Decoding Web3 Compliance: A State of Knowledge Review

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Author: Denis Avetisyan

This article systematically examines the emerging landscape of regulatory technology designed to bring accountability to the decentralized world of cryptocurrencies.

A two-layer Web3 compliance protocol establishes and verifies digital identities at both entity and address levels through document verification, biometric authentication, and cryptographic proofs, while simultaneously providing continuous monitoring and risk assessment via transaction analysis, composite address risk scoring, and behavioral profiling to mitigate illicit activity.
A two-layer Web3 compliance protocol establishes and verifies digital identities at both entity and address levels through document verification, biometric authentication, and cryptographic proofs, while simultaneously providing continuous monitoring and risk assessment via transaction analysis, composite address risk scoring, and behavioral profiling to mitigate illicit activity.

A comprehensive analysis of Web3 RegTech solutions for Virtual Asset Service Provider (VASP) Anti-Money Laundering and Counter-Terrorist Financing (AML/CFT) compliance.

The decentralized nature of Web3 technologies presents a fundamental paradox for regulatory compliance, challenging traditional Anti-Money Laundering and Counter-Financing of Terrorism (AML/CFT) approaches. This systematization, ‘SoK: Web3 RegTech for Cryptocurrency VASP AML/CFT Compliance’, comprehensively analyzes the emerging landscape of blockchain-native RegTech solutions designed to address these challenges. Our work reveals a growing ecosystem leveraging distributed ledger properties for transaction graph analysis, real-time risk assessment, and privacy-preserving verification, yet significant gaps remain between academic innovation and practical deployment. Can these emerging technologies effectively balance regulatory demands with Web3’s core principles of decentralization and user sovereignty, and what further research is needed to realize this potential?

Decentralization’s Dilemma: Navigating the Regulatory Frontier

The burgeoning Web3 landscape, characterized by decentralized architectures and pseudonymous participation, introduces regulatory hurdles unlike any previously encountered. Traditional financial systems rely on centralized intermediaries and known identities, facilitating compliance and enforcement; however, Web3 fundamentally disrupts this model. Transactions occur directly between users, often utilizing cryptographic addresses rather than personally identifiable information, obscuring the origin and destination of funds. This inherent opacity complicates efforts to combat illicit activities such as money laundering and terrorist financing, while also presenting challenges for tax collection and consumer protection. The sheer scale and velocity of blockchain transactions further exacerbate these difficulties, overwhelming conventional monitoring systems and demanding innovative approaches to regulatory oversight.

Conventional regulatory technologies, designed for centralized financial systems, are increasingly inadequate when applied to the dynamic landscape of blockchain transactions. The sheer velocity of transfers on many blockchains, coupled with their intricate smart contract logic, overwhelms systems built for slower, serial processing. Furthermore, the pseudonymous nature of most blockchain addresses obscures the identities of transacting parties, hindering traditional Know Your Customer (KYC) and Anti-Money Laundering (AML) procedures. This creates significant gaps in oversight, as regulators struggle to effectively monitor and investigate potentially illicit activity occurring across decentralized networks. The limitations of existing tools aren’t simply a matter of scale; they represent a fundamental mismatch between the architecture of legacy systems and the unique characteristics of Web3, demanding innovative solutions to maintain market integrity and protect consumers.

Traditional financial monitoring systems primarily focus on what is being transferred – the amount, the addresses involved – but this approach proves inadequate within the Web3 landscape. Understanding the intent behind each transaction is now paramount; a transfer may appear innocuous on the surface, yet facilitate illicit activities such as money laundering, sanctions evasion, or the funding of illegal marketplaces. Advanced analytical tools are being developed to decipher these underlying purposes by leveraging on-chain data, smart contract analysis, and behavioral modeling. These systems aim to identify patterns indicative of malicious activity, even when attempts are made to obscure the true purpose of the transfer through techniques like mixing or layering. Effectively discerning intent requires moving beyond simple tracking to a nuanced understanding of the broader context surrounding each transaction within the Web3 ecosystem.

The escalating complexity of Web3 demands a novel approach to regulation, prompting the development of dedicated Web3 RegTech solutions. These technologies move beyond conventional monitoring systems, employing advanced analytics and on-chain intelligence to decipher patterns and identify illicit activities within decentralized networks. Unlike traditional financial oversight, which relies on centralized intermediaries, Web3 RegTech aims to provide transparency and accountability directly on the blockchain, enabling regulators to assess risk and enforce compliance without stifling innovation. This next generation of regulatory tools focuses on understanding the flow of value, identifying the beneficial owners of digital assets, and flagging suspicious transactions – ultimately striving to balance the promise of decentralized finance with the need for a secure and compliant financial ecosystem.

