ARTIFICIAL INTELLIGENCE
with the psychologists’ own human assessments in over 88 percent of cases and rising. The results will probably never be 100 percent given there will always be differences of opinion between psychologists themselves. The AI algorithm spots behaviors that fall outside accepted norms. They are statistical in nature rather than causal, so one of the challenges lies in applying context to the findings. For example, sports betting in the middle of the night could raise a red flag, but the customer may have just changed their shift pattern or be betting on sports taking place on the other side of the world if local sports are not available. Whilst its abilities are still improving as reference data increases, it is already clear that AI will mean that it is possible to monitor many more transactions. Indeed, such are the responsible gambling (RG) requirements now placed upon operators that many are saying that only through AI will it be possible to meet them. ChatGPT is essentially a glorified chatbot and it is no surprise that there is work now being done on AI-supported chatbots on gaming sites which can pop up and intervene if problematic patterns are observed. This is not an approach favored by everyone but it can be appropriate for players who are early in their slide into addiction. Either way, AI is doing a lot of the heavy lifting. If over 80 percent of the work can be done in the background, the human resource can be freed up, enabling them to intervene where they are most needed. When a member of the RG team contacts a customer, AI should mean they will have a much richer picture to use to explain why the intervention is being made, hopefully making it more effective. It is a decision-making tool designed to support those making interventions. As it becomes more sophisticated and accuracy increases, the number of false positives will be reduced keeping customers happy while prompting fewer but more necessary interventions. The patterns associated with problem gambling, for example, players losing control, playing more rapidly or chasing losses, can be detected in new or inexperienced players well before their behavior would normally be defined as problematic. When these early patterns are observed, it can indicate a player who will go on to develop a gambling addiction. Flagging such players does not mean they are automatically excluded from a platform, but it does mean they can be steered away from more risky forms of gambling keeping them in a safe zone where they can exist happily as long-term customers. As well as AML and RG, AI can help operators better target their marketing and retention strategies for example, by providing
customised player experiences which will optimise revenues. The flipside is also true, with the Advertising Standards Agency in the UK using AI to identify gambling adverts on social media which may break its rules. AI can also help bookmakers to set better odds and even monitor athletes visually to spot signs of fatigue or injury which could impact their performance. Big data: privacy, automated decision making and limitations on use The use cases above show the first steps in applying AI in the world of gambling, and there are sure to be many more that have yet to be dreamt up. Even at this early stage, however, there are those raising issues both regulatory and real world which will place limitations on what the future might look like and how quickly we will get there. All of the examples given so far show rely on AI’s ability to sport patterns in vast quantities of data. That may be fine in the online world where customers provide forms of ID linked to their game play habits and bank records. In the world of land-based casinos it is a very different story where cash is much more prevalent. Even where credit cards are used and linked to loyalty cards and other sources of data this is often stored onsite. Theoretically this might improve data security (although often not in reality), but the way it is stored, the frequency with which it is updated and the widely varying levels of quality mean the hygiene simply is not there for AI to be effective. Unless a sizeable investment is made in data quality and security it will simply be a case of garbage in, garbage out. Regulators have for years focused on online gambling as inherently more risky than offline, and it is certainly true that the physical requirement to be onsite is a limiting factor. But it is ironic that brick and mortar casinos may be left behind in the AML and RG revolution that AI promises to bring to their online counterparts. The chance to integrate AI AML and RG tools should certainly be part of the decision to go cashless. Quality of data aside, there are major issues of privacy and data ownership that will have to be addressed. Gaming companies will need to ensure that their use of AI complies with applicable privacy legislation in their jurisdiction, and this is changing rapidly. Certainly, the principle of data minimisation should be applied so that the only data collected is that which is necessary for the purpose and that it is retained only for as long as necessary to fulfil that purpose. The unique issues that AI can create will also need to be considered. AI relies on training
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IMGL MAGAZINE | OCTOBER 2023
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