One of the crucial skills to be effective in money laundering prevention, detection, and reporting is constant vigilance for the new types (typologies) of money laundering and how those tactics evolve. 

In this article, we're looking at some of the classic and evolving methods and the technology that can block them. 

Smurfing (or structuring) large sums into small ones before paying into an account (placement)

What is it: Breaking down a huge sum of money into smaller (and less suspicious) amounts. 

Example: Fans of the smash TV series, The Sopranos, may remember how the mafia wife Carmela Soprano opened a series of brokerage accounts with different banks, depositing $9,990 each. This is because cash sums of less than $10,000 generally fly under the radar - they do not need to be reported to the IRS. As you can imagine, smurfing millions of dollars requires a lot of players, each transporting small amounts, known as money mules.

Money mules push these fragmented funds into the financial system through various channels, including cash, bank transactions, money services, pre-paid cards, and cryptocurrency. 

Placement: The process of loading illicit money into the financial system is known as “placement", and it is normally the first stage of money laundering. The whole process is often called placement, layering, and integration. Criminals place the funds, “layer” the funds by moving them around to make them harder to trace, before finally “integrating” by making them look legitimate (like company revenue). 

How to block it: Prevent smurfing with risk-based checks and AI-enhanced KYC 

The tension for compliance teams is identifying as much as possible about a user before they even begin submitting their documents or data. 

• With pre-KYC account verification, firms can silently check for other bank accounts in this customer's name, for example, with the same SSN. In addition to verifying if the customer is real, they can glean further information about the potential customer from their other accounts, for example, if the customer is in good financial standing. 
• Checking other user accounts: If the potential customer has suspicious activity in their other bank accounts, this may be worth flagging for further investigation. This is a vital way for firms to avoid using their accounts as channels for smurfing. 
• Risk-based onboarding checks: Firms should always know their customers’ true identity, source of income, and transactional habits. However, to prove these points, the onboarding system should constantly scan the IP addresses, location, mouse movements, users’ proximity to their mobile phone, and more. Bad actors who “smurf” accounts are more likely to demonstrate expert behavior, like knowing just where to click during a signup process. 

Integration: Concocting a complicated web of transactions

What is it: The movement of funds through a complex matrix of transactions, designed to be confusing to follow. 

Example: Money mules need multiple accounts, many created with false or stolen identities, creating yet more crime. A December 2024 case from Interpol, for example, uncovered 82,112 accounts used by 3,500 suspects, giving an average of 42 accounts each. 

Thousands of these “pass-through” or "funnel” accounts are opened to muddy the waters, with various amounts of criminal money flowing through. 

How to block it: Implement intelligence-sharing technology

One of the most effective ways to break the international flow of money laundering is to share intelligence with other organizations, both in-person and through technology. 

Together, firms can form a protective bubble, and warn each other of incoming threats or suspicious behavior. This is an area where machine learning truly shines. Compliance technology runs round-the-clock, detecting patterns and flagging potential criminal activity. 

Re-consolidating the large sums with shell companies 

What is it: Traditionally, the separate illicit funds would be transferred to an opaque bank account or shell company (or shelf company), based in a tax haven such as the British Virgin Isles or Seychelles. From here, the money trail usually cannot be followed any further. 

Example: You can have shell companies owned by some people, within shell companies owned by others, within shell companies owned by someone different. This further blocks scrutiny. The explosive 2016 Panama Papers, for example, revealed how Russian President Vladimir Putin used his network of Oligarchs to conceal as much as $2 billion. 

From here, the shell company can be used in various ways to send the funds back to the beneficial owner, directly or indirectly. Some use trade-based money laundering tactics, like issuing fake invoices. While others may buy huge luxury properties or super yachts, which can be rented out or chartered (by the shell company) for significant fees. 

How to block it: Implement robust KYB technology 

We recommend implementing a tiered KYB model so that the most suspicious companies undergo a much more extensive check. 

Shell company detection and look-through is a critical tool in any KYB flow. These checks often involve identifying ultimate beneficial owners (UBOs) through complex corporate hierarchies. We recommend that organizations find streamlined ways to collect this data in real-time so that decisions can be made quickly. If risk is identified, further investigation may be warranted, but if not, institutions can streamline their processes and not penalize good customers with simpler structures.

Manipulating invoices with trade-based money laundering 

What is it: An evolving tactic is to avoid infiltrating the financial system altogether using businesses, known as trade-based money laundering (TBML). Europol found that 86% of the EU's most threatening criminal networks use legal business structures to move illegal funds. 

Example: While some use shell companies, 63% take over existing businesses (sometimes by force), or set up their own. The business owners are not always willing participants. In 2014, the Sinaloa Drug Cartel forced a small business in the fashion district of Los Angeles to launder $140,000 of ransom money to them after they kidnapped and tortured a family member.

TBML takes many different forms, making it difficult to track. But it usually centers around invoicing. A company could, for example, over-invoice exports or under-invoice imports, using trade documents as a smokescreen. Between 6% and 9% of all US export and import invoices are estimated to be altered. Criminals also manipulate the pricing, or charge for a service which is hard to quantify, like consultancy work. 

