Follow a single pile of dirty cash through its journey and the whole thing becomes clear. It starts as banknotes from a crime, obviously illicit, impossible to spend at scale without questions. First it’s broken into dozens of small deposits spread across several accounts, each one just under the level that would trigger a report. Then it’s wired through a chain of companies in three countries, converted into crypto and back, and run through a cash-heavy business that mixes it with real takings. Finally it buys a flat, or funds a perfectly legitimate company, and re-emerges as clean investment income nobody would look at twice.
The money that was unmistakably dirty at the start is, by the end, indistinguishable from honest wealth. That transformation, from obviously criminal to apparently clean, is the entire point of money laundering, and it happens in three recognised stages: placement, layering, and integration. This guide walks through all three, then widens out to the schemes criminals actually use, why the problem keeps growing, what it costs the businesses caught in the middle, and where detection programmes tend to fail.
What is money laundering?
Money laundering is the process of disguising the origins of money earned through crime so that it can be used openly without revealing where it came from. The logic is simple. Criminals generate proceeds, from drug trafficking, fraud, corruption, tax evasion, trafficking, organised crime, that are obviously tied to the crime that produced them. Spending that money directly would draw attention and lead investigators straight back to the source. Laundering breaks that link, converting tainted money into funds that appear to come from a legitimate origin.
Two things are worth underlining. First, money laundering is always downstream of another crime; the original offence that generated the money is called the predicate offence, and laundering is what makes the proceeds of that offence usable. Second, it’s a process rather than a single act, which is exactly why it gets described in stages. The money has to get into the system, have its trail obscured, and then come back out looking clean, and each of those steps is a distinct problem with distinct methods.
It’s also in itself a crime almost everywhere and forms the basis of a huge amount of regulation. The FATF (Financial Action Task Force) is the global standard setter for AML rules and that’s the body that determines the obligations that banks, fintechs and an ever increasing number of other businesses have to meet (to know their customers, to watch for suspicious activity and to report it). That’s why it’s not just an interesting thing to understand, for a lot of organisations it is a legal requirement to detect it.
It’s not a minor issue. The UN estimates that between 2% and 5% of global GDP is laundered each year (in the order of $2 trillion) and the vast majority of it goes undetected. Despite all the controls that have been put in place by the financial system, laundering remains statistically a crime that pays.
Why we talk about “stages”
The three-stage model is the standard way the world describes money laundering, and FATF frames the problem this way, which has made it the shared language of regulators, banks, and investigators everywhere.
One caveat in respect of the actual process of laundering though. Real laundering is far messier than the three boxes model. The stages overlap, they repeat and sometimes get omitted entirely. A single scheme could have money layering through a dozen times or could simply skip the placement stage as the money was already in the system. The model is not a set of instructions that criminals will follow in a linear fashion. It’s a lens and its value is in showing you where the money is most vulnerable at each stage. It’s less a sequence of events and more a map of where the money is weakest.
Stage one: placement
Placement is the act of getting illicit money into the financial system in the first place. This is the launderer’s hardest moment, because the cash is still sitting right next to the crime that produced it, and large amounts of physical currency are conspicuous in a way that digital balances are not.
The traditional methods all work by making the dirty money look like normal deposits. Structuring (also known as smurfing) is the process of breaking a large sum into a number of smaller deposits which are each below the reporting threshold, often across a number of accounts and people. You have the classic cash intensive front business (restaurant, car wash, nail salon, launderette etc) where the metaphor is absolutely not an accident, you blend the proceeds of crime with legitimate takings. Casinos, money service businesses and the purchase of monetary instruments (money orders etc) are other avenues.
Placement is both the most dangerous stage for the launderer and the very best opportunity for detection. The money is right at its source, the amounts are often quite obvious and it’s at this point that the frontline controls are in place (KYC at onboarding, transaction monitoring, reporting thresholds which will turn a suspicious cash deposit into a report). If you catch the money at this stage you catch it before the trail becomes complex. That’s also why cash is the launderer’s true enemy. It has to be accounted for, it has to be deposited or spent and each of those actions is an opportunity to be spotted. It’s for this reason that the launderer will devote so much effort to the next stage which is to create as much distance as possible between the money and the moment it first entered.
