Innovations are essential for progress. This is the case in general, but especially in a dynamic industry like digital payments.
Successfully implementing Artificial Intelligence
To meet the ever-growing consumer demands for simple and frictionless experiences and to prevent cybercrime as much as possible, technological advances in Artificial Intelligence (AI) are indeed crucial.
Still, there are several challenges to deal with before the full benefits of AI can be harnessed – writes David Daly, Scientific Community Editor-in-Chief at Worldline.
AI is a major trend within digital payments. AI has already become part of many people’s day-to-day lives: just think about the chatbots which you communicate with on websites or the use of voice recognition when you are calling a company.
Where Artificial Intelligence is already being used for fraud detection and for dynamic transaction routing (which adapts iteratively and autonomously based on the feedback that it retrieves from each transaction that is processed), I believe the use of AI still has potential to significantly increase in the coming years.
And that brings us to the major issue in this matter: how do we keep processes that are run by AI safe and successful, especially when they involve a lot of value and/or responsibility. I would like to tell you more about the four very important challenges in using AI, which are all connected:
Accountability
Once you have Artificial Intelligence making decisions on someone’s behalf, the question becomes legally “Who will be responsible for its actions?”. For instance, when a self-driving car causes an accident, who is to blame? The programmer, the manufacturer, or someone else? Naturally, this is also important within finance. Whereas the consequences are manageable when it comes to a low-value purchase, when AI is used to purchase high-value products such as stocks, a house or a car, someone must be held accountable. At the moment, guidelines and regulations for this responsibility are still evolving.
Acceptability
Another very interesting challenge is to what extent AI is accepted. It takes a lot for people to trust AI (for example, current research suggests that autonomous vehicles need to be 100 times safer than those driven by humans for their use to be publicly acceptable). It does not suffice for AI to be as good or as safe as the human alternative; AI needs to be much better than when a person would be involved.
Explainability
When a bank declines credit to someone, for example, in many countries the bank must explain why this request has been denied. When an AI algorithm is used in this case, it can be seen as a black box; you feed it some data and on the basis of this it may decline the request. However, this is legally not compliant because you must provide the reasoning behind this rejection. Also, when AI is used in an advisory role, it should be clear how the AI technology arrived at a particular recommendation. Without this explanation, AI would be very difficult to use in critical decision-making situations.
Measurability
With traditional programming, you program the system once and, afterwards, you can easily test and measure how it performs. With AI programming, however, programs can be adaptive, making it more difficult to tell whether the algorithm is performing better or worse than anticipated. Therefore, traditional KPIs do not work and measuring the result or performance of an algorithm is much more difficult.
As you can see, there are a lot of important challenges to face when implementing AI. It is not a technology you implement from one day to another. It is a thoughtful process and only when the challenges are taken into account it is possible to properly implement AI.
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