It is well documented how A.I./machine learning has been used over the past 30 years to help financial institutions prevent fraud on consumer credit cards. The reason why this was a particularly good use case for the first advances of this technology, was the ever-growing amount of data. As we know neural/machine learning technology learns and performs better when there is more data.

As the commercial landscape has changed with more e/m-commerce, machine learning technology has had to evolve. The reason A.I. is such an important part of the fraud prevention suite of tools is that traditional rules engines have limits, and the modern fraudster spends a lot of time mimicking good customers – i.e. hiding in the data. Today’s fraudsters are very good at detecting vulnerabilities in operational and application management processes by targeting the weak links. 

Modern-day fraudsters operate inside very sophisticated businesses and spend more and more time targeting merchants, not individual consumers. Why target individuals for comparatively smaller gains, when you could target businesses? Whether that is stealing large amounts of data (data breaches) or trying to defraud an acquiring bank, fraudsters are continuing to innovate and expand their operations.

Today’s fraudsters have the technical capability to manage big data and to use the dark web to locate vulnerabilities and devise multidimensional tactics that inflict damage, by sequentially compromising more than one point of vulnerability. Organisations that want to defend themselves against these risks and thwart modern fraud attacks must be able to react quickly and in real time. To do this, they need powerful solutions that are responsive and dynamic, and yet still easy to use and integrate into their existing systems.

By using state of the art machine learning technology coupled with best of breed automated fraud prevention management tools, organisations can significantly reduce the amount of time it takes to analyse data and increase accuracy in fraud detection. In summary, a reduction in false positive rates, means less declines, more transactions and increased operational savings.   

It is important to understand, that there is no point of just utilising machine learning for detecting and preventing fraud. It is critical to ensure that the technology aids the entire end to end fraud management process. Essentially using machine learning as the autopilot.

As with the analogy of flying a plane, sometimes manual intervention needs to be implemented.  Introducing autopilot ML to replace just one of the manual processes, involved in fraud prevention, can drastically reduce the amount of repetitive, expensive work a fraud analyst must do. Allowing them to focus on more valuable activities.