Stage two: layering
Layering is the active creation of distance between the funds and their source. Placement is about getting the money in, layering is about making it impossible to trace once it’s in. That’s the stage which is built in from the outset to defeat detection.
The techniques are all about complexity for its own sake. The money gets moved via a series of wire transfers, bouncing from account to account and across borders, with a particular fondness for jurisdictions that offer secrecy. It passes through a series of shell and front companies that exist purely on paper. It gets converted into assets and back into cash, into crypto and out again, or it gets ‘washed’ through trade based schemes where the value is hidden in over-invoiced or under-invoiced goods. Each stage in the chain adds another layer of paperwork between the clean balance and the dirty origin. It’s not about any one particularly clever trick; it’s about the sheer amount of distance created, the number of transfers, entities and conversions. Once you’ve got enough of that the trail can be practically impossible to follow back even for an investigator who knows where to begin.
That’s where transaction level monitoring starts to fall apart. By design, no single transaction in a properly constructed layering scheme is going to be anything out of the ordinary. The money has been broken up, routed and obfuscated in such a way that each individual transaction is perfectly legitimate. What actually exposes layering is not usually a single transaction. It’s the structure around it, the nested ownership, the linked counterparties, the companies that share an address or a director, the secrecy jurisdiction entity with no real activity. In other words, you don’t catch layering by looking at the transactions themselves. You catch it by looking at the network that underpins them.
Stage three: integration
Integration is the final stage where the now clean-looking money re-enters the economy as apparently legitimate wealth. The laundering is effectively complete and the point of integration is for the criminal to be able to use the money in the open, without attracting any attention.
By now the money buys things that legitimate money buys. Real estate is the perennial favourite, high value, easy to hold, often bought through corporate structures. So are luxury assets, art, yachts, cars. The money gets invested in or lent to real businesses, paid out as salary or consulting fees from a front company, or justified through fake invoicing and sham loans that give it a paper reason to exist. On the surface, every one of these transactions looks completely normal, because that was the whole objective. A regulator reviewing the paperwork in isolation would find nothing to question, which is precisely the problem.
Which creates a problem for anyone trying to catch it. At the integration stage, the transaction itself tells you almost nothing; a property purchase or a business investment looks the same whether the money behind it is clean or filthy. The only remaining signal is not in the payment but in the person and their connections: who they are, what adverse media attaches to them, whether they or the people around them appear on a sanctions list, what their history and associations actually are. This is the stage where reputational and relational screening catches what transaction monitoring, by this point, simply cannot. The money has been scrubbed clean; the person spending it has not.
Common money laundering schemes (2026)
The three stages describe the shape of the problem. In practice, criminals assemble them out of a recurring toolkit of schemes. The specific techniques evolve, but they all serve the same purpose, and the ones showing up most across cases today include:
- Shell and front companies. Entities with no real business, used to hold money, issue fake invoices, and route funds through layers of ownership that hide who’s really in control.
- Trade-based money laundering. Value is moved by over- or under-invoicing goods, mis-describing shipments, or billing for goods that never move at all, so dirty money crosses borders disguised as legitimate trade.
- Cryptos, mixers and chain hopping. The funds get converted into crypto, go through a mixer or are swapped across chains to break the trail and then are cashed out. It’s fast, it’s borderless and it’s increasingly central to layering.
- Money mules. Actual people (often recruited online with the promise of easy money) will lend their accounts to move the funds, adding a layer of innocent-looking account holders between the launderer and the cash.
- Real estate and luxury assets. High-value purchases, frequently through corporate structures, soak up large sums and turn them into assets that look like ordinary wealth.
- Cash-intensive businesses and casinos, including online gaming, where criminal proceeds are blended with genuine takings or cycled through chips and payouts.
- Professional enablers. Lawyers, accountants, and company-formation agents who, wittingly or not, lend their legitimacy and technical skill to structures built to hide money.
Notice that almost none of these is a single transaction. They’re structures and relationships, which is the recurring theme: the more sophisticated the scheme, the more the risk lives in the network rather than the payment.
