Money Laundering Schemes: Most Common Ones in 2026 & How They Work?

‍Key Takeaways (TL;DR)

• Scale is staggering: Global money laundering accounts for an estimated $800 billion to $2 trillion a year, roughly 2-5% of global GDP. Crypto-linked illicit flows alone hit a record $158 billion in 2025, more than tripling 2024 totals.
• Three stages, many methods: Every money laundering scheme follows the same structure: placement (getting dirty money into the financial system), layering (hiding where it came from), and integration (withdrawing it as clean funds). The methods criminals use across these stages are diverse and keep changing.
• AI is changing both sides: Criminals now use AI and automation to move funds faster and stay under the radar. Financial institutions that rely on static rule-based systems are falling behind.
• Fines are accelerating: Regulatory enforcement hit 417% in H1 2025 compared to H1 2024. Weak transaction monitoring is consistently the primary driver of penalties.
• Detection requires behavioral context: Catching modern money laundering schemes requires AI that tracks entities over time, not just flags individual transactions in isolation.
• Cross-institutional data is the edge: No single institution can see the full picture on its own. Fraudio's patented Network Effect AI centralizes transaction data across issuers, acquirers, APMs, and transfers, detecting coordinated laundering campaigns that siloed tools structurally cannot.

Table of Contents

1. Money Laundering Schemes: at a Glance
2. What Is Money Laundering? The Three Stages
3. Most Common Money Laundering Schemes in 2026
4. Why These Schemes Are Increasingly Hard to Detect
5. Red Flags: What Financial Institutions Must Monitor
6. How AI-Driven AML Detection Stops These Schemes
7. Everything You Need to Know About Money Laundering Schemes
8. How Fraudio Helps Payment Companies Fight Money Laundering
9. FAQs About Money Laundering Schemes

Money Laundering Schemes: at a Glance

What Is Money Laundering? The Three Stages

Money laundering is how criminals disguise the origins of illegal funds so they can spend or invest the money without raising red flags with banks, regulators, or law enforcement.

Almost every money laundering scheme, regardless of method, follows three stages:

1. Placement: Dirty money enters the financial system for the first time. This is the riskiest stage for criminals because it requires direct contact with financial institutions. Common methods include cash deposits, using money service businesses, buying prepaid cards, or converting funds into digital assets.

2. Layering: The funds are separated from their criminal source through multiple transactions, entities, accounts, and jurisdictions. The goal is to create a trail so complex that investigators can't trace the money back to its origin. Shell companies, wire transfers, cryptocurrency mixers, and trade invoices are all common layering tools.

3. Integration: The money re-enters the economy as apparently clean funds. This can happen through real estate purchases, business investments, luxury goods, or withdrawing money from a corporate account that's been built up through layered transactions.

Knowing this structure is essential, but it's only the starting point. Each stage has multiple scheme variations, and modern criminals move across them faster than ever.

Most Common Money Laundering Schemes in 2026

1. Structuring (Smurfing)

Structuring, commonly called smurfing, is one of the most widely used placement techniques. It involves deliberately breaking large sums of illicit cash into smaller deposits, each designed to stay below regulatory reporting thresholds. In the United States, financial institutions must file Currency Transaction Reports (CTRs) for transactions exceeding $10,000. Structuring is specifically designed to get around that requirement.

How it works: 

A criminal recruits multiple associates, sometimes across multiple countries, to deposit smaller amounts into separate accounts over time. The funds are then consolidated and wired to a central account. Money mules are frequently used to make the deposits on the criminal's behalf.

What makes it hard to detect: 

Any individual transaction looks unremarkable. Detection requires monitoring how frequently deposits occur, how many accounts are involved, and how the amounts add up across time, not just reviewing each deposit on its own.

Red flags:

• Multiple deposits at or just below reporting thresholds in quick succession
• Deposits made at different branches of the same institution on the same day
• Account activity that doesn't match the customer's stated business profile

2. Shell Companies and Layering Networks

Shell companies have no real operations, no staff, and no genuine business activity. They exist to hold accounts, receive funds, and pass them along, giving a legal appearance of legitimacy that makes tracing dirty money extremely difficult.

How it works: 

A criminal sets up a network of shell companies, often registered across multiple jurisdictions with weak beneficial ownership requirements. Money flows between these entities as fictitious payments for goods or services that don't exist. The true owner's identity is buried behind layers of corporate structures, nominee directors, and offshore accounts.

