AI + Blockchain: Fighting Financial Crime & AML
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Illicit cryptocurrency addresses received at least $154 billion in 2025, a 162% jump from the year before, according to Chainalysis's 2026 Crypto Crime Report. Of that, money launderers moved at least $82 billion, and stablecoins — cryptocurrencies pegged to the US dollar — accounted for 84% of the illicit transaction volume the firm tracked. Machine learning is the primary tool investigators now use to find that money on a public, permanent ledger. This piece explains how it works, who builds it, and where it still fails.
Educational only — not financial advice. This article explains compliance technology and regulatory mechanisms; it does not recommend any investment, token, or trading strategy. Consult a licensed financial advisor or compliance professional for guidance specific to your situation.
Why a "transparent" ledger became a laundering tool
Every Bitcoin or Ethereum transaction is recorded permanently and visible to anyone who wants to look. That sounds like the opposite of a criminal's dream. But transparency and identity are two different things. Blockchains show that wallet A sent 3 BTC to wallet B; they do not show who owns wallet A or wallet B. That gap — pseudonymity without identity — is the same design flaw that makes it possible to trace stolen funds forensically once you know what to look for.
Traditional banks fight money laundering through three overlapping controls: KYC (Know Your Customer, verifying identity when an account opens), transaction monitoring (flagging large or unusual transfers), and mandatory reporting to a financial intelligence unit such as the US Treasury's Financial Crimes Enforcement Network (FinCEN). Those controls work because banks already know who their customers are.
Crypto broke that assumption in three ways. Decentralized exchanges let people swap tokens without ever creating an account. Self-hosted wallets (software or hardware the user controls directly, with no company in between) carry no identity information at all. And a small category of "privacy coins" encrypts sender, receiver, and amount by design. Regulators spent roughly a decade catching up to this, and the catch-up mechanism was, overwhelmingly, machine learning applied to public blockchain data.
How machine learning actually finds the criminals
The core insight behind blockchain forensics is that a wallet address is not a person, but a person's behavior leaves fingerprints across many addresses. Four techniques, layered together, turn that behavior into evidence.
Wallet clustering: turning addresses into entities
Investigators rarely care about a single address; they care about who controls it. Clustering algorithms group addresses that are very likely controlled by the same entity, based on patterns like co-spending (multiple addresses used as inputs to the same transaction, which usually means one wallet software combined them), shared change addresses, and near-identical timing or amount patterns across supposedly separate wallets.
This is conceptually similar to how a detective links a string of burglaries by matching a distinctive tool mark left at each scene. No single mark proves who did it, but the pattern across dozens of scenes narrows the suspect pool dramatically. Chainalysis has stated publicly that its clustering routinely collapses tens of thousands of raw addresses into a much smaller number of real-world entities, though the company does not publish an exact current ratio.
Risk scoring: rating a transaction before it settles
Once addresses are clustered into entities, each new transaction gets scored against questions like: Has this entity been linked to a sanctioned party, a known scam, or a darknet market? Is the transfer amount typical for this sender, or a sudden spike? Does the routing resemble known laundering techniques — structuring (many small transfers to dodge reporting thresholds), layering (routing funds through several intermediaries to obscure origin), or integration (converting crypto back into cash or goods)?
The output is usually a numeric risk score, similar in spirit to a credit score but inverted: higher means more dangerous. Exchanges use this score to decide whether to process a withdrawal automatically, hold it for manual review, or freeze it and file a report.
Anomaly and pattern detection: learning what crime looks like
Supervised learning models train on historical cases — transactions that were later confirmed, through investigation or prosecution, to be tied to laundering, ransomware, or terrorism financing. The model learns the shape of those patterns: ransomware payments tend to arrive as one large lump sum and then fan out quickly into mixing services; terrorism financing often looks like many small transfers to geographically dispersed recipients; drug-trafficking proceeds frequently show up as frequent small deposits that later consolidate into larger withdrawals.
A new transaction that matches one of these learned shapes gets flagged for human review, not automatic action. This is the same statistical logic your bank's card-fraud algorithm uses when it texts you about an unusual purchase — trained on past fraud, applied to a new event, with a human making the final call.
