Artificial intelligence is transforming the way many industries work, particularly the financial sector. New systems have been introduced that utilise AI to fight money laundering and speed up investigations, but AML AI isn’t always good.
AI brings a whole range of challenges, from criminals utilising the software to advance their financial crimes to hefty price tags and tricky integration. In this blog, we’ll dive into the challenges AI is presenting to AML solutions and how this affects financial institutions.
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Money laundering is always evolving, with criminals using new tricks, loopholes and technology to conceal financial crimes. The problem this poses is that AML AI learns by analysing past cases. This means that whilst AI may be able to detect old or existing fraud patterns, it won’t be able to spot any new tricks that humans may pick up.
If criminals use new tactics to launder money, AI doesn’t have the intuition and experience that expert AML officers have and can only work from information it has already learned. This means that AI can miss critical signs of fraud or money laundering during suspicious activity reporting.
What makes AML platforms work effectively is a streamlined system with KYC and KYB checks alongside both manual and automated processes. When it comes to AI, this technology needs data that is easy to decipher, but this is rarely the case in complex fraud investigations. The data is messy, and banks, customers, and organisations often communicate poorly with each other. This means there is often a lack of quality data for AI to use for training.
Without this adequate training and limited data, AI could potentially make incorrect decisions or miss key information that would be picked up through manual checks.
Anti-money laundering is one of the most heavily regulated areas of banking, with every institution having to meet strict standards. AML processes need to be streamlined across organisations to ensure that everyone works collectively and accurately to prevent and detect fraud. To ensure compliance, the Money Laundering, Terrorist Financing and Transfer of Funds and other regulations were established, but these processes don’t always work effectively with AI.
These processes are standardised across all businesses and were created on the basis of manual checks, and AI’s advanced data analysis can be a difficult tool to use alongside traditional regulations.
Issues like how to validate and audit AI come into question, as well as which AML AI systems should be approved for use. Consistency also poses a problem, as some organisations prefer to avoid AI and automation, whilst different countries have entirely different rules on AI.
Until new regulations and guidance are brought into play, the role and use of AI becomes a grey area, and much of the technology can’t be fully relied on until outdated AML rules catch up.
Whilst organisations can use AI to help with AML processes, criminals can also utilise artificial intelligence. In recent years, the growth of AI has led to many launderers using this technology to avoid detection during their schemes.
AI financial crimes and scams have become increasingly common, and criminals can use AI to create realistic false documents and identification that are difficult to detect during KYC checks. As AI crime becomes more advanced, so does the need to develop smarter AML AI to fight these crimes.
This creates an endless race between banks and criminals to develop tactics and means that organisations need to continuously stay on top of technology and AI models to ensure they keep up with criminals.
Whilst not impossible, introducing AI into AML processes is a lengthy, difficult task with lots of blockers that make this software unappealing for many organisations.
The majority of financial establishments already have complex IT systems that are based on years' worth of AML compliance and regulations. Integrating AI into these systems is a delicate and time-consuming process, and AI tools need to be able to interpret data from existing systems, which takes trial and error to complete.
Introducing AI also brings in harsh learning curves for manual AML officers as they need to be trained on how to use these often complex systems and how to accurately interpret AI data alongside manual investigations. This sort of training won’t happen overnight, and these additional training and integration issues can actually halt investigations rather than aiding them.
Whilst AI in AML can have some great benefits, the constant need to develop and update AI can lead to some hefty expenses. Alongside the initial purchase and installation costs, there are also continuous infrastructure expenses associated with regular training and hiring specialists who understand this software and financial crime procedures.
It also takes a lot of power to run AI systems, which not only raises electrical bills but also requires investment in more powerful computers. Data centres are responsible for 2.5 to 3.7% of global greenhouse gas emissions, which also adds an environmental cost and ethical pressures to the price of using AI.
One of the biggest long-term costs is the continuous maintenance and development needed to run AI anti-money laundering systems. Models need constant training, testing and new data in order to work accurately.
Whilst AI may be beneficial, many organisations may struggle to afford this expensive software. With larger companies investing in advanced tech, this could leave other financial systems behind and at a potentially higher risk of exploitation.
AI has huge potential to transform and assist the fight against money laundering and financial crime, but it comes with a number of risks and challenges. From high costs to grey areas in rules, institutions need to proceed carefully when integrating AI into their processes.
While relying solely on AI is always a risk, integrated systems like ours at SmartSearch offer a safe balance, utilising both automation and manual checks to form the optimal AML checking process. We offer services like Triple Check, which utilise automated technology and manual ID checking to ensure all corners are covered when conducting AML checks.
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