Napier AI has launched Insights AI, a new capability within its transaction monitoring product that is designed to give anti-money laundering analysts clearer explanations of customer behaviour during investigations.
The new feature follows testing in the Financial Conduct Authority’s Supercharged Sandbox and is aimed at reducing the manual analysis and context switching that can slow financial crime investigations. According to Napier AI, Insights AI surfaces behavioural patterns and highlights activity contributing to each task directly inside transaction monitoring workflows, helping analysts move beyond the initial alert and assess possible emerging risks more quickly.
The launch builds on work the company presented under “Project Theseus” at the FCA Supercharged Sandbox Showcase. Napier AI said the project involved two tests — pattern mining and fluid dynamics — using frequency-based AI algorithms on large-scale synthetic financial datasets to identify money-laundering typologies more effectively than traditional rules-based systems while using significantly less compute power. Those models now form part of the company’s Continuum platform, where they are tuned by its data science team and underpin the newly announced Insights AI capability.
Dr Janet Bastiman, Chief Data Scientist at Napier AI, said: “Participating in the FCA Supercharged Sandbox allowed us to design and run new approaches to testing AI models for anti money-laundering. One of the biggest historical challenges in tackling complex money laundering typologies is the disconnected nature of the data required for pattern analysis along the complete lifecycle of a customer behaviour or transaction flow.”
She added: “We developed a methodology to detect the ripples in the transaction flow where money had been injected, even if we’re sampling downstream of that. And by applying the concept of the frequency and amplitude of waves to these ‘ripples’ we were able to identify financial crime even when we were sampling further ‘downstream’ from the placement of illicit funds.”
The product release also reflects a broader debate across compliance technology: whether AI can improve detection without weakening auditability, transparency, or regulatory confidence. Napier AI is explicitly positioning the feature as compliance-first and human-in-the-loop, rather than autonomous decision-making. That matters in AML, where institutions are under pressure to improve detection quality while keeping investigations explainable and operationally manageable.
Will Monk, Chief Product Officer at Napier AI, said the aim of Insights AI is to put “the power into the hands of AML analysts, to make the best possible human-in the loop decisions for the alerts”. In practical terms, the launch is about reducing friction inside investigations while making advanced model outputs more usable at analyst level.
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