DATA & ANALYTICS
“ One of the main challenges is managing and analysing large sets of unstructured data ”
ARIEL SHOHAM VP OF RISK PRODUCT , MANGOPAY
now providing some of the financial services that bigger providers once did , this process of refining and selecting features can be a significant challenge for vendors .
Equally , lack of transparency within some of the models used means that the amount of extractable insights to be garnered from the decisions these AI systems use is blinkered .
“ Many fraud prevention solution providers deploy systems that deliver results without clear explanations ,” Ariel explains . “ This makes it difficult for their clients to understand the logic behind decisions . This “ black box ” approach , where the process isn ’ t clear , makes it tough to use AI ’ s findings to help guide big -picture choices .”
Some of these issues , while limiting , still serve the wider goal of fraud detection and prevention . Yet , as attackers press on with new ways to exploit systems , defenders need to use the innovations in AI to be able to tackle them
Attackers wielding AI A 2024 report by fraud detection company Signicat and consultant Consult Hyperion showed deepfakes now represent 6.5 % of total fraud attempts , marking a 2137 % increase over the past three years .
With many ID checks now done over apps via video and voice passwords , this represents a significant challenge for checking those trying to access their accounts .
“ One overarching issue in fraud detection is that new scams are constantly arising , with professional
142 September 2024