Integrating Blockchain Analytics with Traditional AML Controls: A Critical Path to Effective Compliance
As blockchain technology transforms financial services, it introduces both new tools and new threats in the fight against financial crime. While blockchain analytics offer novel capabilities for Anti-Money Laundering (AML) enforcement, they should not be viewed as replacements for traditional AML frameworks. Instead, a hybrid approach — combining blockchain-native tools with conventional compliance practices — is vital to building a resilient and responsive AML regime. Financial institutions and Virtual Asset Service Providers (VASPs) that rely solely on identity verification or blockchain monitoring risk missing critical elements of customer risk assessment and transaction oversight.
Beyond Identity Verification: The Misinterpretation of CDD in Web3
Customer Due Diligence (CDD) is a pillar of AML compliance, entailing not only identity verification but also the assessment of customer risk profiles, understanding of source of funds, and monitoring of ongoing activity. However, some VASPs simply equate CDD with mere identity checks, neglecting the broader obligations that underpin risk-based compliance. This narrow interpretation undermines the effectiveness of AML programs and leaves significant blind spots in identifying illicit activity.
In Web3 ecosystems, the challenge is compounded by pseudonymity and decentralization. Users can generate multiple wallet addresses and interact with permissionless protocols without revealing their identities. While some VASPs implement KYC procedures, these often stop at document-based verification without integrating behavioral or transactional analysis.
Emerging blockchain-based identity solutions — such as Self-Sovereign Identity (SSI) systems and Zero-Knowledge Proofs (ZKPs) — offer more privacy-conscious pathways for verifying user credentials. Yet, these tools must be coupled with traditional due diligence practices, including risk scoring, geographic exposure analysis, and ongoing monitoring, to meet regulatory expectations.
Suspicious Transaction Reporting: Bridging the Gap Between On-Chain and Off-Chain Intelligence
Suspicious Transaction Reporting (STR) is a cornerstone of traditional AML efforts, enabling authorities to detect early signs of money laundering or terrorism financing. However, decentralized networks often lack designated entities responsible for such reporting. The automatic execution of smart contracts and the use of privacy-enhancing technologies — like mixers and privacy coins — further complicate the detection and reporting of suspicious activity.
To address this, regulators and compliance teams must integrate blockchain analytics tools capable of flagging anomalous on-chain behavior in real time. Automated systems driven by artificial intelligence can monitor patterns across wallets and platforms, generating alerts for further investigation. However, these alerts are only meaningful when tied to off-chain data — such as IP addresses, device fingerprints, and prior customer interactions — collected through comprehensive CDD.
Without this integration, STRs in the Web3 space risk being either too vague or entirely absent, especially if VASPs do not fulfill their broader due diligence responsibilities.
Behavioral Analysis: The Missing Link Without Full CDD Implementation
Traditional financial institutions rely on behavioral analysis to detect deviations from established customer patterns. In the crypto space, this is significantly harder due to the fragmented and pseudonymous nature of blockchain interactions. While blockchain analytics can map wallet activity, they lack the context needed to interpret that behavior meaningfully — unless combined with off-chain identity and customer data.
Effective behavioral monitoring in Web3 depends on robust CDD that extends beyond identity documents. This includes understanding customers’ financial backgrounds, transactional history, and risk indicators across jurisdictions. Unfortunately, some VASPs may overlook this, limiting their monitoring capabilities and increasing exposure to illicit activity.
Machine learning models can enhance behavioral detection by analyzing patterns across multiple blockchains and platforms. Still, these models require high-quality input data — data that only thorough, risk-based CDD can provide. Without this, the insights gleaned from behavioral analytics remain superficial.
Investigative Tools: Strengthening Crypto Crime Probes Through Combined Expertise
Investigating crypto-related financial crime demands both blockchain forensics and traditional investigative methods. Tools like chain analysis, wallet clustering, and smart contract audits are crucial in tracing illicit flows on-chain. However, they must be supported by foundational AML practices: document review, cross-border cooperation, and legal evidence gathering.
The global, permissionless nature of cryptocurrency transactions allows bad actors to exploit jurisdictional inconsistencies. Techniques such as chain-hopping and use of privacy coins challenge even experienced investigators. In this context, blockchain analytics tools alone are not sufficient. Investigators must also have access to off-chain data and cross-jurisdictional frameworks — both of which depend heavily on the completeness and quality of CDD performed by VASPs.
When some VASPs fail to implement proper risk-based CDD, the investigative trail often breaks down, making it difficult to establish links between transactions and real-world entities.
Global Coordination and the Role of VASPs
The lack of consistent international AML standards has created an environment where illicit actors can exploit regulatory gaps. VASPs operating in jurisdictions with minimal oversight are often targeted by money launderers and other criminals. Efforts like the Financial Action Task Force’s (FATF) Travel Rule aim to bring more transparency to crypto transactions, requiring VASPs to share sender and recipient information. However, uneven implementation continues to undermine its effectiveness.
To close these gaps, VASPs must move beyond compliance checkboxes and adopt a genuinely risk-based approach to AML. This includes not only verifying identities but also conducting comprehensive customer assessments, ongoing monitoring, and reporting. Public-private partnerships between regulators, exchanges, and analytics providers can help align incentives and improve information sharing.
Ultimately, VASPs must recognize that AML compliance is more than a legal obligation — it’s a critical component of operational integrity and user trust.
Conclusion: AML in Web3 Demands an Integrated Approach
The future of AML in decentralized finance hinges on the integration of traditional and blockchain-specific compliance tools. Identity verification alone is insufficient. A complete AML framework must include robust customer due diligence, behavioral monitoring, suspicious transaction reporting, and investigative capacity — anchored in both on-chain analytics and off-chain intelligence.
VASPs that misinterpret CDD as mere identity verification expose themselves and the financial ecosystem to significant risk. By adopting a hybrid model that draws on the strengths of both conventional AML and blockchain analytics, stakeholders can more effectively detect, prevent, and respond to financial crime in the digital economy.
References
- Central Bank Flags Weaknesses in VASP AML/CFT Frameworks
- FATF calls for stronger crypto risk action globally | ICLG
https://www.fatf-gafi.org/en/publications/Fatfrecommendations/targeted-update-virtual-assets-vasps-2025.html
Web3 way of doing AML? (5 of 6) was originally published in The Capital on Medium, where people are continuing the conversation by highlighting and responding to this story.