From flag to arrest
A flagged transaction typically generates a Suspicious Activity Report (SAR), filed with FinCEN in the United States or the equivalent financial intelligence unit elsewhere. Analysts there can subpoena the exchange that eventually touched the funds — because at some point, laundered crypto usually needs to convert back to a bank-usable currency, and every registered exchange has KYC records. That off-ramp is where pseudonymous blockchain activity meets a real name.
Inside the model: how graph neural networks read layering and mixing
The clustering and rule-based scoring described above are the foundation, but the frontier of blockchain forensics runs on a specific class of machine learning model built for exactly this shape of problem: the graph neural network (GNN).
A blockchain transaction ledger is, structurally, a graph. Wallets are nodes; transactions connecting them are edges, each carrying attributes like amount, timestamp, and fee. Traditional machine learning models expect flat, tabular data — rows and columns — which throws away the very thing that matters most in laundering detection: how money moves through a network of addresses, not just what any single address looks like in isolation. GNNs were built to solve that mismatch. They pass information along the edges of the graph itself, so a node's learned representation reflects not just its own transaction history but the history of everything connected to it, several hops out.
That matters because layering — the deliberate practice of routing funds through many intermediate wallets to obscure their origin — is fundamentally a graph pattern, not a property of any single transaction. A GNN can learn to recognize the shape of a layering chain: a lump sum splitting into a fan of smaller transfers, those transfers recombining downstream, and the combined amount eventually converging on a small number of cash-out points. Academic researchers describe this as a core advantage of graph-based models over row-by-row anomaly detection: GNNs combine structural representation learning (the shape of the network around a node) with temporal modeling (when transfers happen relative to each other), which is closer to how a human investigator actually reads a case file than a simple rules engine is, according to a 2024 review of GNN methods for financial fraud detection published on arXiv.
Mixing services complicate this further because their entire design goal is to defeat exactly the pattern-matching a GNN relies on: many users' funds pool together and get redistributed, so the graph edge between a specific deposit and a specific withdrawal is deliberately severed. Recent research has responded by adding a second dimension to the graph model — frequency. A 2025 approach called ChronoWave-GNN, published in Scientific Reports, jointly models transaction patterns across both time and frequency domains specifically because laundering activity tends to show up as two overlapping signatures: rapid bursts of mixer-style activity layered on top of slow, low-frequency structuring that spreads transfers out to avoid drawing attention. A model that only looks at graph structure, without also weighing how transaction timing clusters or spreads, misses one half of that signature.
A useful analogy: reading a single transaction to spot laundering is like judging a chess move in isolation. It might look unremarkable on its own. A GNN instead evaluates the whole board — every piece's position and how it got there — the way a chess engine assesses a move by its effect on the entire game state, not just the square it lands on. The 2025 academic literature broadly agrees that this graph-native approach, especially when combined with semi-supervised training on the small number of confirmed criminal cases available (most transactions on any public ledger are never labeled as licit or illicit), is now the leading direction for AML research in blockchain settings, according to a review published in Advanced Intelligent Systems by Chen et al. in Wiley Online Library.
None of this makes the models infallible. GNN-based detection still produces false positives, still struggles with fully-encrypted privacy coin ledgers where the graph itself is partially hidden, and still needs a human analyst to convert a flagged pattern into a filed report. What it changes is the ceiling on what pattern a machine can plausibly recognize — from "this one transaction looks odd" to "this cluster of 40 wallets across three weeks moves money the way a known ransomware payout does."
Cross-chain bridges: the newest laundering frontier — and detection gap
Money laundering used to mean moving funds within a single blockchain. Increasingly, it means moving funds between blockchains, and that shift has opened one of the largest gaps in current forensic tooling.
A cross-chain bridge is software that lets a user move an asset from one blockchain to another — say, from Ethereum to a faster or cheaper chain — typically by locking the original asset in a smart contract on the source chain and minting an equivalent representation on the destination chain. Bridges solve a real interoperability problem: different blockchains don't natively talk to each other, so bridges act as the translation layer. That translation layer is also, structurally, a single point where a huge amount of value concentrates, which makes it an attractive target.
