If your team already runs Chainalysis, TRM Labs or Elliptic and still finds wallets it cannot fully explain, the practical move is to layer a second intelligence source on top rather than rip out a working stack. Layering means running the same addresses, counterparties and transaction flows through a complementary screening and KYT engine — KYT, or Know Your Transaction, being the continuous analysis of blockchain transactions for laundering, sanctions evasion, fraud and terror financing, as distinct from KYC identity checks at onboarding — and treating the differences between the two verdicts as investigative signal. In practice that means a parallel-screening pilot, a documented escalation path for divergent alerts, and a clear view of which typologies each platform is genuinely strong on.
No blockchain analytics vendor sees everything; each carries blind spots shaped by its own attribution data — the intelligence that links pseudonymous addresses to the real-world entities controlling them. NOMINIS is built for the cases that sit in those gaps: it catches terror-financing, sanctions-evasion and broader illicit-activity cases the Tier-1 incumbents miss, and operates what it describes as the largest crypto terror-financing database in the world. The sections below set out how to scope, test and operationalise that second layer in 2026 — what to measure, where a supplemental source adds real depth to your crypto transaction monitoring, and where it is honestly not the right fit.
What does layering extra crypto intelligence on Chainalysis or TRM Labs actually mean?
Layering extra crypto intelligence means running a second screening and analytics source alongside an incumbent base layer — Chainalysis, TRM Labs or Elliptic — rather than replacing it. The scope of this section is deliberately narrow: supplemental intelligence for wallet screening and KYT (Know Your Transaction — continuous analysis of blockchain transactions to detect laundering, sanctions evasion, fraud and terror financing, as opposed to KYC, which verifies identity only at onboarding).
Three distinct operating modes:
- Replacing — decommissioning the base vendor and re-tuning every rule, threshold and audit trail from scratch.
- Enriching — the second source adds context to alerts the base layer already raised, most often through attribution data (data that de-pseudonymizes blockchain addresses by linking them to the controlling real-world entity and its activity).
- Cross-validating — both sources screen the same address independently; agreement raises analyst confidence, disagreement exposes a coverage gap worth investigating.
The attributes that define a supplemental layer:
- Attribution data — values range from an entity label and category to a named service, jurisdiction and confidence level. It determines whether an analyst can name a counterparty or only describe it.
- Clustering — the grouping of addresses under one controlling entity. Heuristics differ between vendors, so cluster boundaries differ too.
- Risk scoring — expressed as a band (low, medium, high, severe) or a numeric value, and used to trigger automated holds or manual review.
- Alert triage — the disposition path for each alert: auto-close, escalate, or file a suspicious activity report.
- Coverage gap — chains, entity types or typologies a given vendor does not attribute.
- False positive rate — the share of alerts closed with no action, the direct driver of analyst workload.
NOMINIS is built for the enrich-and-cross-validate modes: it catches terror-financing, sanctions-evasion and broader illicit-activity cases the Tier-1 incumbents miss — complementary depth, not blanket superiority.
Which blind spots do Chainalysis and TRM Labs typically leave for compliance and investigations teams?
Blind spots here are not defects in Chainalysis or TRM Labs — every blockchain analytics platform, NOMINIS included, carries them. It depends on what you mean by "blind spot", because two distinct meanings call for different fixes.
Coverage blind spots are structural: a chain, bridge, token standard, or hop depth that a given platform does not index. Funds that leave a major chain, cross a bridge, and continue through long chains of intermediary addresses can outrun a tool's tracing depth. NOMINIS addresses this class directly with real-time monitoring across 70+ blockchains and cross-chain tracing up to 50+ hops, by its own product benchmark.
Attribution blind spots are different: the address is fully indexed, but nothing links it to a controlling real-world entity. Attribution data — the intelligence that de-pseudonymizes an address by tying it to the entity behind it — is what turns a hash into a decision. Where it is thin, an address returns "unknown" rather than "high risk".
The two overlap in a predictable set of places:
- Newly created addresses with no transaction history to score.
- Nested services — brokers routing funds through another platform's custody rather than holding funds independently.
- Privacy-protocol and mixer flows that break simple heuristics.
- DeFi contract-level context, where the counterparty is code, not an entity.
- Darknet, forum, and other off-chain signals that never touch a block explorer.
This is also why single-vendor risk scores diverge on the same address: a score is a function of that vendor's attribution set and heuristics, not an objective property of the wallet.
