Responsible Gambling Algorithms

Industry executives and experts gathered for a BetBuddy roundtable on responsible gambling algorithms.

Categories

The research explores the development, application, and oversight of responsible gambling (RG) algorithms, focusing on how data-driven models can support harm minimization strategies in the gambling industry. The roundtable brought together industry leaders, regulators, academics, and treatment providers to discuss the balance between model accuracy, transparency, and ethical implementation. Key findings:

• RG algorithms can support both harm minimization and prevention strategies across different risk levels.
• Simpler models are a good starting point, with complexity added as expertise and business needs evolve.
• Self-exclusion data alone is insufficient; new data sources (e.g. loss of control indicators) are needed to improve model accuracy.
• Transparency and interpretability are more important than pure accuracy, especially for regulatory and treatment contexts.
• Company culture is critical—ethical leadership and customer-first values reduce the risk of misuse.
• Model development should be triangulated through internal teams and external experts to ensure robustness.
• Operationalizing model outputs (e.g. for player messaging) requires cross-departmental coordination.
• RG algorithms must be empirically tested for both predictive accuracy and behavioural outcomes.
• Population-level research can help benchmark risk scores and enable cross-operator comparisons.
• Duty of care concerns should not inhibit innovation—well-implemented RG algorithms can enhance protection.
• RG analytics may become a competitive advantage, improving customer trust and brand value.

These insights support a responsible, transparent, and collaborative approach to RG algorithm development, balancing innovation with ethical safeguards. For full details, please refer to the complete document.