Cross-chain bridges and related liquidity protocols have lost more than $2.8 billion cumulatively to exploits since 2021, according to Chainalysis. The pattern has continued: 2026 alone saw the Kelp DAO exploit, in which attackers took roughly $292 million (116,500 rsETH) on April 18, 2026, making it the year's largest single crypto theft to that point, with DeFi losses across the ecosystem topping $750 million by mid-April, according to reporting aggregated by Phemex. THORChain, a major cross-chain liquidity protocol, faced its own roughly $200 million debt crisis in early 2025 that nearly broke its solvency model, and its founder was personally targeted in a separate $1.3 million exploit in September 2025 linked to suspected North Korean threat actors, according to CryptoTimes.
Why do bridges matter so much to money laundering specifically, beyond the theft itself? Two reasons. First, once stolen funds cross a bridge, they inherit an entirely new set of addresses on the destination chain, with no native, cryptographically enforced link back to the source-chain wallet that received them — investigators have to reconstruct that link from off-chain bridge contract logs and timing correlation rather than reading it directly off either ledger. Second, laundering operations use legitimate bridges the same way, moving clean-looking funds across chains specifically to break the continuity that single-chain clustering algorithms depend on, the same chain-hopping technique described earlier in this article, just executed at bridge-contract scale rather than wallet-to-wallet.
Forensic firms have responded by building tracing tools that follow assets across dozens of chains simultaneously rather than treating each chain as a separate investigation. This is genuinely harder than single-chain tracing: it requires correlating a lock event on chain A with a mint event on chain B using timing, amount, and bridge-contract metadata rather than a single shared ledger entry, and bridge protocols vary enough in design that a technique built for one bridge often doesn't transfer cleanly to the next. Cross-chain tracing succeeds less consistently and takes longer than single-chain tracing as a result, which is part of why bridge exploits remain, in the industry's own description, a persistent top-tier security and laundering risk rather than a solved problem.
How the leading compliance vendors differ, conceptually
Three firms dominate public discussion of blockchain forensics: Chainalysis, Elliptic, and TRM Labs. All three build on the same underlying techniques — wallet clustering, risk scoring, and pattern-matching against known-criminal transaction shapes — but they've grown distinct areas of emphasis based on which part of the compliance problem their earliest customers cared most about. The comparison below reflects each firm's publicly stated focus and market position; none of these figures should be read as a technical benchmark, since none of the three publishes head-to-head accuracy comparisons against competitors.
| Firm | Founded / origin | Primary customer base | Publicly stated area of emphasis |
|---|---|---|---|
| Chainalysis | 2014, US | Law enforcement agencies, banks, exchanges globally | Broadest coverage and largest public case history; publishes the widely cited annual Crypto Crime Report |
| Elliptic | 2013, UK | Financial institutions, regulators, exchanges | Sanctions-evasion tracking and regulatory-compliance tooling for banks entering crypto |
| TRM Labs | 2018, US | Exchanges, DeFi protocols, government agencies | Cross-protocol and DeFi-native tracing, reflecting the growth of laundering activity on automated smart-contract platforms |
The practical difference for a compliance officer choosing between them is less about raw capability — all three cover the major chains and maintain proprietary attribution databases built from years of investigations — and more about which workflow the tool was built around first. A bank standing up its first crypto-custody product cares most about sanctions screening and regulatory reporting workflows. A DeFi protocol cares most about real-time transaction risk scoring at the smart-contract level, often before a transaction even settles. A law enforcement agency cares most about historical case depth and the ability to reconstruct a laundering chain months or years after the fact. Firms have optimized their tooling and sales relationships around these different entry points, even though the underlying graph-analytics techniques converge.
It's worth being explicit about what none of these vendors publicly claims: perfect coverage. All three rely on the same structural limitation described elsewhere in this article — privacy coins largely defeat on-chain attribution regardless of vendor, and cross-chain, cross-protocol tracing is acknowledged industry-wide as harder and slower than single-chain analysis. Vendor selection changes which workflow a compliance team gets, not whether the underlying detection ceiling changes.