The growth of money laundering
Money laundering is not a fixed problem and the trajectory is definitely the wrong one. The very forces that have enabled legitimate finance to become faster and more global (instant cross border payments, digital assets, the sheer volume of transactions moving every second) have also given the launderers greater speed, greater reach and more places to hide.
Several shifts are working in tandem. The digital payment rails mean money can cross borders in seconds, removing the time window in which a control can react. Crypto provides a parallel financial system that is borderless by default and in some cases is deliberately opaque. Synthetic and stolen identities (increasingly generated via AI) make it easier to open accounts and get past the onboarding checks which were designed for a much slower, more human world. Laundering has also become a professionalised industry with a full service offering of enablers and specialists who will launder your money for you on a fee basis, with their own methods and infrastructure.
The uncomfortable conclusion is that criminals innovate faster than the controls we put in place to stop them. Every new rail, every new asset class, every new technology becomes a new channel for laundering before the controls can catch up. That’s why detection can’t be a static checklist. It has to be able to keep up with the actual movement of the money.
The impact of money laundering on business
It’s all too easy to look at money laundering and treat it as a victimless crime, some sort of accounting trick that doesn’t actually harm anyone in particular. It isn’t. That is precisely why it’s so damaging. Every single dollar that is laundered is the proceeds of a real predicate crime (fraud, trafficking, corruption etc). Laundering enables those crimes to pay, and as such it quietly enables the next one.
For the business, the exposure is direct and tangible. A firm that’s got money laundering going on (whether it’s knowingly or not) can be hit with massive fines, be forced to go through a remediation programme, have external monitors put in place to watch it and, in the most extreme cases, have its executives prosecuted. The reputational damage is far more profound and long-lasting. In financial services, the product is trust and a money laundering scandal is a complete and total destruction of that trust with customers, with partners and with the regulators who will then determine how closely you’re going to be watched from that point onwards.
There are also quieter costs. Banks will simply walk away from an entire sector or region if they decide it’s too risky (this is known as de-risking) and in doing so they effectively cut off legitimate businesses as well as the risky ones. And of course laundered money corrupts the markets, enabling the criminals to fund businesses which can then go on to undercut, outbid and outspend the legitimate competitors who are playing by the rules. The damage isn’t just to the criminals. It’s damage to everyone else who shares the financial system with them.
Regulatory fines for money laundering: case studies
The scale of the consequences is easiest to appreciate in the cases that made the news and the recent record is quite sobering.
In 2024 TD Bank pleaded guilty to BSA violations and conspiracy to commit money laundering and agreed to pay over $3bn to US authorities (the largest such penalty ever levied against a US bank). The regulators found that the bank had failed to monitor trillions of dollars in transactions and as a result had allowed criminal organisations to move hundreds of millions of dollars through its accounts. In 2023 Binance agreed to pay approximately $4.3bn to US authorities (one of the largest corporate settlements on record) over AML and sanctions failures with its founder also pleading guilty. In 2022 Danske Bank pleaded guilty in the US and forfeited around $2bn over the scandal at its small Estonian branch through which it is believed an estimated €200bn of suspicious funds had passed.
What’s striking is what they have in common. None of them were about a missing rule. The obligations were there, the frameworks were in place and in each case the signals were absolutely there to be seen. What went wrong was detection. What went wrong was the actual, day to day work of spotting the suspicious activity and doing something about it. That’s the gap that generates the money and the fines. For more on how the criminals actually move the money, Moody’s have a good little primer.
The detection paradox
Line the three stages up and a pattern emerges that should shape how anyone thinks about catching money laundering. As the money moves through the stages, it gets steadily harder to spot in the transaction and steadily easier to spot in the person and the network.
Placement is fundamentally a transaction issue (obvious cash, reporting thresholds, monitoring at the point of entry). Layering is a structural issue (the money appears perfectly legitimate on a transaction by transaction basis and it’s only the web of ownership and counterparties that exposes it). Integration is an identity issue (the transaction is perfect and the only thing you can really pick up is who the people are). The further the money goes down the pipeline the less the transaction itself will reveal and the more it will depend on the people and the relationships that surround it.
