Crypto money laundering hit a record $82 billion in 2025, according to new research from blockchain analytics firm Chainalysis, reported by Reuters and confirmed by outlets including U.S. News, Bloomberg, and CNBC. That figure is more than eight times the roughly $10 billion Chainalysis estimated for 2020, and it lands at a moment when crypto compliance teams are leaning harder than ever on blockchain analytics tools, wallet-screening APIs, and transaction-monitoring engines to keep pace. For software teams building the infrastructure behind crypto money laundering tracing, the report is also a case study in what happens when criminal networks professionalize faster than the tooling built to catch them.
This is a news analysis of what the $82 billion number actually means, how Chainalysis says it builds these estimates, why a cluster of Chinese-language money-laundering networks (CMLNs) now accounts for roughly a fifth of the illicit crypto ecosystem, and what the data means for the engineering teams who build blockchain forensics software for a living.
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Chainalysis’s 2025 Numbers: $82 Billion and Climbing
Chainalysis’s latest crime report puts the total on-chain crypto laundering volume for 2025 at “at least” $82 billion, a figure Reuters first reported on January 27, 2026 and that U.S. News, MLex, and Yahoo Finance have since repeated. The company says the real number is probably higher, since any wallet-attribution methodology misses addresses that haven’t yet been linked to a known entity, mixer, or laundering service. That caveat matters: Chainalysis itself describes the estimate as a floor, not a ceiling.
The jump from $10 billion in 2020 to $82 billion in 2025 reflects both the growth of the crypto market itself and the maturing of laundering-as-a-service networks that didn’t really exist in their current form five years ago. Crypto adoption climbed through that stretch, and so did the sophistication of the groups moving dirty money through it. Reporting from CNBC frames the growth less as a crypto-specific crime wave and more as organized crime migrating its existing playbook onto rails that move faster and cross borders more easily than cash ever could.
How the $82 Billion Estimate Was Built
Chainalysis describes its methodology as connecting real-world criminal activity to on-chain records using a mix of machine learning models and human forensic analysts. In practice, that means clustering wallet addresses into entities, tagging those entities against known services (exchanges, mixers, darknet markets, sanctioned addresses), and then tracing fund flows through hops, bridges, and stablecoin conversions until the money either exits to fiat or goes cold.
A Chainalysis spokesperson declined to give Reuters additional detail on the specific modeling techniques behind the $82 billion figure, which is typical for a company whose entire commercial value rests on proprietary attribution methods. That reticence also means outside researchers can’t fully audit the number, a limitation several compliance engineers have flagged as the tradeoff for using any closed-source blockchain forensics platform instead of building detection logic in-house.
For developers who build on top of this kind of data, the methodology question isn’t academic. Wallet-screening APIs and compliance SDKs only earn trust if their false-positive and false-negative rates hold up under scrutiny, and vendors that won’t publish methodology details are asking enterprise clients and regulators to take accuracy on faith.
The $16.1 Billion Engine: Chinese-Language Laundering Networks
The most specific and arguably most newsworthy part of the Chainalysis report is its breakdown of Chinese-language money-laundering networks, a category the firm calls CMLNs. Chainalysis says it identified nearly 1,800 active wallets tied to these networks, which collectively processed about $16.1 billion in cryptocurrency during 2025. That works out to close to $44 million a day moving through CMLN-linked wallets, a pace that Bloomberg and The Record both describe as evidence of a maturing, professionalized criminal supply chain rather than a scattering of opportunistic scammers.
Chainalysis says CMLNs now represent roughly 20% of the entire illicit cryptocurrency ecosystem it tracks, a share large enough to make them one of the single biggest categories in the crime report rather than a footnote. The firm traces the origin of these networks back to the COVID-19 pandemic, when cross-border movement restrictions and a surge in online scam operations across Southeast Asia pushed criminal groups toward crypto rails that didn’t require physical cash handoffs.
Chainalysis is careful to note that the CMLN label describes a set of Chinese-language services and communities, not a single centralized organization and not a strictly China-based operation. The networks span jurisdictions, and the “Chinese-language” framing refers to the primary language used in recruitment, marketing, and coordination rather than national origin of every participant.