The companies building this infrastructure
A handful of specialized firms sell blockchain analytics to governments, exchanges, and banks. None of them are infallible, and all of them work from public blockchain data plus proprietary attribution databases built over years of investigations.
Chainalysis is the largest player, used by hundreds of law enforcement agencies and thousands of financial institutions worldwide, according to the company. Its best-documented public case is the Colonial Pipeline ransomware investigation: Colonial Pipeline paid 75 bitcoin (about $4.4 million at the time) to the DarkSide ransomware group in May 2021. By tracing the public Bitcoin ledger, the FBI identified a wallet holding 63.7 of those bitcoins and obtained the private key, allowing the Department of Justice to seize roughly $2.3 million in June 2021.
Elliptic, a UK-based competitor, focuses heavily on sanctions evasion — tracking whether entities under US Treasury sanctions are using crypto to route around banking restrictions.
TRM Labs specializes in decentralized finance (DeFi) and cross-protocol tracing, since a growing share of laundering activity moves through automated smart-contract platforms rather than centralized exchanges.
The regulatory net tightening around exchanges
What is the FATF Travel Rule?
The Financial Action Task Force's Travel Rule (Recommendation 16) requires crypto exchanges and other virtual asset service providers to collect and transmit sender and recipient identity information — full name, wallet address, and identifying details — whenever a transfer crosses a set threshold, similar to how wire transfers already work in traditional banking. FATF adopted revised guidance in June 2025, and as of that year's global survey, 85 of 117 surveyed jurisdictions (about 73%) had passed Travel Rule legislation, up from 65 the year before, though FATF's own reporting notes enforcement still lags behind the law on the books in many countries.
What does MiCA require of crypto companies in the EU?
The EU's Markets in Crypto-Assets Regulation (MiCA) became fully applicable to crypto-asset service providers on December 30, 2024, after an earlier phase covering stablecoins took effect in June 2024. It requires licensed exchanges operating in the EU to perform identity verification, ongoing transaction monitoring, and suspicious-activity reporting — the same three pillars traditional banks have followed for decades. A transitional grandfathering period for firms already operating legally runs until July 1, 2026, giving existing providers time to obtain formal authorization.
What happened to sanctions on the Tornado Cash mixer?
This is one of the more consequential reversals in crypto regulatory history. In August 2022, OFAC (the US Treasury's Office of Foreign Assets Control) sanctioned Tornado Cash, a mixing service, after North Korea's Lazarus Group allegedly used it to launder stolen funds. Crypto developers and civil liberties groups sued, arguing that open-source, immutable smart-contract code could not legally be "property" subject to sanctions. In November 2024, the Fifth Circuit Court of Appeals agreed, and on March 21, 2025, OFAC formally lifted the sanctions, removing Tornado Cash addresses from the Specially Designated Nationals list. The ruling narrowed what OFAC can sanction going forward and reshaped how compliance teams think about mixer-related risk — the code itself is no longer sanctioned, even though funds that pass through it can still trigger scrutiny depending on their origin.
Regulatory approach compared across three jurisdictions
| Jurisdiction | Core framework | Key requirement | Status as of mid-2026 |
|---|---|---|---|
| United States | Bank Secrecy Act + FinCEN guidance | SARs required above reporting thresholds; exchanges treated as money service businesses | Enforced, patchwork by state |
| European Union | MiCA | Licensed CASPs must run full KYC/AML programs | Fully applicable since December 30, 2024; transition ends July 1, 2026 |
| Global baseline | FATF Recommendation 16 (Travel Rule) | Identity data must travel with transfers above set thresholds | 73% of surveyed jurisdictions have passed legislation; enforcement uneven |
Caption: Regulatory frameworks differ in mechanism but converge on the same three controls banks have used for decades — identity verification, monitoring, and reporting.
Where the system breaks down
Privacy coins remain effectively unreadable
Monero and Zcash use cryptographic techniques (ring signatures and zero-knowledge proofs, respectively) that hide sender, receiver, or amount directly on the public ledger. Standard blockchain forensics tools, which rely on analyzing visible transaction graphs, largely cannot deanonymize Monero transactions using on-chain data alone. This has made privacy coins a recurring target of delisting pressure from exchanges responding to regulatory scrutiny, and some jurisdictions have restricted their trading outright. The unresolved tension is real: the same cryptographic privacy that protects a journalist or a domestic abuse survivor from surveillance also protects a launderer.
