Martin Rehak, founder and CEO of Resistant AI, discusses how artificial intelligence (AI) can lend itself towards the fight against money laundering
The Fintech industry’s rapid growth and use of new technologies to meet the rise in demand for online services has brought with it increased levels of cyber crime. Criminals have taken advantage of the benefits digital banks offer to access money, launder illicit money and fund terrorism worldwide. The growth in technology for blockchain and digital payments provides new opportunities for criminals to launder funds at faster speeds and larger scales than they might have been able to previously. According to UK Finance, criminals stole a total of £753.9 million through fraud in the first half of 2021, an increase of 30% compared to H1 2020.
With the huge amounts of data that Fintechs process, it’s no mean feat to detect potential money laundering activities using manual processes. But as fast as financial services adopt AI and automation to scale, fraudsters are matching – and surpassing them – in sophistication.
Traditional financial institutions have had years to build out their AML programmes, gradually adapting to the increasing regulatory demands. Fintechs are playing catchup and trying to scale their resources and technology in line with demand for their services, whilst remaining compliant with regulations.
Digital banks need actionable insights fast to develop and improve their own AML/Countering the Financing of Terrorism (CFT) frameworks, but they face some key challenges in maintaining AML compliance. In 2021, the Financial Conduct Authority (FCA) announced that they were investigating Monzo for potential non-compliance with AML/CFT regulations, which should be a signal that there is increased focus on Fintechs.
Firstly, their reliance on online banking leaves them vulnerable when approving an account or transaction, needing proper risk assessment measures. Then there’s the amount of data which needs processing, and at pace, which comprises many types of data – from IP and geolocation data to other personal data obtained from apps and digital devices. The amount of data is hard to sift through to surface actionable, relevant, and timely insights – especially when current compliance processes are typically repetitive, data-intensive tasks that lack efficiency.
Besides the constraints imposed by convenience and the digital nature of their services, the fintechs also have smaller, leaner teams and budgets – yet must comply with the same regulations as bigger banks for AML/CFT. They also face similar licensing requirements, to obtain a licence through the Financial Conduct Authority (FCA) or partner with a licensed bank.
It’s vital for digital banks to be aware of the most prevalent financial crime typologies, to anticipate how criminals will exploit them. These include money mules, where individuals are recruited to move funds, knowingly or unknowingly, as part of a money-laundering scheme.