Inside the Telegram “Guarantee” Market
Much of the CMLN activity runs through Telegram, which Chainalysis and multiple outlets describe as the primary channel for advertising laundering services, recruiting new customers, and coordinating transactions. The platforms at the center of this activity are known as “guarantee” markets: informal escrow-style marketplaces that vouch for sellers, hold funds in dispute scenarios, and build the kind of trust that lets strangers transact with criminal service providers without a formal contract.
These guarantee platforms don’t necessarily execute every underlying blockchain transfer themselves. Instead, researchers describe them as marketing, matching, and dispute-resolution infrastructure that sits on top of the actual laundering transactions, which can route through dozens of other wallets and services. That structure gives the broader network resilience: taking down one Telegram channel, wallet cluster, or guarantee platform rarely disrupts the rest of the ecosystem, since the underlying service providers can simply regroup under a new storefront.
This isn’t a new enforcement challenge. U.S. authorities previously took direct action against one high-profile guarantee market, sanctioning Xinbi Guarantee and freezing $52 million in crypto tied to the platform. The Chainalysis data suggests that enforcement action against individual guarantee markets hasn’t meaningfully dented the broader CMLN ecosystem’s throughput, which is exactly the resilience problem compliance teams and law enforcement keep running into.
The Developer Stack Behind Blockchain Forensics
None of this tracing happens without software, and that’s where the story intersects with the coding-tools world. Blockchain analytics vendors sell their capabilities to exchanges, wallets, and fintech platforms through a handful of standard integration patterns: REST APIs for wallet risk scores, SDKs that embed screening logic directly into an application, and in some cases widgets or iFrames for teams that want a faster front-end integration without touching backend infrastructure.
Scorechain, one vendor in this space, publicly documents a unified API and SDK that expose wallet screening, transaction monitoring, graph analysis, and counterparty data through a single integration layer, illustrating the pattern most blockchain forensics platforms now follow. A typical integration looks something like this at the code level:
// Pseudocode: screening a deposit address before crediting funds
const riskScore = await blockchainAnalyticsAPI.screenAddress({
address: depositAddress,
chain: "ethereum",
includeCounterparties: true
});
if (riskScore.sanctionsMatch || riskScore.riskLevel === "severe") {
await flagForManualReview(transaction);
} else {
await creditAccount(transaction);
}Engineering teams typically wire this kind of check into several points in a product: account creation, pre-deposit screening, transaction authorization, withdrawal approval, and periodic back-book reviews of existing customers. Each checkpoint adds latency and false-positive risk, which is why most platforms combine a vendor’s risk score with internal rules tuned to their own customer base, transaction size thresholds, and jurisdictional exposure rather than treating any single API response as a final verdict.
Graph-analysis systems sit a layer deeper in this stack. Instead of scoring a single address, they model entire clusters of wallets, exchanges, mixers, and bridges as a connected graph, which is what lets a platform like Chainalysis trace funds through several intermediate hops instead of only flagging a transaction that touches a sanctioned address directly. That graph-based approach is also what underpins cross-chain analytics, since laundering flows increasingly hop between networks via bridges and stablecoin swaps specifically to break up the trail a single-chain tool would see.
Why AI Coding Tools Matter to Compliance Engineering
Building and maintaining this kind of forensics infrastructure is a software engineering problem as much as it is a financial-crime problem, and that’s increasingly showing up in how compliance and risk engineering teams write code. The 2025 Stack Overflow Developer Survey found that 84% of respondents use or plan to use AI tools in their development process, and 51% of professional developers now use AI coding tools daily, according to Stack Overflow.
Among developers who use out-of-the-box AI assistants, 67.9% had used GitHub Copilot and 40.8% had used Claude Code, per the same survey. Those adoption rates matter for teams maintaining the kind of transaction-monitoring pipelines, address-screening integrations, and case-management APIs this article describes, since AI-assisted coding tools are increasingly the default way engineers write and review the detection rules that flag laundering patterns like structuring, peeling chains, and mixer exposure.
Gartner projects that 90% of enterprise software engineers will use AI code assistants by 2028, up from less than 14% in early 2024, according to its 2025 research on strategic software engineering trends. If that holds, the teams building the next generation of blockchain forensics tooling, entity-resolution systems, and compliance SDKs will be writing most of that code with AI assistance baked into their workflow by default, not as an experimental add-on.