Chain-hopping and mixing add cost, not certainty
Criminals increasingly move funds across multiple blockchains (Bitcoin to Ethereum to a lesser-known chain and back) to break the continuity that clustering algorithms depend on. Mixing services pool many users' funds together and redistribute them, deliberately severing the link between deposit and withdrawal. Both techniques raise the cost and time of laundering money rather than eliminating detection risk entirely — Chainalysis and similar firms have developed cross-chain tracing specifically to follow funds through these hops, though it takes longer and succeeds less consistently than single-chain tracing.
False positives are a real compliance cost
Anomaly-detection models trained to catch rare criminal patterns inevitably flag some legitimate activity too — a small business owner making frequent modest transfers, or a trader whose activity happens to resemble a known laundering shape. Exchanges must staff human review teams to sort real threats from statistical noise, and aggressive flagging can freeze legitimate customers' funds for days while a report gets processed. This is the same trade-off every fraud-detection system faces: tighten the threshold and you catch more real crime, but you also lock out more honest users.
What this means for the future of financial surveillance
The trajectory is toward broader, faster, more automated tracing. Firms are building tools to follow assets across 50 or more blockchains simultaneously and to link on-chain wallet clusters to off-chain exchange accounts in near real time, rather than the weeks-to-months timeline that characterized early investigations like Colonial Pipeline. State actors have also entered the picture on the offense: Chainalysis's 2026 report documents a 694% surge in state-level sanctions circumvention, including North Korean hacking groups and a Russian ruble-backed token used to facilitate an estimated $93.3 billion in sanctions evasion. That escalation is likely to accelerate investment in cross-chain and cross-border analytics on the compliance side, not slow it down.
Common misconceptions about crypto AML
"Blockchain is anonymous, so laundering is untraceable." This inverts the actual problem. Blockchains are pseudonymous and permanent, which is nearly the opposite of anonymous — every transaction is recorded forever and publicly visible. The hard part for investigators isn't finding the transaction; it's linking a wallet to a real identity, which is exactly what clustering and off-ramp subpoenas solve.
"AI catches all money laundering automatically." It does not. Machine learning flags patterns for human review; it does not make arrests or freeze funds on its own, and privacy coins sit largely outside its reach. Most successful cases still require weeks or months of combined algorithmic and human investigative work.
"Regulation will just ban crypto to stop laundering." Given a global crypto market measured in the hundreds of billions of dollars in legitimate holdings, and the direction every major regulator has taken (FATF, MiCA, US Treasury) toward licensing and monitoring frameworks rather than prohibition, an outright ban looks far less likely than continued tightening of KYC and reporting requirements.
Frequently asked questions
Q: If blockchain is transparent, why is money laundering still possible? A: Transparency and identity are different things. Every transaction is publicly visible, but the wallets involved are pseudonymous by default. Machine learning's job is closing that gap — linking addresses to real-world entities through behavioral clustering, not reading hidden data.
Q: Does machine learning catch all money laundering on crypto? A: No. Privacy coins like Monero are largely resistant to current on-chain forensics techniques, and mixing or chain-hopping can delay detection substantially. Most flagged cases still require months of combined algorithmic and human investigation before enforcement action follows.
Q: Is crypto AML stronger or weaker than traditional banking AML? A: They have different strengths. Crypto monitoring can run continuously and in near real time across a fully public ledger, but faces genuine identity gaps at self-hosted wallets. Bank AML relies on known customer identities but depends more heavily on slower, manual review. In practice, the two systems increasingly connect at the point where crypto converts back to cash.
Q: What is the FATF Travel Rule and who does it apply to? A: It is a 2019 FATF recommendation, revised in June 2025, requiring virtual asset service providers — exchanges, custodial wallet providers, and similar businesses — to collect and transmit sender and recipient identity information on transfers above a set threshold, mirroring the wire-transfer rule banks already follow.
