicators: the number of active addresses and transaction volume, which provide context for assessing what is normal on the network.

All of these metrics are available to anyone who knows how to read the blockchain. The challenge lies in the sheer volume of data and in the fact that addresses alone do not reveal who is behind them.

How AI is changing on-chain analytics

AI does not create new data, but it enables faster and deeper analysis of existing data. The most significant changes are happening in three areas.

Address clustering

A single person or organisation often uses many different addresses. Machine learning models analyse transaction patterns, such as addresses that appear together as inputs in the same transaction or similar timing patterns, and group them into likely entities. Instead of thousands of unrelated addresses, the result is an overview of entities such as exchanges, funds or services.

Pattern and anomaly detection

Algorithms can learn what “normal” behaviour on the network looks like and flag deviations: unusually large transfers, sudden changes in fund flows or transaction chains typical of concealing the origin of money. What would take an analyst days, a model can flag in a matter of seconds.

Language models as an interface

Large language models make analytics more accessible to those who are not technical experts. Instead of writing database queries, users can ask a question in plain language, and the model translates it into an analysis and summarises the result. Such tools can also explain what happened in a particular transaction or smart contract.

Practical applications

Security and user protection

AI models help with the early detection of fraud, phishing campaigns and compromised smart contracts. When funds from a known hack start moving across the network, analytics systems can trace their path and alert platforms before they are converted into other assets.

Regulatory compliance (AML and the Travel Rule)

Crypto-asset service providers in the EU must implement anti-money laundering and counter-terrorist financing measures and apply the Travel Rule, which requires originator and beneficiary information to accompany transactions. AI-assisted on-chain analytics enables address risk assessment, the detection of links to sanctioned or high-risk entities, and more efficient transaction monitoring at scale.

Market insights

On-chain data is also used for market analysis, but it has clear limitations there. We wrote more about this in our blog: AI Analytics in Predicting Market Movements: The Pros and Cons

Research and transparency

Journalists, researchers and regulators use the same tools to analyse market events, trace funds in fraud cases and better understand how decentralised protocols work.

Limitations and risks

AI significantly expands the capabilities of on-chain analytics, but it is not infallible.

• Incorrect assumptions: address clustering is based on probabilities. An address may be wrongly linked to a particular entity, which can lead to inaccurate conclusions.
• Training data bias: a model is only as good as the data it was trained on. New fraud patterns may go undetected.
• Language model errors: language models sometimes produce convincing but incorrect answers. Conclusions should always be verified against the source data.
• Privacy: the more sophisticated the analytics, the easier it becomes to link addresses to real people. This raises important questions about protecting the privacy of users who have done nothing unlawful.
• Overreliance: automated systems work best as support for human judgement, not as a replacement for it.

How does this affect everyday crypto use?

For most crypto users, AI and on-chain analytics work in the background. When a regulated platform reviews a transaction or requests additional information about the recipient, part of that process often relies on exactly these kinds of tools. Their purpose is to protect users and the financial system from abuse.

Users who want to research the market on their own also have access to public blockchain explorers and analytics platforms. When using such tools, it helps to:

• understand what each metric actually measures,
• cross-check conclusions across multiple sources,
• keep in mind that no indicator guarantees future price movements.

It is also important to remember that transactions on public blockchains are permanently visible, so understanding the basics of on-chain analytics is useful for your own security and privacy as well.

Conclusion

On-chain analytics uses one of the blockchain’s fundamental features, transparency, to turn raw data into understandable insights. Artificial intelligence brings speed, scale and accessibility to this process: from address clustering and anomaly detection to language interfaces that make analytics accessible to a wider audience.

At the same time, AI remains a tool that requires verification and responsible use. It delivers the most value when combined with expert human judgement, a clear regulatory framework and respect for user privacy.