This AI is combating money laundering and keeping out Russian oligarchs

Banks and financial institutions are facing a rising tide of fraud and money laundering, and a growing pressure to keep up with tightening financial regulations.

Despite increasing spending by up to 10 per cent a year in some advanced markets between 2015 and 2022, the financial industry detects only about 2 per cent of global financial crime flows, according to Interpol.

Now, some believe artificial intelligence (AI) could help relieve the burden.

In Norway, the fintech start-up Strise has built an AI platform that scans public registries and media reports to flag potential money-laundering risks in real time.

The AI agent is designed to vet new applications for opening accounts at financial institutions subject to the European anti-money laundering legislation, such as banks, insurance companies, and payment services.

Replacing a time-consuming and labour-intensive process

If you’ve ever opened an online bank account, you’ll have been asked to fill in details such as your address and occupation and update them once a year. This is part of the Know Your Customer (KYC) process, a legal requirement designed to verify who clients are and where their money comes from.

Traditionally, KYC checks have relied on teams of compliance analysts sifting through databases, corporate filings and news reports to confirm ownership, trace connections, and spot potential risks.

These checks are meant to stop criminals from using legitimate banks to move dirty money.

But they are slow and expensive.

“Now you can have AI that retrieves information and puts it together in a whole new way,” Marit Rødevand, Strise co-founder and CEO, told Euronews Next.

“If you can spot a shady company at the point of onboarding, you can prevent them from getting a bank account, being onboarded to financial solutions,” she added.

Strise’s AI system automatically identifies warning signs such as links to sanctioned individuals, high-risk jurisdictions, or politically connected figures who may be vulnerable to corruption.

For example, analysts who use this system can see warning signs on individuals on sanction lists and politicians who may be “highly influential” or “more susceptible to corruption” and “money laundering,” according to Robin Lycka, a solution architect at Strise.

Russian oligarchs

Strise says financial institutions using its platform have been able to identify and decline high-risk companies more efficiently, increasing their case-handling capacity up to tenfold without adding staff.

In a demonstration, Strise showed a company portfolio where warning signs flashed over a possible Russian oligarch ownership.

“Once you have that information, you can choose from a portfolio level whether or not you want to complete that onboarding with the calculated risk classification,” Lycka said.

In another portfolio, the system flagged an Estonian-based company associated with two individuals who had been convicted for one of the largest cryptocurrency frauds in history, amounting to $560 million (480 euros).

The platform can also generate reports and summaries of its findings, using large language models (LLMs) to compile risk narratives for regulatory filings, a task that previously required hours of manual writing.

“What makes me hopeful is that we can really make an impact, moving away from just checkbox compliance to actually freeing up resources to really help stop financial crime and really get into preventing fraud,” Rødevand said.

“There are so many cases in the media and personal stories about lives being devastated by these types of crimes. And I truly want us to help change that,” she added.

The European Union is currently finalising a sweeping Anti-Money Laundering Authority (AMLA) in Frankfurt and an EU-wide directive due to take effect in 2027 “to combat money laundering and the financing of terrorism”.

Stanislaw Tosza, an associate professor in Compliance and Law Enforcement at the University of Luxembourg, told Euronews Next that the reform brings in a “new area of responsibility”.

“The ever-expanding scope of anti-money laundering (AML) obligations, combined with the increasing risk of sanctions for non-compliance, makes AI an attractive tool for obliged entities seeking to manage these growing responsibilities,” Tosza said.

He added that under EU data protection law, some degree of human oversight is required “when automated systems make decisions that significantly affect people”.

Strise says its customers have been able to reduce false positives, which is when a system flags something as suspicious even though it’s completely legitimate, by “30 to 40 per cent with automated customer monitoring”.

“This means far less manual work for analysts who would otherwise spend hours reviewing unnecessary risk alerts rather than catching real risk and fighting financial crime,” Lars Lunde Birkeland, Strise CMO, told Euronews Next in a statement.

But experts caution that while automation may reduce the number of false positives, it can also make errors harder to detect or contest.

“The integration of AI into these decision-making processes further reduces transparency: it may become even more difficult for affected individuals to understand the basis for such evaluations or to challenge them effectively,” Tosza said.

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