Analytical Foundations: Deciphering On-Chain Signals

Robust transaction monitoring forms the initial layer of defense in Web3 RegTech, involving the continuous surveillance of cryptocurrency transactions for indicators of illicit activity. This process encompasses both on-chain and off-chain data sources, with systems designed to flag transactions exceeding pre-defined thresholds, those originating from or destined for known high-risk addresses – such as those associated with sanctioned entities or prior fraud – and those exhibiting unusual patterns. Monitoring extends to transaction attributes including value, frequency, and the involved addresses, utilizing techniques like address clustering and heuristic analysis to identify potentially suspicious activity. Real-time alerts are generated upon detection of anomalies, enabling rapid investigation and potential intervention to prevent financial crime.

Cross-chain analytics addresses the limitations of monitoring activity within a single blockchain by tracking the flow of funds and assets across multiple blockchains. Illicit actors frequently utilize multi-chain transactions to obfuscate the origin and destination of funds, moving assets between networks to evade detection and regulatory scrutiny. This technique involves correlating transaction data from disparate blockchains, identifying patterns indicative of money laundering, sanctions evasion, or other illegal activities. By establishing linkages between addresses and transactions across chains, cross-chain analytics provides a more comprehensive view of risk exposure than single-chain analysis, enabling RegTech solutions to detect and prevent illicit activity that would otherwise remain hidden.

Machine learning models address the shortcomings of traditional rule-based systems in Web3 RegTech by identifying complex and evolving patterns indicative of illicit activity. Unlike rule-based systems which rely on predefined criteria, these models are trained on large datasets of on-chain transactions to recognize anomalous behaviors that deviate from established norms. Common techniques include supervised learning, where models are trained on labeled data to classify transactions as legitimate or suspicious, and unsupervised learning, which identifies outliers and clusters of unusual activity without prior labeling. These models continuously adapt to new data, improving their accuracy in detecting sophisticated fraud, money laundering, and other illicit financial flows that would otherwise evade detection.

Transaction graph analysis enhances detection capabilities by representing blockchain data as a network where nodes are addresses and edges represent transactions. This allows for the identification of previously hidden relationships and patterns of activity that are not apparent when examining individual transactions in isolation. By mapping the flow of funds and interactions between addresses, analysts can uncover complex networks indicative of money laundering, fraud, or other illicit behavior. The technique relies on graph algorithms to identify key nodes, detect communities, and calculate metrics like centrality and path length, providing insights into the structure and behavior of the network. This approach is particularly effective in identifying mixers, tumblers, and other obfuscation techniques used to conceal the origin or destination of funds.

Preserving Privacy: The Architecture of Responsible Oversight

Privacy-Enhancing Technologies (PETs) represent a suite of techniques allowing regulatory compliance and data analysis without compromising individual privacy. These technologies move beyond traditional anonymization methods, which can be vulnerable to re-identification, by enabling data processing in a manner that minimizes data exposure. Rather than requiring centralized access to raw data, PETs facilitate computations directly on encrypted or obfuscated data, or allow for the derivation of insights without revealing the underlying data points themselves. This approach supports regulators in achieving oversight and enforcing compliance while simultaneously upholding user privacy expectations and reducing the risks associated with data breaches and misuse. The adoption of PETs is increasingly viewed as a core component of responsible data governance and a means to reconcile the demands of both innovation and privacy protection.

Secure Multi-Party Computation (SMPC) is a cryptographic technique that enables multiple parties to jointly compute a function over their private data without revealing that data to each other. This is achieved through a distributed computation where each party’s input is kept secret, and only the result of the function is revealed. SMPC relies on cryptographic protocols, such as secret sharing and homomorphic encryption, to ensure data confidentiality and computational correctness. Practical implementations often involve a combination of these techniques to optimize performance and scalability. Applications include fraud detection, medical research, and financial modeling, where data sharing is beneficial but privacy concerns are paramount. The core principle is that no single party learns anything about the other parties’ inputs beyond what can be inferred from the final result of the computation.

Zero-Knowledge Proofs (ZKPs) are a cryptographic method allowing one party (the prover) to demonstrate to another party (the verifier) that a statement is true, without conveying any information beyond the validity of the statement itself. This is achieved through a challenge-response protocol where the prover demonstrates knowledge of a secret without revealing the secret. Crucially, ZKPs offer statistical proof; a verifier can be convinced with a high degree of certainty, but cannot dispute the proof without possessing the secret information. Applications extend to authentication systems, verifiable computation, and privacy-preserving data sharing, as the proofs themselves are significantly smaller than the data they verify, reducing bandwidth and storage requirements while maintaining data confidentiality.

Graph Neural Networks (GNNs) facilitate analysis of relational data by operating directly on graph structures, which represent entities as nodes and their interactions as edges. This approach minimizes data exposure because GNNs can perform computations – such as node classification, link prediction, and graph classification – without requiring centralized access to the complete dataset. Instead of transmitting raw data attributes, GNNs leverage the graph’s topology and aggregated feature information, reducing the risk of individual data point identification. Furthermore, techniques like federated learning can be combined with GNNs, allowing models to be trained on decentralized graph data without data ever leaving its source, enhancing both privacy and scalability for complex relationship analysis.

