Move beyond compliance tick-boxes and create practical, measurable approaches that protect both customers and businesses. Drawing from practices in multiple jurisdictions, this template helps leaders prioritise early detection over reactive intervention.

Decision Template

1. Purpose

This template provides executives with a structured framework to design and evaluate systems for identifying problem gambling behaviours before they escalate. The aim is to move beyond compliance tick-boxes and create practical, measurable approaches that protect both customers and businesses. Drawing from practices in multiple jurisdictions, this template helps leaders prioritise early detection over reactive intervention.

2. When to Use

Apply this template when:

• Reviewing or upgrading responsible gambling monitoring systems.
• Evaluating the effectiveness of current risk indicators across markets.
• Preparing for regulatory changes that strengthen responsible gambling requirements.
• Considering investment in new technologies, such as AI-driven behavioural analytics or cross-operator data sharing initiatives.
• Benchmarking against best practice in other regulated industries, such as financial services’ fraud detection or healthcare early-warning systems.

3. Template Framework

Step 1: Define Indicators
List behaviours that may indicate risk. Examples: frequent deposit increases, chasing losses, night-time play, use of multiple accounts, or sudden spikes in time spent.

Step 2: Assign Risk Weights
Weight each indicator by severity. For instance, a sudden doubling of deposits may score higher than late-night play.

Step 3: Scoring Matrix

Step 4: Set Escalation Thresholds
Define when scores trigger specific actions:

• Low risk (0–10): Automated nudge, e.g. safe play reminder.
• Medium risk (11–20): Customer contact and voluntary limit suggestion.
• High risk (21+): Account review, mandatory intervention, or regulator notification.

Step 5: Review and Adapt
Schedule quarterly reviews of thresholds and weights to ensure the framework reflects changing patterns, regulatory updates, and cross-border operational realities.

4. Guidance Notes

• Global Comparisons Matter: In the UK, the Gambling Commission has tightened expectations for proactive monitoring, particularly around affordability. In Sweden, regulators emphasise account-based tracking. Cross-jurisdiction benchmarking helps avoid gaps where regulations differ.
• Borrow from Other Sectors: Financial services apply layered risk-scoring for fraud detection, combining transaction patterns with customer profiling. Healthcare systems use triage models, where small markers combine to flag patients for early review. Both approaches show the value of cumulative scoring rather than one-off alerts.
• Balance Automation with Human Oversight: Automated monitoring reduces blind spots, but human review is essential to capture context. For example, an account flagged for increased deposits may simply reflect a customer receiving a yearly bonus. Human checks prevent overreach and reputational risk.
• Integrate Cross-Operator Insights: Markets like Australia are exploring shared data models to track at-risk players across operators. Executives should anticipate similar demands globally and build readiness for secure, privacy-conscious data sharing.

5. Questions for Senior Leaders

1. Which current indicators in our system are most predictive of harm, and which may be outdated or too blunt?
2. How often do we revisit thresholds and scoring weights to ensure they reflect new patterns of behaviour and regulatory expectations?
3. What lessons from other industries or jurisdictions could we adapt to strengthen our early detection framework?

Tier: Pro. Read time: 7–9 minutes.

Problem Gambling Early Detection Worksheet

Use this table to record behaviours, assign risk weights, and calculate scores for decision-making. Add rows as needed.

Total Risk Score: ________

Escalation Level: ________