Eve Whittaker, Market Planning Director, LexisNexis Risk Solutions.

Money laundering should never be viewed as a victimless crime, which is why banking regulators and the financial industry need to make it more difficult for the bad actors that undertake such crimes to succeed.

After all, effective prevention of this type of crime will save the financial sector millions – if not billions – of dollars, while keeping regulators happy. At the same time, it could conceivably save scores of lives and improve the global economy through the adoption of new technologies.

One such technology is artificial intelligence (AI), which could be used to thwart global financial crime through the effective leveraging of AI-derived insights by financial institutions, something that could dramatically improve their overall compliance programmes.

Eve Whittaker, Market Planning Director at LexisNexis Risk Solutions, notes that there are many ways that AI can be leveraged to create such a large impact.

“For one, the enhanced detection offered by AI models is powerful, as the technology can analyse vast quantities of data points. Moreover, it can also consider how these interact, positioning it to more reliably predict when and where fraudulent activity is taking place,” she says.

“AI models are able to detect and surface fraudulent behaviour far more rapidly, while also allowing banks to keep up with evolving patterns. In the past, such behaviour was identified through rules-based systems, but AI is more dynamic in the ways it looks for risk. Remember that with a rules-based model, you need regular updates, whereas with an AI model, it is able to learn from previously-identified behaviours, creating a more proactive and dynamic approach to determining risk.”

She adds that it makes a huge contribution to risk assessment, in terms of understanding how portfolio customers actually react and behave. Because the AI understands what constitutes unusual behaviour from such customers, it can more easily detect risks earlier, ensuring effective compliance.”

Whittaker suggests that in the grand scheme of things, the wider impact of such technology can be very significant, providing the technology is adopted in the right way.

“Essentially, AI improves both the efficiency and the speed of detecting risk. And the earlier one can surface a risk pattern, the sooner one can stop it happening. With banks, real-time transactions are a key part of today’s digital world. Therefore, real-time interdiction is equally important, because, in a situation where a transaction is completed before any fraud is detected, the cost can be huge for a bank. Thus, the ability AI offers to process vast quantities of data rapidly and speedily, helps to detect anomalies and allows for action to be taken and for the bank to quickly intervene.

“AI also learns on the go, so organisations can consider a bigger set of data points and how these interact, in order to see if these conform to specific patterns of fraudulent activity. This could include things like biometric analysis or device profiling of individual clients.”

She points out that AI also allows rapid processing of all key factors, and then determines if there is big enough combination of these to dig deeper. It’s about running a model that allows it to surmise if risky business is happening, by providing more nuanced detail – such as the pressure a client puts on their phone’s screen, or the way they swipe. Ultimately, every piece of data can serve as a clue to help surface fraudulent and criminal activity.

“The main areas where AI is beneficial includes analysing behaviours and data points, but there is also an efficiency angle – it works really well in reducing false positives. This is because existing monitoring systems tend to throw up a lot of false positives, and an enterprise can waste significant time clearing these. AI, on the other hand, can compare an incident to other, previous ones, helping to eliminate false positives more quickly, thereby boosting efficiencies,” concludes Whittaker.