£100bn financial crime problem: Are anti-money laundering checks working?

Criminals are no longer confined to the dark web. Open-source platforms, including social media, online marketplaces, forums, and even legitimate business networks, are actively used for money laundering, fraud, and illicit activities.

New techniques, such as synthetic identity fraud, shell companies with misleading records, and the use of crowdfunding for illicit fund transfers, are making detection harder than ever. As a result, financial crime has cost the UK an estimated £100 billion a year.

With criminals becoming more sophisticated, regulated firms are feeling the pressure. In fact, nearly three-quarters (72%) report feeling overwhelmed by anti-financial crime compliance demands. 

Where anti-money laundering checks fall short

The challenge is that many firms still rely on manual, human-led AML due diligence processes. These manual checks:

• Can take days to complete
• Are limited in scope due to time constraints
• Will never fully capture everything available in the public domain about an individual or entity

AML teams have traditionally built risk profiles using structured data like:

• Sanction lists and watchlists 
• PEP lists (politically exposed persons)
• Corporate records (business ownership and transactions)

While useful, these sources are static and limited. They often miss critical insights found in:

• Investigative news reports
• Blogs and whistleblower accounts
• Deep-web sources and forums
• Interviews and exposés

With criminals moving faster than ever, firms must rethink their approach to AML due diligence. Traditional methods alone are no longer enough. With so much data online, firms could improve risk detection, but the sheer volume of information is overwhelming their resources. Without advanced technology to process unstructured data effectively, it either leads to missed risks or excessive workloads.

How long do anti-money laundering checks take

The duration of an anti-money laundering (AML) check varies widely, depending on how efficiently information is gathered, analysed, and processed. One of our legal clients is a prime example: they spent 2 million hours annually identifying clients in the AML process, with an average onboarding time of 26 hours per client. Matter-opening delays caused approximately 2.4 million hours of lost time each year. At a charge rate of £900 per hour, the total cost became so excessive that the General Counsel couldn’t even present it.

Why AML regulations are causing overwhelm

Regulations are evolving to close loopholes and tighten AML controls. But this has created a complex, constantly changing compliance landscape that compliance teams struggle to keep up with. Many firms worry about staying compliant, given the frequent rule changes and harsh penalties for violations.

Cost of compliance: a barrier to business

With compliance getting more stringent, nearly one in three financial institutions now see the cost of compliance as a barrier to profitability. Firms are forced to spend more on:

• Specialist compliance teams
• Advanced AML technology for monitoring and due diligence
• Regular training to keep up with changing regulations

Smaller firms, in particular, struggle to absorb these costs. This puts them at a disadvantage. Compliance isn’t just expensive, it also affects customer relationships. More than one in ten (13%) firms say compliance requirements have hurt customer interactions, as strict AML and KYC checks create friction in client onboarding.

How AI is speeding up anti-money laundering checks

Traditional AML due diligence is often slow, reactive, and dependent on structured data sources. AI transforms this process, enabling deep analysis in minutes rather than days.

AI can scan and analyse vast amounts of structured and unstructured data, identifying risks that human analysts might overlook. By automating the review of thousands of sources in real time, AI-driven tools can detect emerging threats, flag suspicious patterns, and provide instant risk assessments.

Criminals don’t just have a footprint within AML databases (in fact, it’s easy to get removed). They leave digital footprints across the open web. AI-powered AML solutions integrate open-source intelligence (OSINT) from indexed web sources, including media reports and investigative journalism, blogs, forums, whistleblower disclosures, news reports and much more. This additional layer of information adds crucial context to AML screening data for a more accurate risk profile. 

AI-powered tools can also link individuals and companies across multiple data sources, exposing hidden relationships, beneficial ownership structures, and networks of illicit activity. By connecting fragmented data points, AI uncovers red flags that would be nearly impossible to find and connects the dots manually. With this 360-degree view of risk, compliance teams can apply the right risk-based AML controls.