Money laundering continues to evolve at pace, with criminal networks adapting quickly to new payment technologies, digital channels and cross-border infrastructures.
As financial crime becomes more complex, AML typologies — the common patterns and behaviours associated with money laundering — remain one of the most important tools available to banks, FinTechs and payment firms, said AiPrise.
Rather than focusing on isolated red flags, typologies help institutions understand how illicit activity unfolds in practice, enabling earlier detection and more effective intervention.
In 2026, AML typologies are especially relevant as transaction volumes increase and financial ecosystems become more interconnected. Criminals now exploit everything from instant payments and embedded finance to crypto assets and online platforms. As a result, compliance teams can no longer rely solely on static, rules-based monitoring. Typologies provide context, allowing firms to recognise behavioural patterns such as unusual transaction velocity, circular fund flows or coordinated activity across multiple accounts and channels.
One of the most persistent typologies remains structuring, often referred to as smurfing. While the core concept is unchanged, the execution has become far more sophisticated. Instead of repeated cash deposits at a single branch, funds are fragmented across mobile wallets, prepaid cards, ATMs and cross-border payment rails. This multi-channel activity is frequently supported by mule networks or synthetic identities, making detection far more challenging without holistic, customer-level analysis.
Shell companies and hidden beneficial ownership structures also continue to present significant risk. Criminals rely on nominee directors, layered offshore entities and dormant companies to disguise control and legitimacy. Transactions may appear routine, yet financial behaviour often bears little resemblance to the stated business activity. Weak KYB processes and incomplete ownership data remain major vulnerabilities for many institutions.
Trade-based money laundering remains one of the most difficult typologies to identify. Through invoice manipulation, phantom shipments and commodity mispricing, criminals can move significant value under the guise of legitimate trade. Fragmented trade documentation and limited data sharing across borders make this risk particularly acute in high-volume trade corridors.
Crypto-related typologies have also expanded. Launderers increasingly combine mixers, decentralised exchanges, cross-chain bridges and privacy coins to obscure transaction trails. This multi-layered approach means that visibility on a single blockchain is no longer sufficient, increasing the importance of real-time analytics and behavioural monitoring.
Beyond digital assets, high-value real estate continues to absorb large volumes of illicit funds, often through opaque ownership structures and rapid resale strategies. Insurance products are also abused, with criminals exploiting premium funding, early surrender and redirected payouts. Cash-intensive businesses, underground banking networks and online gambling platforms further illustrate how illicit funds can be blended, transferred or legitimised outside traditional banking channels.
Identity fraud underpins many of these typologies. Synthetic identities and stolen credentials allow criminals to open accounts, move funds quickly and exploit gaps in onboarding and monitoring controls. Without robust KYC and ongoing behavioural analysis, these risks can scale rapidly.
To remain effective, financial institutions must embed AML typologies directly into their compliance strategies. This includes aligning monitoring rules with known laundering behaviours, applying typology-driven risk scoring, strengthening customer due diligence and continuously updating systems based on regulatory guidance. When combined with advanced analytics and well-trained teams, typologies provide a practical, intelligence-led framework for combating financial crime in 2026 and beyond.
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