Competitive Landscape: How Blockchain Analytics Vendors Compare
Chainalysis is the most frequently cited name in crypto-crime reporting, but it isn’t the only vendor selling blockchain forensics software to exchanges, banks, and government agencies. The category also includes firms building competing wallet-screening and transaction-monitoring platforms, each pitching some mix of API-first integration, SDK embedding, and investigator-facing graph tools aimed at the same compliance teams.
The practical differentiator for most engineering teams evaluating these platforms isn’t raw coverage claims, which are hard to verify independently, but integration depth: whether a vendor exposes a full API and SDK for custom workflows, versus a narrower dashboard-only product that forces compliance analysts to work outside their existing case-management tools. Teams already running secrets-management infrastructure like HashiCorp Vault or a comparable secrets manager to store API keys for these blockchain analytics integrations tend to prefer vendors with mature SDKs over ones that only offer a web console.
Privileged-access management is the other piece of this puzzle that rarely gets discussed in crypto-crime coverage. Any team with API credentials capable of pulling wallet-attribution data or triggering a sanctions freeze is sitting on a high-value credential, which is exactly the kind of access that platforms compared in pieces like CyberArk vs. BeyondTrust vs. Delinea are built to lock down. A leaked or over-provisioned API key into a blockchain forensics platform is arguably a bigger single point of failure than a compromised customer wallet.
Market Impact: Exchanges, Regulators, and Compliance Budgets
An $82 billion laundering estimate puts direct pressure on exchanges, custodians, and payment processors to prove their screening stack catches more than it misses. Regulators in multiple jurisdictions have already tightened licensing requirements for crypto platforms this year, and firms facing scrutiny over weak anti-money-laundering controls, such as the regulatory questions raised around Coins.ph’s license risk with the Philippine central bank, show how quickly a compliance gap can turn into an existential business problem rather than a line-item fine.
For blockchain analytics vendors, the report is effectively a sales pitch written by a third party: a bigger laundering number justifies bigger compliance budgets, and bigger compliance budgets mean more enterprise contracts for API access, SDK licenses, and case-management seats. That dynamic isn’t unique to crypto. Security vendors across categories have long benefited when industry-wide breach or fraud numbers climb, since the data makes the budget conversation with a board easier for compliance and security leaders to win.
The CMLN-specific findings also put pressure on Telegram, which has faced recurring criticism over how its channels and groups get used to coordinate illegal activity ranging from laundering to broader cybercrime markets. Whether Telegram changes its moderation posture in response to this specific report remains to be seen, and nothing in the current reporting suggests a policy shift is imminent.
Historical Context: From Early Mixers to Professionalized Networks
Crypto laundering has gone through a few distinct eras since Bitcoin’s early years, when simple mixing services and darknet marketplaces represented most of the illicit volume blockchain analysts tracked. The 2020 estimate of roughly $10 billion reflected an ecosystem still dominated by ransomware payouts, darknet market proceeds, and relatively unsophisticated cash-out methods.
The jump to $82 billion in five years tracks with the broader professionalization of cybercrime generally, a trend visible well beyond crypto laundering specifically. The ransomware economy alone has grown into a multi-billion-dollar criminal industry with its own affiliate programs, leak sites, and negotiation playbooks, a structure detailed in prior reporting on how the ransomware economy actually works. CMLNs fit the same pattern: criminal groups borrowing organizational structure from legitimate business, complete with marketing channels, customer service, dispute resolution, and specialization between the people who steal funds and the people who launder them.
Stablecoins have also reshaped the laundering landscape since 2020. Their price stability and liquidity make them a preferred vehicle for moving large sums quickly compared with volatile assets, and outlets covering the broader $82 billion figure have noted that a large share of illicit flows now route through stablecoins rather than Bitcoin, a shift that barely registered in 2020-era laundering reports.