What makes it hard to detect: 

Beneficial ownership is deliberately hidden. Investigators looking into one company may have no visibility into the wider network. Many shell companies are registered in jurisdictions that actively limit disclosure, making it hard to trace who's actually in control.

Red flags:

• Companies with no staff, physical address, or verifiable business activity
• Payments between related entities with no clear commercial purpose
• Frequent changes to company ownership or directorship
• Transactions routed through jurisdictions with low regulatory oversight

3. Trade-Based Money Laundering (TBML)

Trade-based money laundering exploits the sheer scale and complexity of global trade to hide illicit fund movements. With millions of cross-border shipments processed daily across multiple jurisdictions, TBML is one of the hardest laundering methods to detect.

How it works: 

Criminals manipulate trade documentation, primarily invoices, to make moving money across borders look like legitimate commerce. The main techniques are:

• Over-invoicing: Declaring goods at a higher value than their actual price, so excess funds can be transferred to the seller's country as payment
• Under-invoicing: Declaring goods below their actual value, quietly shifting purchasing power to the buyer's country
• False invoicing: Creating paperwork for goods that were never actually shipped
• Multiple invoicing: Billing the same shipment several times to justify repeated fund transfers

What makes it hard to detect: 

Unlike cash-based schemes, TBML is built on legitimate trade infrastructure. The documentation exists. The goods may even exist. The manipulation is in the valuation or volume, and catching it requires comparing invoiced values against market benchmarks across international trade flows.

Red flags:

• Invoice values that diverge from market pricing for the same goods
• Trade relationships between parties in jurisdictions with no clear commercial rationale
• Profit margins that don't match the stated business type
• Repeated transactions between the same counterparties with no obvious commercial reason

4. Real Estate Laundering

Real estate has long been one of the most favored integration methods. Property transactions are high-value, cross-border-capable, and historically less transparent than financial transactions. That combination makes them attractive for moving illicit funds at scale.

How it works: 

Criminals buy real estate using illicit funds, then sell or refinance the property to generate apparently legitimate proceeds. Offshore ownership structures, using shell companies, nominees, and trusts, hide who's actually funding the purchase. Valuation manipulation is also common, with properties deliberately overpriced or underpriced to move value between parties.

Specific tactics:

• Buying property with illicit cash structured to look like a legitimate investment
• Rolling the same asset through multiple transactions so that illegal proceeds appear as resale profits
• Using offshore shell structures to hide beneficial ownership entirely

What makes it hard to detect: 

Real estate transactions are complex, often involving multiple intermediaries (lawyers, agents, notaries), and in many jurisdictions, they haven't historically been subject to strict AML reporting requirements, though that's changing. Mexico, for example, expanded AML obligations for real estate professionals in its 2025 AML reform.

5. Cash-Intensive Business Schemes

Front businesses that naturally take in a lot of cash, like restaurants, car washes, laundromats, parking facilities, and casinos, are classic vehicles for placement and integration. The model is direct: inflate reported revenues to absorb dirty cash into apparent business income.

How it works: 

A criminal runs a legitimate cash-heavy business and reports income far above what the business actually earns. Dirty cash gets mixed with genuine business revenue in the accounts. The combined amount is deposited as legitimate earnings, making the illicit funds look like they came from normal operations.

What makes it hard to detect: 

Service businesses with little to no physical inventory can legitimately claim wide variation in revenues, making it hard to say what's normal. Detection requires comparing revenue against staffing levels, footfall data, cost structures, and industry benchmarks.

Red flags:

• Revenue well above industry averages for comparable businesses
• Cash deposits that don't match the business's operating hours, size, or location
• Incomplete, inconsistent, or frequently amended financial records
• Sudden increases in reported income with no corresponding growth in operations

6. Cryptocurrency and Mixer-Based Laundering

Cryptocurrency has become a major and fast-growing channel for money laundering. Crypto-linked illicit flows reached an estimated $158 billion in 2025, more than tripling 2024 totals. The appeal for criminals is speed, pseudonymity, and the ability to move value across borders without going through traditional financial institutions.