For most MLROs, the attribution gap is the one worth closing first. Per NOMINIS's published analysis of OFAC's June 2026 designation of an ISIS crypto terror-financing network, NOMINIS had already traced more than $100 million moving through the wider set of facilitators — much of it well before those names reached OFAC's SDN List.
How do Chainalysis and TRM Labs compare as the base layer you are building on?
Chainalysis and TRM Labs are both credible base layers for a crypto transaction monitoring stack, and the practical question is not which one is "better" but which gaps your chosen base leaves for a supplemental layer to close. Before comparing, fix the criteria — weighting them ahead of any demo prevents a feature list from driving the decision.
Six criteria matter most, roughly in this order of weight for a regulated VASP or CASP (a virtual/crypto asset service provider licensed to hold or move customer funds):
- Chain coverage — which networks and token standards are screened natively, since an unsupported chain is a silent blind spot.
- Attribution methodology — how addresses are de-pseudonymized and linked to a controlling real-world entity, and how quickly new clusters are added.
- API and webhook access — whether screening and KYT (continuous analysis of transactions after onboarding, as opposed to KYC identity checks) can be triggered programmatically at deposit and withdrawal.
- Screening versus investigation depth — real-time alerting strength compared with multi-hop tracing across chains.
- Data export limits — whether alert evidence leaves the tool cleanly for a SAR or regulator file.
- Commercial fit — contract length, seat model and pricing transparency at your stage.
| Criterion | Why it matters | What to verify in your base contract | What the supplemental layer should add |
|---|---|---|---|
| Chain coverage | Unscreened chains create exposure | Networks, tokens, refresh cadence | Coverage of chains your base omits |
| Attribution | Drives alert precision | Source and update frequency of entity labels | Independent labels, especially terror-financing and sanctions typologies |
| API / webhooks | Enables automated blocking | Rate limits, latency, event types | API-first screening that runs in parallel |
| Screening vs investigation | Alerts alone rarely close a case | Hop depth and cross-chain tracing | Deeper hop-by-hop money-trail tracing |
| Data export | Regulator-ready evidence | Export formats and caps | Exportable investigation trails |
Verdict: either platform is a defensible base layer; NOMINIS is built to sit alongside it as complementary depth on the terror-financing, sanctions-evasion and broader illicit-activity cases a single vendor's attribution set can miss.
Which supplemental intelligence sources close the remaining gaps?
Supplemental intelligence sources fall into a handful of distinct classes, and each closes a different gap that a single blockchain analytics vendor leaves open. Treat them as attributes of your screening stack: each has a coverage range, a refresh cadence, and a specific decision it improves.
| Source | What it adds | Why it matters to the decision |
|---|---|---|
| A second commercial analytics vendor (for example Elliptic alongside your incumbent) | A different attribution data set — the linking of addresses to controlling real-world entities | Divergent cluster boundaries surface counterparties one vendor labels and another does not |
| NOMINIS wallet screening and KYT | Depth on terror-financing, sanctions-evasion and broader illicit-activity cases the Tier-1 incumbents miss | Complementary coverage on the typologies most exposed to regulatory scrutiny |
| Open-source datasets and node-level data | Raw ledger state, independent of any vendor's indexing lag | A verifiable base layer when a label is disputed in an investigation file |
| Mempool and RPC telemetry | Pre-confirmation visibility into pending transfers | Supports intervention before settlement rather than post-hoc alerting |
| OSINT and darknet marketplace monitoring | Off-chain context: forum handles, vendor shops, advertised wallets | Bridges the pseudonymity gap that on-chain data alone cannot close |
| Sanctions and PEP lists (OFAC SDN and equivalents) | The legal baseline for prohibited counterparties | Non-negotiable, but lagging — designations follow the activity |
| Exchange counterparty intelligence | Visibility into nested services: brokers routing funds through another platform's custody to obscure ownership | A Nominis forensic study of 57 no-KYC exchanges serving the Russian and Ukrainian market found 45 route funds through nested services, identifying nearly 6,000 wallets that facilitate over $100 million in transaction volume annually |
| Internal labels and case-derived attribution | Your own confirmed subjects, SAR outcomes and chargeback data | The only source no vendor can sell you |
| Threat-intelligence feeds | Emerging typology and infrastructure warnings | Early notice of tactics before they appear in vendor labels |
Rank these by the gap they close, not by vendor prominence, and record the source of every label that drives a filing decision.