Key 2025 Crypto Laundering Metrics
| Metric | Figure | Source |
|---|---|---|
| Total on-chain crypto laundering, 2025 | At least $82 billion | Chainalysis, via Reuters/U.S. News |
| Comparable estimate, 2020 | Approximately $10 billion | Chainalysis |
| CMLN crypto processed, 2025 | $16.1 billion | Chainalysis, via Bloomberg/CNBC |
| CMLN active wallets identified | Nearly 1,800 | Chainalysis |
| CMLN average daily throughput | Nearly $44 million/day | Chainalysis |
| CMLN share of illicit crypto ecosystem | Roughly 20% | Chainalysis |
| CMLN emergence period | COVID-19 pandemic era | Chainalysis |
Blockchain Forensics Tool Categories for Developers
| Tool category | What it does | Typical integration point |
|---|---|---|
| Blockchain analytics APIs | Wallet risk scores, sanctions exposure, entity attribution | Account onboarding |
| Compliance SDKs | Embed screening and travel-rule checks directly in-app | Wallet and exchange apps |
| Graph-analysis engines | Model address clusters, mixers, and bridges as a graph | Investigation case tools |
| Address-screening services | Screen deposit/withdrawal addresses against risk lists | Transaction authorization |
| Transaction-monitoring engines | Flag structuring, peeling chains, chain-hopping | Real-time alerting |
| Cross-chain analytics | Track funds across bridges, wrapped assets, stablecoins | Multi-chain dashboards |
| Case-management APIs | Assign alerts, attach evidence, generate audit trails | Compliance operations |
What Comes Next: Five Predictions
- The 2026 estimate will likely top $82 billion again. Chainalysis itself frames the current figure as an undercount, and the trend line from $10 billion to $82 billion over five years shows no sign of flattening.
- Regulators will push harder on Telegram-adjacent “guarantee” markets. Expect more sanctions actions modeled on the Xinbi Guarantee case as agencies try to disrupt the trust infrastructure that lets CMLNs operate at scale.
- Compliance SDK adoption will keep climbing among mid-size exchanges. Smaller platforms that previously relied on manual reviews are likely to adopt API-based screening as licensing requirements tighten in multiple jurisdictions.
- AI-assisted development will reshape how detection rules get written and maintained. With daily AI tool usage already above 50% among professional developers, compliance engineering teams are likely to lean further into AI-assisted code review for transaction-monitoring logic.
- Stablecoin-specific monitoring tools will see new investment. As laundering volume continues shifting toward stablecoins, expect blockchain analytics vendors to expand cross-chain and stablecoin-specific tracing features rather than treating them as a secondary feature of existing products.
Frequently Asked Questions
How much crypto did Chainalysis say was laundered in 2025?
Chainalysis estimated at least $82 billion in on-chain crypto laundering during 2025, a figure first reported by Reuters on January 27, 2026 and confirmed by outlets including U.S. News, Bloomberg, and MLex.
What are Chinese-language money-laundering networks (CMLNs)?
CMLNs are a category of laundering networks that Chainalysis says emerged during the COVID-19 pandemic and primarily advertise their services through Chinese-language Telegram channels. They processed an estimated $16.1 billion in crypto during 2025, roughly 20% of the illicit crypto ecosystem Chainalysis tracks.
What is a Telegram “guarantee” market?
A guarantee market is an informal escrow-style platform on Telegram that vouches for sellers of illicit services, holds funds in disputes, and helps establish trust between laundering-service providers and their customers. These platforms often handle marketing and dispute resolution rather than directly executing every blockchain transaction.
How does Chainalysis calculate its laundering estimates?
The company says it connects real-world criminal activity to on-chain records using machine learning models combined with human forensic analysts, clustering wallet addresses into entities and tracing fund flows across hops, bridges, and stablecoin conversions. A Chainalysis spokesperson declined to share further methodology details with Reuters.
Is the $82 billion figure likely to be accurate?
Chainalysis describes the estimate as a likely undercount, since wallet attribution is inherently incomplete and new laundering wallets surface constantly. Independent researchers cannot fully audit the number because Chainalysis treats its exact modeling methodology as proprietary.
What tools do developers use to build blockchain forensics software?
Common categories include blockchain analytics APIs for wallet risk scoring, compliance SDKs for embedding screening logic, graph-analysis engines for mapping transaction clusters, and cross-chain analytics for tracking funds across bridges and stablecoins. Vendors like Scorechain publish unified API and SDK access covering several of these functions at once.
Has anyone been sanctioned over these laundering networks?
Yes. U.S. authorities previously sanctioned the Xinbi Guarantee platform and froze $52 million in associated crypto, one of the few concrete enforcement actions taken against a Telegram-based guarantee market tied to this ecosystem.