A Holistic Framework: Integrating RegTech for Decentralized Systems

Web3 RegTech transcends the limitations of isolated tools, functioning instead as a cohesive, multi-layered system designed for thorough risk mitigation within decentralized environments. Rather than relying on single points of analysis, effective solutions integrate diverse methods – from identifying potentially illicit addresses to interpreting the intent behind transactions – creating a holistic view of on-chain activity. This integrated approach is crucial because risks in Web3 are often obscured by the complex interactions between smart contracts, decentralized exchanges, and various token movements; a single technological fix proves insufficient. By combining address screening, behavioral analysis, and transaction intent monitoring, platforms can achieve a more accurate assessment of risk profiles, reduce false positives, and ultimately foster a safer, more compliant Web3 ecosystem. This systemic perspective is central to building robust and scalable regulatory technology for decentralized systems.

Address screening forms a foundational layer in Web3 regulatory technology, functioning as a crucial first line of defense against illicit activity. This process involves meticulously examining transaction origins and destinations, comparing them against known lists of compromised addresses, sanctioned entities, and those associated with illegal activities like fraud or money laundering. By flagging transactions linked to these high-risk addresses, platforms can proactively identify and prevent potentially harmful interactions. The efficacy of address screening relies heavily on the comprehensiveness and constant updating of these watchlists, alongside increasingly sophisticated techniques to circumvent obfuscation methods employed by malicious actors. This proactive approach not only mitigates financial crime but also enhances the overall integrity and trustworthiness of the Web3 ecosystem, fostering greater adoption and responsible innovation.

Traditional transaction monitoring often flags legitimate activity due to a lack of contextual understanding, generating numerous false positives. Intent-aware monitoring addresses this limitation by shifting the focus from what is happening on the blockchain to why it is happening. This advanced approach analyzes the purpose behind cryptocurrency transactions – identifying, for example, whether funds are being used for decentralized finance (DeFi) participation, NFT purchases, or charitable donations – to differentiate between legitimate and illicit activities. By incorporating this semantic understanding, systems can significantly improve the accuracy of risk assessments, reducing the burden on compliance teams and allowing them to concentrate on genuinely suspicious behavior. This nuanced analysis moves beyond simple pattern matching, enabling a more effective and efficient approach to combating financial crime within the Web3 ecosystem.

A comprehensive analysis of the emerging Web3 RegTech landscape reveals a diverse ecosystem of solutions, meticulously documented through the categorization of 41 commercial platforms and 28 academic prototypes. This systematization of knowledge extends beyond mere inventory; the study identifies key architectural distinctions between these platforms and distills their functionalities into 5 distinct compliance mechanisms. By mapping this complex terrain, researchers not only provide a current snapshot of the field, but also pinpoint critical research directions necessary to bolster compliance effectiveness and address the unique challenges presented by decentralized technologies. This work serves as a foundational resource for navigating the evolving regulatory demands within the Web3 space and fostering innovation in compliance solutions.

The pursuit of Web3 RegTech, as detailed in this systematization of knowledge, echoes a fundamental principle of efficient communication. It strives to reduce noise – in this case, the opacity of transactions and the complexities of cross-chain compliance – to reveal the underlying signal of legitimate activity. As Claude Shannon observed, “The most important thing in communication is to get the message across, not to make a beautiful sound.” This paper’s focus on blockchain analytics and privacy-enhancing technologies isn’t merely about technological innovation; it’s about distilling clear, verifiable information from a system inherently designed for obfuscation. The aim is to minimize ambiguity, enabling effective AML/CFT compliance within the decentralized web, a task requiring surgical precision and relentless simplification.

Where To Now?

The systematization offered here merely clarifies the shape of the problem, not its solution. Current Web3 RegTech addresses symptoms – transaction monitoring, address labeling – while the underlying ailment persists: a fundamental tension between decentralization and centralized control. Blockchain analytics, for example, frequently relies on heuristics, introducing both false positives and opportunities for circumvention. The pursuit of perfect traceability is, demonstrably, an exercise in asymptotic approximation.

Future work must acknowledge this. Privacy-Enhancing Technologies, notably Zero-Knowledge Proofs, offer a potential, though complex, path forward. However, their effective implementation requires standardization and interoperability – qualities conspicuously absent in the current landscape. Cross-chain compliance remains largely aspirational; the fragmented nature of the Web3 ecosystem actively resists holistic oversight.

Ultimately, the field’s progress hinges not on technological innovation alone, but on a sober assessment of what compliance can achieve in a genuinely decentralized system. The goal should not be to replicate traditional financial controls onto the blockchain, but to forge new paradigms – even if those paradigms necessitate a degree of regulatory humility.

Original article: https://arxiv.org/pdf/2512.24888.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-01 20:41