How is AI being used in anti-money laundering (AML) customer screening? How is it delivering, and what is standing between adoption and measurable returns?
We asked 400 senior compliance, risk and financial crime leaders across the UK, US, France and Germany to find out.
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| 84% | are concerned about AI-enabled financial crime. | Criminal networks can use a new tool the week it reaches the market. Regulated institutions must test and govern it first. |
| 28% | have AI fully deployed across their screening operations. | Another 44% have it partly deployed. The rest are still piloting or planning. |
| Close to half | of alerts at the average institution are false positives. | This is despite two thirds of institutions using AI to reduce them. |
Financial institutions and banks have spread AI thinly across six screening tasks at once. And 90% use more than one screening vendor, meaning AI is being given a fragmented view of risk that leads to more false positives.
Research by Vitreous World for Ripjar: 400 C-suite and Director-level decision-makers, 100 in each market, 2026.
AI in anti-money laundering (AML) screening is the use of AI models to check customers against sanctions lists, politically exposed persons (PEPs), watchlists and adverse media, meaning news reports that link a person or company to financial crime.
Entity resolution matches every record an institution holds on a customer, across all its systems, to a single profile. Without it, one customer can generate several alerts that analysts must reconcile by hand.
15 October 2026. Sign up on this page and we’ll send it to your inbox.
