The growth of financial asset tokenization, the expansion of opportunities for market abuse in crypto-assets trades and the use of cryptoassets to perform or facilitate illicit activity have given rise to the need for blockchain intelligence, to both extract and analyze blockchain transaction data for use in financial services supervision and law enforcement. However, blockchain intelligence and the training of how to investigate blockchain transactions has predominantly been performed manually, relying on tools and training provided by blockchain intelligence vendors.

For example, law enforcement agencies use blockchain intelligence tools to trace funds with illicit origins back to known cryptoasset exchanges, enabling them to issue information requests. Similarly, major cryptoasset industry players utilize these tools to meet compliance requirements and adhere to anti-money laundering (AML) regulations. However, it has become evident that such manual investigations can no longer keep up with the rapidly increasing volume and complexity of blockchain data, nor can it provide macro data that is fundamental for making economic decisions, or for a day when most assets have become tokenized.

As the use of blockchains for asset tokenization and cross-border payments increases, there is a pressing need to develop better blockchain intelligence, and integrated, interoperable solutions to deal with the challenges and opportunities afforded by blockchain technology.

Law enforcement is now facing a growing number of cases, which can no longer be managed manually and regulators and policymakers have few if any tools to deal with the rising number of tokenized assets.

Supervisors overseeing crypto-asset service providers (CASPs) are dealing with an influx of data, and with the rise of decentralized finance (DeFi) services – often deeply nested and challenging to analyze manually – the need to rethink blockchain intelligence has become clear.

This shift calls for reimagining blockchain intelligence as a data science task, leveraging advanced tools and techniques to manage the increasing data volumes and complexity, and subject to rigorous and resilient testing methodologies, to ensure the methods are sound.

Consequently, data availability, accessibility and interoperability will be essential for efficient and effective blockchain intelligence in the future. As a result, we must consider a more systematic and standardized approach that enables stakeholders to exchange relevant on- and off-chain data in a safe, secure and accessible forum, that shares intelligence which enables regulators, policymakers, financial institutions, compliance professionals and law enforcement, to make better decisions.

The need for data exchange and interoperability emerged in the law enforcement sector several years ago. In response, the team behind the open-source cryptoasset analytics platform GraphSense defined a structured format for exchanging attribution tags – metadata that provides contextual information, such as ownership details, for specific cryptoasset addresses. This work was done in close collaboration with INTERPOL, leading to the development of the TagPacks format^[1], which is now widely used by various stakeholders for interoperable data exchange.  While this standard enables metadata interoperability on a syntactic and structural level, it soon became clear that semantic interoperability was also necessary. Consequently, multiple institutions collaborated on defining the INTERPOL Dark Web and Virtual Assets Taxonomy (DWVA), which has since been adopted by MISP^[2]. The GraphSense TagPack format and the INTERPOL DWVA taxonomy are now integrated and utilized across a range of tools, enhancing consistency and collaboration in blockchain intelligence.

The proliferation of crypto-asset payment tokens such as stablecoins (e-money tokens in the EU) and their use in cross-border transfers of value has also underscored the need for authorities to be equipped with an understanding of their workings, as well as the tools to supervise their impact on their economies.

Everything from capital flight to money laundering, sanctions to tax evasion, can and is facilitated by unregulated e-money tokens and it is essential that authorities not only have an adequate understanding of the challenges posed by these developments, but also the necessary tools to oversee their impact and manage their use. 

On the occasion of the opening of this forum, it was publicly announced the establishment of a working group aimed at introducing a new occupation—Blockchain Analyst—into Romania’s Classification of Occupations as well as in the European Skills, Competences, Qualifications and Occupations (ESCO) framework. This initiative was grounded in the latest developments in the labor market, reflecting the growing adoption of blockchain technology across various sectors such as finance & banking, healthcare, identity management and beyond. The challenges inherent in this endeavor stem from the complexity of the field and the multifaceted expertise required of a blockchain intelligence professional. This role demands proficiency in cryptography, economics, legal frameworks, cybersecurity, and data analysis.

The need for this occupation arose from the increasing demand for specialists capable of analyzing blockchain data, identifying trends, and preventing fraud. Professionals in blockchain intelligence play a critical role in compliance, monitoring and mitigating risky or illegal activities by detecting patterns and analyzing transactions within vast volumes of data.

The goal of the forum and the proposed sessions and workshops was to establish the appropriate framework to facilitate discussion about the importance of common data exchange and interoperability standards within the digitalization of finance and especially the blockchain intelligence space.
Building on lessons learned in both financial services and law enforcement, the forum brought together stakeholders from diverse fields to gather their requirements and insights. The ultimate aim was to initiate a broader standardization effort, resulting in universally adopted data formats and taxonomies that can be applied across multiple sectors.