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BH: There are certainly similar challenges, but there’s often nuance and different perspectives, too. On our first panel on Monday, there were representatives from Canada, Denmark, Nigeria and Singapore. Very different cultures and environments, and yet you could see them coalescing around the talking points. They all need to keep their jurisdiction and regulatory regime fit for purpose. They recognize that the industry is changing, and it’s not easy sometimes to move as quickly as they would like. There’s definitely something around the attraction and retention of talent. We, as a sector, are not alone in that. However, the range of skills needed within the regulatory space is much greater than it was even five years ago. We have to think much harder about how we attract and keep the best people when we compete with the private sector on salaries we can pay. Then, there’s an interesting shared focus on making the most of the opportunities around data. Being efficient and effective with data is something everyone I talk to sees as a challenge. And then, finally, illegal gambling is a sectoral issue that we all have in common. Whether you’re a regulator, an operator or an advisor, people are thinking about illegal gambling, its impact and the role they should play in limiting it. SP: Picking up the point about data, there has been a lot of talk during the conference about data driving regulatory decisions. What do you see as the possibilities and the limitations of that approach. BH: Personally, I think the biggest limitation around data is aligning around what we mean by data and AI. AI spans everything from predictive text on your phone to the most complex, deep learning model and everything in between. It’s an easy word to throw around, but you want to be clear what you mean by being data-driven.
From my perspective, there are two things. The first one is around process. How do you make your process more efficient? How do you use data to re-engineer and improve processes? And if you look at the evolution of risk models, for example, the risk of operator non-compliance, how do you make sure you target your resource in the right place at the right time for the right reason? I think this is one of those areas where data is going to increase our potential to be more efficient and effective. The second point is around data analysis and the ability to deliver deeper analysis than you can do with traditional techniques. By using wider data sets, we can better understand the market and what players are doing. In turn, this should enable us to see how things are changing and the impact that has so we can make decisions on whether to react with regulatory changes. Put simply, it means we can target more effectively than we have in the past. SP: AI has been high on the agenda of the conference. Do you see an additional challenge for regulators to enlarge their employee base and bring this kind of expertise in house? BH: There’s as interesting balance between what you can retain in-house and what you keep on retainer, as it were. From the perspective of my day job at the Gambling Commission in Great Britain, I’m keen to develop in-house capability, but I wouldn’t think for a minute that we will always have all the skills we need. We need outside experience to come in and supplement that. But it’s an ongoing challenge. Teo Chun Ching from Singapore made the point in his panel session that they don’t need everything in-house. They can easily buy in highly specialized expert services. That’s definitely a model, isn’t it? What’s interesting is the tipping point in terms of developing something in-house versus bringing in new thinking, a fresh perspective, someone who’s
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IMGL MAGAZINE | DECEMBER 2024
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