That’s why a programme based purely on transaction monitoring is actually the strongest precisely where the launderer is most cautious and the weakest precisely where the funds are already clean. It does a good job at the placement stage and then falls away at every stage after. Modern AML practice overcomes that by combining transaction monitoring with continuous screening of the people and entities involved (their sanctions and PEP status, their adverse media, their litigation) and most importantly their ownership and relationships. The transaction tells you what moved. The network tells you whether it should concern you.
How to detect and prevent money laundering
Against all of that, what actually works? There’s no single control that stops money laundering; effective prevention is a layered programme, built around a risk-based approach, where effort is concentrated on the customers, products, and geographies that carry the most risk rather than spread evenly across everything. The glossary is a good plain-English reference on how the pieces fit together. The core components are well established:
- Know your customer and customer due diligence. Verify who you’re dealing with at onboarding, and understand the nature and purpose of the relationship, so you have a baseline to measure against later.
- Transaction monitoring. Look for patterns that don’t fit (structuring, unusual volumes, movement to high risk jurisdictions etc) and flag the oddities.
- Sanctions, PEP and adverse media screening. Screen the customer and the people behind them against watchlists and adverse media both at onboarding and on an ongoing basis.
- Beneficial ownership and counterparty analysis. Work out who actually owns and controls an entity so that shell companies and hidden connections come to light.
- Ongoing monitoring. Re-screen over time as a customer who was clean at onboarding can change and a one-off check goes stale very quickly.
- Suspicious activity reporting. When something doesn’t add up, file the report, which is both a legal obligation and part of how the wider system catches what any single firm can’t.
The three stage model is a good way of checking your programme for gaps as each stage reveals a different signal. Placement is covered by KYC and transaction monitoring; layering is covered by beneficial ownership and counterparty analysis; integration is covered by adverse media, sanctions and ongoing screening of the people behind the transaction. A gap at any one stage is a stage where clean money will walk through and as the stages pass the buck to each other a gap at one stage becomes someone else’s problem at the next. That’s how money that should have been stopped at placement ends up buying property two years later and no one has ever looked at it twice.
Where detection programmes break down
Most money laundering failures aren’t failures of rules. All of the cases above had rulebooks. They were failures of perception, programmes which looked at the right things on the surface but simply didn’t see the risk. A number of the breakdowns were so recurrent that they were worth naming.
Siloed data is the first. The transaction lives in one system, the customer record in another, the screening result in a third and the link that would reveal the scheme is across all three. A launderer exploiting the gaps between the systems is invisible to any one of them. Alert fatigue is the second: you have screening and monitoring that produces massive volumes of false positives which drown out the real signal and the team end up clearing alerts by reflex, including the one that actually mattered. Static, periodic review is the third: you’ve got a programme that looks at a customer once at onboarding and then again every few years. It’s completely blind in the gaps in between, precisely when the customer’s risk is changing.
Then there are the structural blind spots. A programme built solely on transaction monitoring is weakest in precisely the area where the money appears clean (the detection paradox). Name screening rather than identity resolution leads to a flood of false positives and a complete failure to pick up the actual match which is just a spelling variant away. A programme which is simply run as a compliance checkbox, to tick the box with the examiner rather than to actually do anything to detect money laundering, will pass the audit and will miss the launderers. The common thread is that each of these is a programme which looks but doesn’t see. Closing the gap is about connecting the data, removing the noise, screening continuously and most importantly of all, watching the people and the networks behind the money rather than the money itself.
Catch it where it’s still visible
The uncomfortable truth of the three stages is that the longer money launders, the cleaner it looks, until, at integration, the transaction gives nothing away at all. What remains visible, the whole way through, is the people and the connections behind the money. That’s the signal that doesn’t wash out, no matter how many accounts, borders, or shell companies the money passes through.
That’s the signal that Beady is designed to detect. It looks at the people and entities behind a transaction across sanctions and PEPs, adverse media, legal records and ownership data with every finding traceable back to its source so you can validate it. It doesn’t do transaction monitoring. It covers the network and reputation signals that transaction monitoring simply can’t see. That’s exactly what you need to catch laundering at the layering and integration stages, where the money is already clean and this stage is most often missed by a detection program.
If you’d like to see what Beady surfaces on the people and entities behind your own exposure, book a demo and we’ll walk through it with you.