How it works: 

Criminals convert illicit funds into cryptocurrency, then use tools designed to obscure the transaction trail:

• Mixers / Tumblers: Services that pool cryptocurrency from multiple users and redistribute different coins, breaking the traceable link between sender and recipient. One known mixer was taken down after laundering over $3 billion.
• Chain-hopping: Converting funds across multiple cryptocurrencies and blockchains to obstruct tracing
• DeFi protocols: Using decentralized finance platforms to swap assets across chains without centralized intermediaries
• Peer-to-peer exchanges: Trading directly between individuals in jurisdictions with minimal oversight

What makes it hard to detect: 

Pseudonymous addresses, cross-chain movements, and mixer obfuscation make tracing extremely difficult for traditional AML tools. Advanced blockchain analytics, combined with behavioral profiling of on-ramp and off-ramp transaction patterns, is increasingly effective at surfacing suspicious flows.

Regulatory enforcement here is accelerating sharply. Global AML fines in H1 2025 increased 417% compared to H1 2024. OKX paid $504 million in penalties. KuCoin agreed to nearly $300 million in fines. These cases consistently trace back to one root failure: inadequate transaction monitoring.

7. Transaction Laundering Through Merchant Networks

Transaction laundering, sometimes called undisclosed aggregation or factoring, is a form of merchant fraud that sits at the intersection of acquiring risk and AML. A merchant registered in a low-risk category processes payments on behalf of entirely different, often illegal, operations through their account.

How it works: 

A merchant sets up a payment processing account claiming to sell low-risk goods, such as books, digital downloads, or general merchandise. The account then gets used to process payments for undisclosed businesses, which may include illegal gambling operations, adult content platforms, or drug-related services. The acquirer becomes an unknowing channel for laundering criminal proceeds through what looks like a legitimate merchant relationship.

Why it matters for payment companies: 

Card networks fine acquirers for facilitating illegal transaction processing, regardless of whether the acquirer knew about the arrangement. The liability falls on the acquiring institution.

Red flags:

• Transaction patterns that don't match the merchant's stated product or service category
• Unusually high refund rates, dispute rates, or chargeback ratios for the stated business type
• Settlement flows that don't align with the merchant's reported revenue or operating model
• Sudden changes in transaction volume with no obvious commercial explanation

8. Money Mule Networks

Money mule networks are the logistical backbone that many money laundering schemes rely on for placement and layering. Recruited or coerced individuals, known as mules, let their accounts be used to receive, hold, and pass on illicit funds, putting more distance between the criminal and the money.

How it works: 

Criminal networks recruit mules through job scams, social engineering, or coercion. Each mule receives funds, often from fraud victims in authorized push payment (APP) schemes, and immediately sends them to another account, often across jurisdictions. Individual transactions can look entirely normal. The criminal pattern only becomes visible when you look at the behavior of accounts as a network.

Why it matters for payment companies: 

As APP fraud volumes rise, payment companies face direct liability for reimbursing victims. Catching mule accounts before they disperse funds is the only way to limit losses and support recovery.

Red flags:

• Accounts receiving funds from multiple unrelated sources in rapid succession
• Near-zero balance maintained as funds arrive and immediately move out
• Inflow-to-outflow ratios that don't match the account's stated purpose
• Coordinated behavior across a cluster of accounts, with multiple accounts showing the same pattern at the same time

Why These Schemes Are Increasingly Hard to Detect

Several structural factors are making modern money laundering schemes harder to catch using traditional controls:

• Speed of digital payments: Real-time payment systems give mule networks seconds to disperse funds before a freeze can be applied. Batch-based or end-of-day reporting is too slow to intervene.
• AI-assisted criminal operations: Criminals are actively using AI to generate convincing documentation for TBML schemes, scale smurfing operations, and map out the thresholds in transaction monitoring rules, then stay deliberately below them.
• Regulatory arbitrage: Criminal networks route funds through jurisdictions with weaker oversight, exploiting gaps between national AML frameworks. The FATF expanded its list of monitored jurisdictions from 13 high-risk countries in February 2025 to 23 by 2026, but gaps remain.
• Fragmented data: If your institution can only see one side of a transaction, either issuing or acquiring, you will miss the cross-institutional patterns that reveal coordinated laundering campaigns. This is a structural constraint, not just a tooling problem. Regulations in most markets prohibit institutions from connecting data across their issuing and acquiring operations, so each side is working with an incomplete picture by design.

That legal boundary is precisely where the biggest detection gap lies for payment companies, and it is the gap that centralized AI, trained across both sides of the flow simultaneously, is built to close. 