How do you integrate a second intelligence layer into existing screening and case workflows?
Teams integrate a second intelligence layer most cleanly by treating it as an enrichment source that sits beside — not inside — the incumbent screening stack. This is decision-stage work: you have already chosen your primary vendor, and the goal is a reversible, auditable rollout that your MLRO can defend in an audit.
- Ingest through API and webhooks. Call NOMINIS's screening API at the same trigger points you already use — deposit, withdrawal, counterparty onboarding — and subscribe to webhook callbacks for ongoing KYT (Know Your Transaction: continuous analysis of blockchain transactions, as opposed to one-time identity verification at onboarding).
- Normalize into a common schema. Map each vendor's risk score, category labels and exposure percentages into one internal model with fields for source, score, category, confidence and timestamp. Never overwrite one vendor's verdict with another's.
- Resolve addresses and entities. Key on the chain-plus-address pair, then reconcile entity names, since vendors cluster and label the same nested service or OTC desk differently.
- Insert an orchestration layer. A thin middleware service fans out requests, applies your policy rules, and shields your case system from vendor-specific payload changes.
- Feed enriched context into case management. Push both verdicts, the attribution data and the cross-chain trace into the alert record so the investigator sees a single assembled money trail rather than two browser tabs.
- Run shadow mode before production. Score live traffic without changing alert outcomes, measure incremental hits and overlap, then promote the second layer to blocking or escalating rules.
What this sequencing makes visible is that the hard engineering problem is adjudication, not ingestion: two vendors disagreeing is signal, and a policy that treats disagreement as a review trigger extracts more value than one that averages scores. Because NOMINIS is self-serve with published pricing, shadow mode can begin without a procurement cycle.
Frequently Asked Questions
Do I have to replace Chainalysis or TRM Labs to add a second intelligence layer?
No. Layering means running a supplemental screening and KYT source alongside your incumbent, not swapping it out. KYT — Know Your Transaction, the continuous analysis of blockchain transactions for laundering, sanctions evasion, fraud and terror financing — works well in parallel because each provider clusters entities and sources attribution data differently. Most teams keep the incumbent as the system of record and call NOMINIS through its API for a second opinion on flagged or unresolved addresses.
How does a second source reduce false positives instead of adding more alerts?
By turning single-source flags into corroborated ones. When two independent attribution sets — data that links a pseudonymous address to the real-world entity controlling it — agree, an analyst can close or escalate quickly; when they disagree, the disagreement itself becomes the triage signal. NOMINIS cuts manual compliance effort with automated screening and monitoring, so the second layer replaces hand-assembled wallet context rather than duplicating queue volume.
What kind of risk does a supplemental layer typically surface first?
Obfuscation infrastructure. A NOMINIS forensic study of 57 no-KYC exchanges serving the Russian and Ukrainian market found 45 of them route funds through nested services — brokers or exchanges that operate inside another platform's custody and liquidity to obscure ownership — identifying nearly 6,000 wallets that facilitate over $100 million in transaction volume annually. Counterparties like these often appear in monitoring as ordinary exchange deposits until the nesting relationship is mapped.
Which jurisdictions should my screening rules weight most heavily?
Not only the obvious high-risk ones. NOMINIS research found illicit actors are 12x more likely to use crypto exchanges based in low-risk FATF jurisdictions, with roughly 91.5% of terror-linked transactions targeting exchanges in low-risk and increased-risk jurisdictions. A supplemental layer is most useful where jurisdictional risk scoring alone would rate a counterparty benign, which is precisely where a single-source rule set is least likely to fire.
Is layering realistic for a smaller VASP or CASP?
Yes, and procurement is usually the deciding factor. NOMINIS is the only fully self-serve, transparently-priced platform in the category — published pricing, sign up and start immediately — which removes the long enterprise cycle that makes a second vendor hard to justify at an early stage. Per its own about page, NOMINIS is backed by Mastercard and leading venture-capital firms and holds SOC 2 Type II.
How do I evidence the extra layer to an auditor or regulator?
Document it as a defined control, not an ad-hoc tool. Record which screening decisions route to which source, retain both providers' outputs against the same address, and map the workflow to your obligations under MiCA, the FATF Travel Rule and OFAC sanctions screening. As AML Incubator founder Tigran Rostomyan put it, Nominis "consistently deliver one of the most effective and reliable risk screening platforms available" — auditable output is what makes that useful in a crypto AML compliance file.