“ Fraud prevention requires looking at many different variables and data types , and giving a real-time response . AI excels at that ,” he says . “ Some of them are given by the user , some of them are actually behind the scenes and are passively collected . All of this needs to be calculated very quickly with a decision made before money is lost . That ’ s especially true in digital payments and in digital goods . AI is very useful at that , and I think it ’ s still the dominant way to tackle fraud scenarios right now .”
Kabala believes that compliance has some catching up to do as an AI usecase , particularly when compared to fraud prevention . “ For compliance , it ’ s still a work in progress ,” he tells us . “ Because regulators typically require companies audited to provide a ‘ show-of-work ’, outlining every step of the way in coming to a particular conclusion , it makes the usecase a bit different to fraud prevention . The timeframes are longer , and the required detail is greater . But AI is getting there .”
It ’ s important to distinguish between these traditional applications of AI , which learn from patterns of data and act upon them , from Generative AI , which has stolen a lot of the recent attention and dominated public discourse . They are two separate channels , although that doesn ’ t mean that Gen AI has no role in financial services or payments ; in fact , the future for Gen AI looks very promising indeed .
Gen AI is set to become a US $ 1.3tn industry by 2032 – an increase of more than 3,000 % compared with 2022 – according to research from Bloomberg Intelligence .
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