If you want to see where that gap is costing you before committing to anything, a Proof of Results test lets you run Fraudio's detection against your historical data in parallel with your current setup, with zero integration work and no commitment required.

Red Flags: What Financial Institutions Must Monitor

A good anti-money laundering software needs to be configured to surface behavioral patterns, not just flag individual transactions. The most consistent red flags across all common money laundering schemes include:

Account behavior:

• Sudden activity changes that don't match the account's established history
• Inflows from multiple unrelated sources with rapid outbound transfers
• Transactions to or from high-risk jurisdictions without a clear business rationale
• Repeated transactions structured to stay below reporting thresholds

Merchant behavior:

• Processing volumes that don't match the stated business type or size
• Refund, dispute, or chargeback rates outside peer-group norms
• Settlement requests that deviate from historical averages without a clear cause
• New merchant accounts showing immediate high-volume activity

Entity-level signals:

• Counterparty diversity is expanding rapidly without a commercial explanation
• IP address or device signals that don't match the stated account location
• Links between accounts, merchants, or entities sharing identifiers (same IP, same device, same beneficiary)
• Exposure to PEP, sanctions, or adverse media lists

How AI-Driven AML Detection Stops These Schemes

Rules-based transaction monitoring catches what rules were written to catch, and nothing more. Rules are reactive; they get written after a scheme is identified. Criminals adapt faster than rule sets can be updated.

Effective fraud detection for money laundering requires two things working together:

1. Entity-level behavioral profiling and link analysis

AI models build continuous profiles of each account, merchant, or entity, tracking how behavior changes over time. Deviations from an entity's own baseline, and from how similar peers behave, surface suspicious patterns that no single transaction would reveal.

Link analysis maps the relationships between accounts by counterparty, IP address, device, transaction timing, and settlement flows. That makes coordinated criminal networks visible as structures rather than as isolated suspicious transactions.

2. Centralized cross-institutional data

A siloed AI model trained only on your own transactions cannot see coordinated patterns that span multiple institutions. Individual institutions are legally prohibited from connecting data across their issuing and acquiring operations, so the cross-institutional view is not something any organization can build internally.

A centralized dataset that pulls together transaction data across issuers, acquirers, processors, and fintech platforms gives the AI the context needed to surface laundering campaigns that no single institution could detect alone. Models trained on centralized data also protect customers from the first transaction processed, without the months-long ramp-up that siloed models require.

Everything You Need to Know About Money Laundering Schemes

How Fraudio Helps Payment Companies Fight Money Laundering

Most AML tools were built for a single institution looking at its own data and that architecture has a hard limit. It cannot see the coordinated behavior that spans multiple payment companies, jurisdictions, and transaction types.

Fraudio's patented Network Effect AI breaks that constraint. By centralizing transaction data from issuers, acquirers, APMs, transfers, and remittances into a single dataset, Fraudio's models learn from billions of cross-institutional transactions in real time - making mule networks and transaction laundering campaigns visible at the network level, not just within a single institution.

Two of the schemes covered in this article illustrate why that matters:

• Transaction laundering is one of the highest-liability exposures acquirers face - card networks hold them responsible for illegal processing regardless of whether they knew it was happening. Fraudio's merchant monitoring detects anomalies in processing patterns, peer-group deviations, and mismatches between stated business type and actual flows, typically weeks before chargebacks arrive. Viva Wallet deployed this capability and caught fraudulent merchants three weeks earlier than their previous solution, achieving 8x ROI and a 600% increase in fraud team efficiency.
• Money mule networks require that same cross-institutional view. Fraudio's P2P product continuously profiles accounts across time and flags coordinated mule activity in real time, enabling institutions to freeze accounts before funds are fully dispersed.

Fraudio's AML solution is built specifically for payment companies under regulatory pressure from central banks, card schemes, PSD2, and GDPR. It goes live in weeks, includes full case management with SAR-format downloads and a complete audit trail, and runs on pay-per-use pricing with no setup fees or hidden charges.

FAQs About Money Laundering Schemes

What are the most common money laundering schemes?

