In recent years, anti-money laundering (AML) has become a top priority for accountants in the UK, following a rise in financial crime and increase in regulatory scrutiny.
It’s therefore essential to build the best AML system possible for your firm. You’ve no doubt already tried to do that — but have you adopted artificial intelligence (AI) into your firm for AML purposes?
By leveraging AI, you can improve your processes, detect suspicious activities and reduce the risk of financial crime. How? In this article, we explain everything you need to know.
Automating AML processes
One of the most significant challenges accounting firms face in their AML efforts is the manual processing of large volumes of data.
Not only is this way of working time-consuming, restricting the amount of checks firms can do in a given time, but it can be prone to errors and duplication, too.
AI-powered automation can help solve this problem, however, as it can pull in a lot of data from different external sources — including places like sanctions lists — and present it to you in a matter of moments.
In other words, AI can shave off a lot of AML work by collecting data for you, allowing you to focus on analysis and high-risk activities.
Automated data extraction will benefit a range of your AML efforts, from fraud detection to risk assessment and customer due diligence.
Improved fraud detection
However, AI isn’t just about spewing out a bunch of numbers; it’s also capable of detecting fraud by analysing data and recognising patterns.
Traditionally, detecting fraud has required a lot of work on your part to learn the truth of a business’s finances. You’ve probably built a selection of tools to help you do that — and AI is just one to add.
With it, you won’t have to worry about data extraction or painstakingly looking for irregular patterns. Instead you’ll just have to check the suspicious patterns the AI has identified to investigate further.
AI can identify all sorts of unusual behaviour patterns, such as people frequently changing their personal information, using multiple identities, or odd transaction frequency and amounts.
One last thing: you may be using data analytic tools, which have their advantages, but because of their use of deterministic rules-based logic, they can return a high number of false positives.
AI helps here too, as it can reduce the number of false positives, leaving fewer false red flags to check for suspicious activity.
Keeping up with fraud strategies
As with any type of technology, AI is only as effective as the person who programmes it. This is where a lot of the criticism of AI in AML comes from, as the biases of programmers could blind AI to certain suspicious behaviours and patterns a trained accountant might spot.
However, AI is constantly evolving to allow AML staff to get better at catching fraud. So, if your AI isn’t quite working how you want, you can change the rules based on past data and key fraud indicators.
Meanwhile, you can bet that software developers will work to improve their own algorithms to catch more. We may even start to see predictive models, where patterns of activity reveal new ways that criminals might engage in fraudulent activities.
Of course, criminals could find new tactics and avoid future predictive AI models, but AI will probably be faster than any other to uncover new patterns of behaviour and identify cases of fraud and money laundering.