How to block it: Combining KYC, KYB, and Transaction Monitoring

For financial firms thinking about their business clients, the best way forward is to analyze income and expense flows with a critical eye. AI-enhanced KYB and transaction monitoring platforms, like Sardine’s, continuously analyze business transactions, directors, persons of interest, locations, and behaviors. If any suspicious activity is detected, compliance officers are quickly alerted. When a company changes its baseline behavior, something fishy is likely to be happening and warrants further investigation.

Converting currencies by moving goods

What is it: Criminal gangs operating abroad need to convert the dollars into local currency. 

Example: The drug lords in TV series like Ozark or Breaking Bad are based in Mexico.  Unlike the movies, however, today's criminals do not generally carry suitcases of cash over the border to their bosses’ houses. Increasingly, they are converting dollars by moving goods. 

In 2024, for example, the Sinaloa Cartel used dollars from selling fentanyl to buy bulk cell phones, which they sold across stores in Mexico. This newer style of money laundering means that no cash crosses the border, it's an expanding part of the Black Market Peso Exchange. 

How to block it: Implement merchant risk software 

In the case of the cell phone store above, merchant risk software should be able to alert firms about suspicious transactional flows. Sardine, for example, has 4,800 money laundering-based features to identify red flags. 

We also recommend focusing on spending card data to monitor how the merchant is distributing the funds and whether that matches the initial onboarding information. 

When combined with a real-time assessment of a merchant, this can flag any sudden changes in what a merchant is selling vs what they claim to sell or are incorporated to do. Generative AI can help flag discrepancies between tax filings, incorporation documents, websites, invoices, and payment data.

Crypto is the irrevocable payment for money launderers

What is it: Today, cryptoassets are a money laundering tool because transfers cannot be revoked. Many traditional laundering techniques of smurfing and creating confusing trails have evolved into the world of digital assets. 

Example: Criminals take advantage of every stage of the system, from hacking other crypto users to storing assets digitally or cashing them in for fiat currency. In 2020, two North Korean hackers stole $250 million worth of cryptocurrency, which was laundered through Chinese over-the-counter traders. 

Criminals can rapidly move illicit funds from one location to another without crossing a border. Moreover, they can hide the proceeds across a vast web of crypto assets, like NFTs or currencies. This can obscure transaction trails even more. While international police are cracking down on illegal sites like Garantex, with the arrest of one bad actor in March 2025, the problem persists. 

How to stop it: Implement on-chain and off-chain wallet screening and transaction monitoring

We recommend organizations monitor both on-chain and off-chain data sources to provide a complete risk picture. For example, crypto wallet screening, combined with KYC, transaction monitoring, device, behavior, and other risk signals, provides a much higher resolution picture of what’s happening. Criminals often exploit the gaps between traditional finance and the crypto industry. 

Closing these gaps requires pulling together multiple data sources into a single real-time dashboard for investigations and case management. Sardine's AML and Transaction Monitoring allows firms to build up to 500 of their own rules, to truly personalize the risk management to the unique compliance needs of the service. 

Stay one step ahead 

In this article, we've listed six of the classic and evolving money laundering techniques. But criminals continue to reinvest in new tools all the time. Protection against money laundering requires ever more training, upskilling, and investigations. Although it may feel like the work is never-ending, the good news is that our efforts will - and do - pay off. 

Cutting off the financial incentive means criminals have less illicit funding to reinvest in new money laundering techniques. This can make the job easier over time. It also means they have less money to continue committing crimes. Effective AML technology helps to stem the flow of fentanyl sales, terrorist financing, human trafficking, kidnappings, extortions, and more. 

Every blocked transaction, Suspicious Activity Report, and piece of intelligence shared is a win. 

We can combine our capabilities to provide interconnected, world-leading compliance that never stops learning. 

By joining our data consortium, Sonar, you can benefit from the shared intelligence and expertise of a wide range of members, including Visa, Square, Airbase, Blockchain.com, Novo and Straddle.

Frequently Asked Questions (FAQ)

How often should we update our AML monitoring models and scenarios? At a minimum, firms should review and update models quarterly, or whenever new risk indicators, regulatory guidance, or known typologies emerge. Continuous learning and periodic tuning keep your detection capabilities aligned with evolving threats.

Are advanced analytics and machine learning essential for AML compliance? Machine learning can spot nuanced patterns that rule-based systems might miss, making it a valuable addition to a robust AML toolkit. Investing in robust AML technology is an economy that pays for itself, helping to avoid regulatory fines stretching into billions and curbing criminal behavior.

How can we reduce false positives without missing genuine suspicious activity? Adopting a risk-based approach, refining alert thresholds, and leveraging machine learning models help lower false positives. Enhancing KYC data quality also ensures the system has accurate baselines for normal activity, improving alert precision.

Can we rely solely on automated systems to detect AML risks? Automated systems are vital but most effective when complemented by skilled human analysts. Expert judgment, contextual understanding, and investigative experience remain critical for interpreting complex patterns and making informed compliance decisions.

What role do regulatory guidelines and industry consortia play in improving AML efforts? Compliance frameworks and guidelines from FATF, FinCEN, and the EU Commission guide best practices. Industry groups, information-sharing alliances, and regulatory sandboxes provide platforms to exchange knowledge, test solutions, and adapt to new AML typologies collectively.