Case Study
False positives
Legitimate activity is incorrectly flagged as risky or illicit.
Underlying data usually fails in one of three ways:
Impacts of false positives
When good actors are flagged as bad – such as attributing an address as belonging to a sanctioned entity, to an exchange, or decentralized service, when it doesn’t – the damage can add up fast.
False positives in crypto investigations
- Wasted investigative time following clean flows instead of real threats.
- Risk of wrongful freezes or prosecutions when people or services are misidentified as illicit or sanctioned.
- Erosion of credibility with courts, partners, and the public if evidence has to be walked back.
False positives in crypto compliance
- Alert overload that drags down compliance productivity.
- Incorrectly blocked transactions and offboarding of legitimate customers, hitting revenue and growth.
- Bad customer experiences from frozen funds and unnecessary case escalations.
With Chainalysis
Act with confidence
Because Chainalysis data has been independently shown to achieve ~0.01% false positives, customers can be confident when our systems say “high risk”.
Seizing or freezing funds requires rock‑solid proof and credible insight. Around the world, agencies have recovered and frozen more illicit crypto with Chainalysis than with any other provider, a direct result of the reliability and defensibility of our data.
Impacts of false negatives
On the flip side, when bad actors are overlooked, and investigators fail to see exposure to sanctioned entities, darknet markets, or known scam infrastructure, the risks don’t just hide, they compound quietly in the background.
False negatives in crypto investigations
- Missed links to real threat actors so criminal networks stay active and evolve instead of being disrupted.
- Lost seizure opportunities when funds move through un-attributed services before anyone realizes they’re illicit.
- Incomplete case narratives that surface only part of the money trail, weakening charges and making it harder to secure convictions.
- Victims left exposed when repeat patterns of fraud or abuse aren’t connected back to the same wallets, services, or networks.
False negatives in crypto compliance
- Undetected exposure to sanctions, terrorist financing, and other high‑risk entities that can lead to regulatory breaches, fines, and enforcement actions.
- A false sense of security that materially understates the real level of risk on your platform.
- Eroded trust with regulators, banking partners, and customers when “clean” activity is later revealed to have been linked to serious illicit behavior.
With Chainalysis
Catch the threats others miss
Chainalysis data has been independently validated to achieve up to 95% coverage of known illicit service addresses. Customers can be confident they’re not missing hidden exposure or critical leads when our systems say “low risk.”
Case Study
Uncovering Iran’s hidden sanctions exposure
Our research into Iran’s crypto ecosystem shows how deep visibility into illicit networks helps reduce false negatives at scale. While other vendors have indicated a decrease in sanctioned activity, Chainalysis identified that addresses associated with the Islamic Revolutionary Guard Corps (IRGC) account for ~50% of Iran’s total crypto activity in Q4 2025, with IRGC‑linked wallets receiving over $2B in 2024 and more than $3B in 2025.
CASE STUDY
Exposing industrial‑scale laundering networks others miss
Chainalysis identified Chinese‑language money laundering networks that now process about 20% of known illicit crypto laundering, moving $16.1B in 2025 alone (~$44M per day) across 1,799+ active wallets. These networks have grown 7,325x faster than flows to centralized exchanges since 2020, and we’ve mapped six distinct laundering typologies with clear on‑chain behavioral fingerprints.
Impacts of unclear confidence levels
Many blockchain tools blend hard evidence and probabilistic guesses into the same view. That makes it hard to know when you’re looking at something you can act on or just a model’s best guess.
This matters because:
- Investigators may treat low‑confidence labels as facts, weakening cases or forcing walk‑backs later.
- Compliance teams don’t know which alerts to prioritize vs. which are just directional, so policies and playbooks get fuzzy.
How Chainalysis handles confidence levels responsibly
Evidence-based attributions
Our vetted entities are built on directly observable, auditable proof and are treated as ground truth.
Signals
Clearly separated layer of blockchain intelligence, categorizing previously unidentified addresses based on what they appear to be doing on-chain and confidence level. Learn more
We found that Chainalysis’ coverage of cryptocurrencies and data in both KYT and Reactor was, by far, outperforming its competitors.
Eva Crouwel, Head of Financial Crime, Luno
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