The most common money laundering schemes include structuring (smurfing), shell company networks, trade-based money laundering (TBML), real estate laundering, cash-intensive business schemes, cryptocurrency mixing, transaction laundering through merchant networks, and money mule networks. Each targets a different stage of the laundering process, whether placement, layering, or integration. Globally, money laundering accounts for an estimated $800 billion to $2 trillion annually, and crypto-linked illicit flows alone hit $158 billion in 2025.

How does structuring work as a money laundering scheme?

Structuring, also called smurfing, works by deliberately breaking large sums of illicit cash into smaller deposits designed to stay below regulatory reporting thresholds, such as the $10,000 CTR threshold in the United States. Criminals use associates or money mules to deposit these smaller amounts into multiple accounts across different branches or institutions. The funds are then consolidated and transferred. Catching it requires analyzing deposit frequency and patterns across accounts over time, as any individual transaction looks unremarkable in isolation.

What is trade-based money laundering?

Trade-based money laundering (TBML) exploits international trade transactions to move illicit funds across borders, making them look like legitimate commercial payments. Criminals manipulate invoices through over-invoicing, under-invoicing, false invoicing, or multiple invoicing to justify the transfer of value between counterparties. TBML is one of the most complex laundering methods because the documentation can be entirely genuine; only the valuation or volume has been manipulated. The Financial Action Task Force (FATF) identifies TBML as a primary vehicle for large-scale money laundering globally.

How do shell companies facilitate money laundering?

Shell companies facilitate money laundering by creating a legal entity that can hold accounts and transfer funds without any real business operations, staff, or assets. Criminals route illicit funds through networks of shell companies across multiple jurisdictions as payments for fictitious goods or services, building up layers of apparent legitimacy. The true owner's identity is hidden behind nominee directors and offshore registrations in jurisdictions with limited disclosure requirements. Each transaction in the chain looks like a normal business payment; the criminal structure only becomes visible through network-level analysis of all the entities involved.

What are red flags for money laundering in payment transactions?

Red flags include accounts receiving funds from multiple unrelated sources with immediate outbound transfers, inflow-to-outflow ratios that don't match the account's purpose, and transactions structured to stay just below reporting thresholds. On the merchant side, processing volumes inconsistent with the stated business type, sudden behavioral changes without commercial explanation, and links between accounts sharing the same IP address, device, or beneficiary are consistent indicators. For merchant portfolios specifically, unusually high dispute or chargeback rates relative to peer-group benchmarks are an early signal of transaction laundering.

How is cryptocurrency used to launder money?

Cryptocurrency is used to launder money through mixing services that pool and redistribute funds to break the transaction trail, chain-hopping across multiple blockchains to obstruct tracing, peer-to-peer exchanges with limited oversight, and DeFi protocols that allow cross-chain asset swaps without centralized intermediaries. Crypto-linked illicit flows hit a record $158 billion in 2025, more than tripling 2024 totals. Global AML fines increased 417% in H1 2025 compared to H1 2024, with inadequate transaction monitoring consistently cited as the primary compliance failure.

What is the difference between money laundering and fraud?

Money laundering and fraud are distinct crimes, though they frequently occur together. Fraud involves obtaining money or assets through deception, for example, through APP fraud, payment card fraud, or invoice fraud. Money laundering is the process of disguising where those proceeds came from. In practice, fraud generates dirty money, and laundering cleans it. Financial institutions need to monitor for both separately, since the detection signals differ at each stage.

Can AI fully replace rule-based AML monitoring?

AI cannot fully replace rules in AML monitoring; the two serve different functions and work best together. Rules provide explicit, auditable controls that regulators can review and that compliance teams can update instantly in response to known patterns. AI adds behavioral context, anomaly detection, and link analysis that rules cannot deliver, particularly for detecting unknown or evolving schemes. In Fraudio's architecture, rules trigger first on known patterns, and AI analyzes the remaining transaction universe for emerging threats that no rule has yet been written to catch.

What happens to financial institutions that fail AML requirements?

Financial institutions that fail AML requirements face regulatory fines, license revocation, reputational damage, and in serious cases, criminal prosecution of senior executives. Total AML-related penalties reached approximately $1.23 billion in H1 2025 alone, a 417% increase over H1 2024. Ineffective transaction monitoring has been identified as the most consistent driver of the largest enforcement actions, including OKX's $504 million penalty and Deutsche Bank's $186 million fine. Beyond financial penalties, AML failures destroy the trust of banking partners, card schemes, and regulators whose ongoing approval is essential for operating a payment business.