LONDON, 21 October – Global economies are losing an estimated $5.5 trillion USD every year to money laundering, according to new research from Napier AI. The figure represents roughly 5 per cent of global GDP, underscoring the destabilising impact of financial crime on markets worldwide.
The Napier AI / AML Index 2025–2026, developed in partnership with GlobalData and Napier AI’s Data Science team led by Dr Janet Bastiman, evaluates how effectively 40 major markets combat money laundering and terrorist financing, and assesses the growing role of artificial intelligence in strengthening compliance.
The report finds that AI-driven compliance systems could cut regulated firms’ operational costs by as much as $183 billion USD annually, while reducing illicit financial flows could allow global economies to recover more than $3.3 trillion USD each year.
In absolute terms, China, the United States, Germany and India suffer the highest losses. Smaller economies, including the UAE, Romania and South Africa, face some of the most severe impacts relative to the size of their GDP.
The United States sees almost $730 billion USD laundered annually, around 2.5 per cent of its GDP, making it the second most affected nation after China. Brazil faces one of the largest proportional losses, with illicit finance consuming nearly 8 per cent of its GDP. Germany’s annual loss exceeds $209 billion USD, or 4.5 per cent of GDP.
In the United Kingdom, money laundering drains $195 billion USD each year, equivalent to 5.35 per cent of GDP, a deterioration from last year. Rising compliance costs and London’s role as a global financial hub are key contributors. Although the UK has heavily invested in AI, the report concludes that these efficiencies have not yet materialised. Meanwhile, countries such as Canada and Australia have achieved modest improvements thanks to early AI adoption and tightened regulatory frameworks.
Operational pressures are also mounting. Compliance teams worldwide are overwhelmed by large volumes of suspicious activity alerts, the majority of which are false positives. UK institutions typically handle 250–300 alerts per day, compared with around 2,000 in Australia. Nigeria faces 3,000–5,000 alerts daily, while Uganda manages around 600. These volumes closely track GDP losses, indicating how strained systems allow criminal networks to exploit gaps.
Overall, the Index shows a rise in the total value of illicit flows. Several major economies, including the UK, Germany and Brazil, have experienced worsening impacts relative to GDP, highlighting that despite technological advancements, progress remains uneven and financial crime continues to impose a significant burden on both developed and emerging markets.
Greg Watson, CEO of Napier AI, said:
“Our research makes it clear that global money laundering remains a multi-trillion-dollar challenge, but we are beginning to see the first signs of AI making a measurable difference. The problem is that compliance teams are still overwhelmed by the sheer volume of alerts, most of which turn out to be false positives. Smarter, more precise systems are essential to cut through this noise, strengthen detection, and deliver real economic benefits.
For markets such as Brazil and the UK, where the GDP impact is especially severe, the potential efficiency gains from AI are enormous. Last year’s index estimated global losses at $5.2 trillion USD; this year, the figure has risen again, signalling that illicit activity continues to grow. The deterioration in countries like the UK shows that progress is uneven, and reinforces the need for transparent, compliance-first AI.
The rapid introduction of tariffs this year has also contributed to the persistence of money laundering, creating fertile ground for financial crime. As companies and supply chains adjust to shifting trade barriers, new vulnerabilities have appeared. Criminal groups have exploited these disruptions by manipulating payments, falsifying invoices, and routing shipments through third countries to disguise their origin. AI can play a crucial role in navigating these risks from identifying suspicious behaviour to improving alert accuracy, ultimately saving economies hundreds of billions.”
The Napier AI / AML Index further emphasises the transformational potential of AI in financial crime compliance. In surveys conducted for the Index, 73 per cent of industry respondents rated AI as “very useful” for transaction flagging, while 27 per cent identified it as the single most effective tool for detecting suspicious activity within AML processes.
