An exclusive article by Fred Kahn
Crypto transactions moving across several chains are creating growing pain points for AML teams as legacy rule engines fail to capture the full path of these flows. Institutions report higher alert volumes, slower investigations, and reduced visibility when assets jump between multiple networks in rapid succession. Supervisors in major jurisdictions continue to stress that full traceability of digital asset transfers is essential for meeting monitoring obligations. The challenge intensifies when firms rely on monitoring frameworks originally designed for single-chain environments. These limitations now drive a widening operational burden for analysts handling complex multi-step activity.
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Multi-chain monitoring limits
Financial institutions originally adopted monitoring engines structured for linear value movement, which aligned with traditional single-chain activity. Multi-chain behavior disrupts this logic since digital assets can hop between networks, pass through bridges, or convert into tokenized representations that complicate automated tracing. Regulatory expectations require full visibility across all phases of a virtual asset transfer, yet older systems often lose context the moment a transaction exits its original network. These gaps create blind spots that criminals can exploit by routing assets through several chains to obscure origin points or mask intermediary steps.
Analysts often receive alerts linked only to the final portion of a multi-chain flow, while the earlier high-risk activity occurred on a network that the monitoring engine did not capture. This fragmentation weakens risk assessments and forces analysts to reconstruct flows manually through external tools. Multi-chain laundering typologies typically rely on rapid transfers through various infrastructures, increasing the difficulty of producing a coherent risk narrative. Institutions operating with static rule sets experience even greater strain because thresholds and logic designed for predictable activity cannot adapt to cross-chain patterns.
The issue becomes more complex when assets move through decentralized services that batch or reroute value in ways that legacy systems cannot interpret. Supervisors continue to state that understanding origin, intermediary points, and destination is essential for effective monitoring. Multi-chain flows challenge this requirement by scattering data across ecosystems with incompatible formats and limited linkage identifiers. The resulting disconnect places heavy pressure on AML teams responsible for producing accurate, regulator-ready case files.
Operational strain for AML analysts
Analysts increasingly report operational fatigue stemming from high alert volumes tied to fragmented blockchain visibility. A single investigation can involve several token types, wrapped assets, or bridged transactions, each requiring manual verification. Existing monitoring engines often lack the ability to combine these elements into a unified alert, causing investigations to stretch across multiple independent dashboards. Many teams rely on third-party blockchain intelligence tools to fill visibility gaps, but this introduces delays and forces analysts to validate evidence through internal procedures that depend on consistency and auditability.
Supervisors expect firms to maintain effective monitoring regardless of vendor arrangements, which means analysts must reconcile data sources before reaching any escalation decision. This slows investigation times during high-risk events such as potential sanctions evasion or cross-border movement involving restricted jurisdictions. Multi-chain laundering patterns often rely on complex routing to avoid detection, making the absence of consolidated visibility a significant risk factor. Analysts must reconstruct each hop of the flow to determine whether a suspicious outcome originated from an undisclosed chain.
Documentation requirements extend the workload further. Case files must show the complete lifecycle of the asset movement, with a clear explanation of why each step contributed to risk. When internal systems fail to provide the full sequence, analysts generate supplemental work manually, increasing the likelihood of inconsistencies. Institutions report that training new staff now requires deeper technical education due to the complexity of multi-chain behavior, including bridge mechanisms, smart contracts, and liquidity routing.
Technology gaps and regulatory expectations
Monitoring systems have struggled to adapt to the rapid expansion of multi-chain financial activity. Many firms still use engines designed for fiat or single network environments, which cannot interpret derivative token representations or synthetic versions of assets created in cross-chain transfers. Supervisory authorities reiterate that oversight obligations apply regardless of the underlying chain or mechanism used for movement. Institutions must therefore ensure the capability to detect suspicious behavior even when activity spans several blockchain ecosystems.
Cross-chain bridges present significant detection challenges. They serve as conversion points where illicit actors can obscure origin trails by swapping assets between networks that lack standardized data structures. Traditional monitoring logic often fails to link pre-bridge and post-bridge activities, producing alerts that represent only a fraction of the actual flow. Supervisors highlight the elevated risk associated with services that enable transfers without customer due diligence, especially when origin chains fall outside the scope of internal monitoring.
Additional complexity appears when assets move through decentralized liquidity pools. Transactions can be batched or executed within smart contract frameworks that obscure sequencing. Monitoring engines not designed for this behavior may interpret these steps as isolated events rather than components of a laundering route. The ongoing evolution of digital asset infrastructure requires institutions to continuously evaluate their monitoring capabilities to ensure alignment with supervisory expectations focused on traceability and risk mitigation.
Evolving supervisory focus
Authorities overseeing digital asset markets are increasingly focused on whether institutions can detect cross-chain laundering involving crypto transfers. The core expectation remains that firms maintain visibility across all transaction stages, regardless of the number of networks used. Institutions with outdated rule engines risk missing critical indicators, which can heighten regulatory attention when investigators cannot demonstrate effective oversight. Supervisors continue to emphasize that end-to-end monitoring must not be compromised by technological limitations.
Financial institutions respond by assessing monitoring upgrades, enhancing analytics capable of mapping multi-chain flows, and revisiting rule libraries that no longer reflect current typologies. Governance programs are expanding to include processes that ensure emerging cross-chain behaviors are embedded into detection logic. Training methods evolve accordingly, with a stronger focus on forensic skills that help analysts identify subtle indicators beyond legacy alert parameters. Institutions that adapt to these operational and regulatory realities will be better positioned to manage risks associated with the growing complexity of digital asset ecosystems.
- Key Points
- Crypto activity across multiple chains creates AML blind spots
- Fragmented alerts increase analyst workload and investigation time
- Supervisors expect full traceability across all chains and conversion points
- Legacy rule engines struggle with decentralized and cross-chain behavior
- Institutions are investing in upgraded monitoring and analytic capabilities
Related Links
- FATF Virtual Assets Guidance
- FATF Recommendations
- European Banking Authority AML Resources
- Financial Conduct Authority Financial Crime Publications
- FinCEN AML and Digital Assets Resources
Other FinCrime Central Articles About Transaction Monitoring
- Why Your Old Transaction Monitoring System Is Bleeding Your Budget Dry
- AI and Analytics Usher in a New Era of Transaction Monitoring in AML
- Understanding False Positives in AML Compliance
Some of FinCrime Central’s articles may have been enriched or edited with the help of AI tools. It may contain unintentional errors.
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