Soft2Bet iGaming Compliance
Used well, AI can help
responsible gambling teams do the one thing they rarely have enough of, which is prioritise. It can surface patterns at scale and highlight where attention is needed, but I do not believe AI should be the final voice making the call.
CONTROL COMES BEFORE CAPABILITY
Before anyone gets excited about models, teams need a clear view of what AI is actually being used across the business. An AI inventory sounds basic, but it is often the fastest way to surface risk, especially when employees are already familiar with such tools. From there, the sensible move is to classify use cases by risk and revisit that classification when the use changes, because a tool that feels harmless for drafting can become sensitive the moment it touches decision-making or player-related analysis.
Ownership has to be equally clear. AI spans product, engineering, data, privacy, security, legal, and compliance, so oversight requires a straightforward decision-making and escalation process. AI literacy is most effective when it is built into day-to-day workflows, including procurement, where AI checks sit alongside privacy and security checks, backed by policies that protect confidential information when external tools are involved.
THE REAL WORK STARTS AFTER LAUNCH
AI systems often perform best when first deployed, as the data is familiar, benchmarks are fresh, and the application closely matches
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the original design. Te risk usually shows up later, once the model is embedded in everyday workflows and the business starts treating it as “business as usual”. Tat’s when false positives can quietly stack up, triggering interventions that feel justified in isolation but add up to the wrong outcome over time. Drift adds another layer, because both data and behaviour change, and yesterday’s thresholds can become today’s blind spots.
What separates mature teams is the discipline around what happens next. Performance monitoring, incident handling, and audit trails must remain intact long after deployment, with clear ownership for investigating anomalies and making safe adjustments. In 2026, trust will hinge less on whether an AI model looks good in a demo and more on how confidently you can manage it when it misfires in the real world.
RG STILL NEEDS HUMANS IN THE LOOP
Used well, AI can help responsible gambling teams do the one thing they rarely have enough of, which is prioritise. It can surface patterns at scale and highlight where attention is needed, but I do not believe AI should be the final voice making the call. Te moment you solely rely on an AI model, you risk turning a sensitive, human issue into an automated outcome that is hard to defend and even harder to get right consistently.
Tere is a practical reason for that, too. If you point AI at a player database without careful thresholds and context, it will find “risk” almost everywhere. I mentioned on the webinar that you can end up with outputs that effectively label 50 per cent or 60 per cent of a database as potentially high risk. Tat is not a workable result for any business, and it does not translate into meaningful player care because no team can intervene at that scale with the nuance it requires.
Te approach I trust is to use AI to create sensible categories and surface signals, then rely on humans to review, apply judgment, and decide on proportionate interventions. Data protection also needs to sit close to this work, because responsible gambling models can involve large volumes of behavioural and financial indicators that carry long-term obligations once collected and used.
THE PRODUCT HAS TO DO MORE OF THE WORK
Across iGaming, the levers that once made acquisition easier are narrowing. Marketing, sponsorship and promotions are all being
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