As Web3 continues to evolve, it presents both challenges and opportunities for anti-money laundering (AML) enforcement. The decentralized nature of blockchain networks complicates compliance efforts, but at the same time, blockchain’s transparency, immutability, and programmability offer powerful tools for detecting and preventing illicit financial activities. Unlike traditional financial systems, where centralized authorities oversee AML compliance, blockchain enables automated, data-driven approaches to risk assessment and monitoring. Here we explore key blockchain-based AML techniques, their effectiveness, and the challenges in integrating them with traditional compliance frameworks, also noting that over-reliance on such techniques and tools without building a solid AML capabilities.
Transaction Monitoring and Risk Scoring on Blockchain
One of the most effective ways to combat financial crime in Web3 is through real-time transaction monitoring and risk scoring. Blockchain transactions can be analyzed to detect suspicious patterns, with risk-scoring models assigning levels of concern based on factors such as source of funds, transaction size, frequency, and wallet associations. Automated alerts flag high-risk transactions for further investigation, helping compliance teams and regulators respond swiftly to potential threats.
Several blockchain analytics firms have developed sophisticated tools to support these efforts. KYT (Know Your Transaction) is a typical example which employs machine learning to identify high-risk transactions and track illicit wallet movements, and such tool can also monitor transactions across multiple blockchains to uncover suspicious behavior. Real-time risk scoring is also possible for recognizing and categorizing different financial institutions engaged in crypto transactions for necessary appropriate due diligence and control measures in terms of client onboarding consideration and ongoing transaction monitoring and investigation.
Despite these advancements, transaction monitoring on blockchain still faces challenges. Many wallets are not linked to real-world identities, making it difficult to trace the ultimate beneficiaries of suspicious activity. Criminals frequently use obfuscation techniques, such as layering transactions across multiple wallets and chains, which complicates detection. Additionally, false positives remain a concern, potentially placing unnecessary compliance burdens on legitimate users. While blockchain-based monitoring significantly enhances AML efforts, integrating these tools with off-chain KYC measures is essential for ensuring accurate risk assessments.
On-Chain Analytics and Forensic Investigation
Forensic investigation in the blockchain space relies on analyzing transaction histories to identify links between illicit addresses and financial crimes. Investigators utilize graph-based visualizations to track fund flows across multiple wallets and chains, while heuristics and clustering techniques help deanonymize wallet owners. These methods are particularly useful in tracing cryptocurrency movements associated with darknet markets, fraud schemes, and sanctioned entities.
Leading forensic tools, commonly developed and provdied by reputable KYT vendors, can provide law enforcement agencies and financial institutions with the ability to trace illicit transactions and predict financial crime risks. These tools have played a crucial role in recovering stolen funds and aiding investigations into crypto-related financial crimes.
However, forensic tracing in the cryptocurrency space is not without significant limitations. Privacy-centric cryptocurrencies such as Monero and Zcash, along with obfuscation tools like Tornado Cash, pose substantial challenges for investigators by concealing transaction origins and destinations. The increasing use of cross-chain transactions further complicates the tracing process, as assets can be moved fluidly across blockchains with varying degrees of transparency and oversight.
Although on-chain analytics tools have made considerable progress in identifying suspicious patterns and addresses, their effectiveness is constrained without regulatory cooperation — particularly from DeFi platforms and privacy-oriented blockchain projects. These gaps in compliance continue to create exploitable blind spots in the broader digital asset ecosystem.
A critical concern that deserves further emphasis is the role of centralized cryptocurrency exchanges themselves. Acting effectively as large-scale “mixers,” exchanges aggregate crypto inflows into internal wallets, after which outgoing transfers can no longer be reliably linked to their original sources. This is due to the creation and use of numerous wallet addresses within the exchange’s internal ledger system, which are neither publicly disclosed nor externally auditable. As a result, once funds enter such an exchange, forensic tracing often reaches a dead end — undermining transparency and hindering enforcement efforts.
Address Screening and Wallet Risk Profiling
Another critical AML tool in the blockchain space is wallet risk profiling, where crypto wallets are assessed based on transaction history and flagged if linked to illicit activities such as sanctions evasion, fraud, or darknet transactions. Virtual Asset Service Providers (VASPs) and financial institutions integrate these risk databases to prevent high-risk wallets from engaging in transactions.
Currently, reputable vendors offer wallet screening tools that allow exchanges and financial institutions to make more informed compliance decisions. These solutions help detect and prevent interactions with known illicit actors, while also supporting automated enforcement of internal compliance policies.
However, wallet screening is not without its shortcomings. Malicious actors can easily generate new wallets to evade detection, and many illicit transactions may involve wallets that have yet to be flagged, limiting the overall effectiveness of screening alone. Additionally, false positives remain a concern, as they can unintentionally restrict access to financial services for legitimate users. This underscores the importance of continuously refining risk-scoring algorithms to improve accuracy and reduce unintended impact.
Equally important is the ongoing maintenance of the databases that underpin these tools. Vendors must ensure timely updates and robust accuracy checks to maintain the reliability and relevance of wallet intelligence in a fast-changing threat environment.
