Money laundering methods continue to evolve at pace, forcing financial institutions to rethink how they identify and prevent illicit activity.
According to AiPrise, as criminal networks adopt digital payments, cryptoassets, instant settlement and cross-border platforms, understanding AML typologies has become essential for banks, FinTechs and payment providers operating in 2026.
AML typologies describe the common patterns and behaviours used to launder illicit funds. Rather than focusing on single transactions, typologies help compliance teams recognise broader schemes, such as structuring, mule networks or trade-based laundering, before they escalate into large-scale financial crime. This intelligence-driven approach allows institutions to intervene earlier, strengthen controls and reduce regulatory exposure.
In 2026, AML typologies are no longer static reference documents. They underpin modern transaction monitoring and risk scoring models, enabling institutions to understand how laundering schemes actually function across digital wallets, crypto platforms, embedded finance and international payment rails. Regulators including FATF, FinCEN and EU AML authorities continue to publish typology guidance, reinforcing expectations that firms actively embed this intelligence into their AML frameworks.
One of the most persistent risks remains structuring and smurfing across multiple channels. Criminals now distribute micro-transactions across bank accounts, mobile wallets, prepaid cards and cross-border FinTech rails, often using mule networks or synthetic identities. Detecting this behaviour requires holistic, cross-channel analysis rather than isolated rule-based checks.
Hidden beneficial ownership and shell companies also remain central to layering schemes. Nominee directors, offshore entities and dormant companies with no real economic activity are frequently used to disguise ownership and justify large, irregular payments. Weak KYB and beneficial ownership verification continues to expose institutions to significant risk in this area.
Trade-based money laundering remains one of the hardest typologies to identify, particularly where trade documentation is fragmented. Over- and under-invoicing, phantom shipments and commodity mispricing allow criminals to move value across borders under the appearance of legitimate commerce. Without integrated trade, invoice and pricing data, these schemes can remain hidden for long periods.
Crypto laundering has become increasingly complex as criminals move beyond mainstream blockchains. Mixers, decentralised exchanges, cross-chain bridges and privacy coins are now routinely used to obscure transaction trails. This shift has raised the bar for blockchain analytics and real-time crypto risk monitoring across regulated platforms.
Real estate continues to attract illicit funds due to high asset values and opaque ownership structures. Properties purchased through trusts, LLCs or offshore entities can absorb large sums before being resold or rented, effectively cleaning the proceeds. Similar risks persist within insurance products, where high-value policies, early surrenders and redirected payouts are exploited to legitimise illicit funds.
Cash-intensive businesses remain vulnerable to mingling schemes, while underground banking networks such as hawala allow funds to move across borders outside formal financial systems. Online gambling and gaming platforms are also increasingly abused, with criminals converting illicit funds into apparent winnings through high-volume micro-transactions.
Identity fraud and synthetic identities underpin many of these typologies, enabling criminals to open accounts, move funds quickly and exploit gaps in KYC processes across both traditional finance and crypto platforms.
Understanding these typologies is only the starting point. Banks must actively integrate them into compliance strategies through risk-based monitoring, enhanced due diligence, behavioural analytics and continuous staff training. By embedding typology intelligence into daily operations, institutions can strengthen detection, reduce false positives and maintain regulatory compliance in an increasingly complex financial crime landscape.
Copyright © 2025 RegTech Analyst
Copyright © 2018 RegTech Analyst