Q: Did the US actually lift sanctions on Tornado Cash? A: Yes. After a Fifth Circuit appeals court ruled in November 2024 that OFAC exceeded its authority by sanctioning immutable smart-contract code, the Treasury formally removed Tornado Cash from its sanctions list on March 21, 2025.
Q: Can criminals reliably evade AI-based blockchain detection? A: Partially, and at a cost. Mixing services charge fees of roughly 1-3% and add processing time; chain-hopping across multiple blockchains adds complexity and delay. These techniques raise the odds of evading detection but do not eliminate the risk, and forensic firms continue to build cross-chain tracing specifically to counter them.
Q: Why do exchanges sometimes freeze legitimate customer funds? A: Anomaly-detection models are tuned to catch rare criminal patterns, which means some ordinary activity gets flagged as a false positive. Exchanges hold flagged withdrawals for human review before releasing or reporting them, which can delay access to funds for otherwise compliant customers.
Q: How does MiCA change compliance obligations for EU crypto exchanges? A: MiCA, fully applicable since December 30, 2024, requires licensed crypto-asset service providers in the EU to run KYC verification, ongoing transaction monitoring, and suspicious-activity reporting — bringing crypto firms under obligations comparable to those traditional EU banks already follow.
Q: Why are privacy coins facing delisting pressure from exchanges? A: Because standard blockchain forensics tools cannot trace Monero or Zcash transactions on-chain, exchanges operating under KYC/AML regulatory regimes face compliance risk for listing them. Some exchanges and at least one national regulator have restricted or banned trading in privacy coins as a result.
Q: How do graph neural networks improve on older transaction-monitoring methods? A: Traditional models score one transaction at a time using flat tabular data, which discards how funds move through a network of wallets. Graph neural networks instead learn from the structure of the transaction graph itself, letting them recognize multi-hop layering chains and fan-out/fan-in patterns that a row-by-row model would miss entirely.
Q: Can forensic tools reliably trace stolen funds across a cross-chain bridge? A: Only partially, and with more effort than single-chain tracing requires. Investigators must correlate a lock event on the source chain with a mint event on the destination chain using timing and amount data rather than a single shared ledger entry, and techniques built for one bridge protocol often don't transfer cleanly to another. Cross-chain bridges and related protocols have lost more than $2.8 billion cumulatively to exploits since 2021, according to Chainalysis, making this one of the industry's most active detection gaps.
Q: How are Chainalysis, Elliptic, and TRM Labs different from each other? A: All three build on the same core techniques — wallet clustering, risk scoring, and pattern matching — but grew around different entry points. Chainalysis is best known for its law enforcement case history and annual crime reporting; Elliptic emphasizes sanctions-evasion tracking for banks; TRM Labs focuses on DeFi and cross-protocol tracing. None publishes a head-to-head accuracy comparison against the others.
Q: Does using an AI compliance tool guarantee an exchange won't process illicit funds? A: No tool offers that guarantee. Every major forensic vendor works from public blockchain data plus proprietary attribution history, and all face the same structural blind spots — privacy coins, well-executed mixing, and cross-chain hops. AI compliance tools reduce risk and generate leads for human review; they do not eliminate the possibility that illicit funds pass through undetected.
The takeaway
Blockchain forensics does not defeat crypto's pseudonymity by breaking cryptography; it defeats it by treating behavior as evidence, the same way any detective work does. Machine learning clusters addresses into entities, scores transactions for risk, and flags patterns learned from real prior cases, and human investigators finish the job by subpoenaing the exchange where laundered funds eventually meet a bank account. That system catches a meaningful share of crime, but privacy coins, mixing services, and cross-chain hops still create real gaps, and the scale of the problem — $154 billion in illicit crypto flows in 2025 alone — is growing faster than enforcement capacity.
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About the Author
SciHub101 is an independent science and technology publication founded and edited by Buğra Sözeri, a software engineer and app developer based in Türkiye. AI tools assist with drafting and research. Source documentation in the existing archive is uneven, and articles are being reviewed and corrected. The Editorial Team byline identifies editorial responsibility; it does not certify specialist or independent human review. Where we compare products, anything built by our own team is labelled as such — see the editorial guidelines and About page for how corrections and disclosures work.
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