Gambling has long been popular among in-person and online players.
However, with the rise of online gambling platforms, the accessibility of gambling has significantly increased, bringing with it a rise in problem gambling. This issue affects millions and can have financial, emotional and social consequences.
As such, the gambling industry is increasingly turning to machine learning (ML) as a tool for early detection and intervention to help combat these challenges.
The rise of online casinos
The online casino industry has experienced significant growth in recent years, driven by technological advancements and increased internet accessibility. According to a report by Research and Markets, the online gambling market grew from USD 79.80 billion in 2023 to USD 88.33 billion in 2024, with theindustry set to reach USD 161.32 billion by 2030.
Most gambling sites are required by their licensing bodies to implement responsible gambling policies, including self-exclusion options and deposit limits. For example, any given online casino available in Ontario must adhere to the rules set by the Alcohol and Gaming Commission of Ontario.
Understanding problem gambling
Problem gambling is a pattern of gambling behaviour that disrupts personal, family or vocational pursuits. The National Council on Problem Gambling reports that around 2.5 million adults in the US meet the criteria for severe gambling issues.
An additional five to eight million experience mild to moderate gambling problems. These behaviours can lead to financial struggles, relationship breakdowns and mental health issues, making early detection and intervention crucial. This is where machine learning takes centre stage.
How machine learning works
Machine learning, a subset of artificial intelligence, involves training algorithms to recognize patterns and make predictions based on data. In the context of gambling, ML analyzes user data, including betting frequency, wager amounts, time spent on gambling and sudden changes in behaviour, to identify potential problem gamblers.
Commonly used algorithms include decision trees, neural networks and clustering models, which can effectively predict and flag high-risk behaviours.
Data collection and analysis
Online gambling platforms collect vast user data, including transaction histories, game choices and behavioural patterns. This data is then processed using ML algorithms to detect anomalies or patterns that may indicate problem gambling.
For example, sudden spikes in betting amounts or frequency can trigger alerts, prompting further analysis or intervention.
Machine learning in action
Several online gambling operators have integrated ML systems to enhance their responsible gambling measures. For instance, Kindred Group, the parent company of Unibet, employs a system called PS-EDS (Player Safety Early Detection System). This system monitors user behaviour in real time, analyzing factors such as deposit frequency and changes in betting patterns. When potentially harmful behaviour is detected, the system alerts the operator, who can provide tailored interventions.
Another example is BetBuddy, a software solution that uses ML to identify at-risk players by analyzing their gambling behaviours. BetBuddy's approach includes clustering similar behaviour patterns and applying predictive models to assess risk levels. This proactive approach allows operators to engage with players before their behaviour escalates into problem gambling.
The benefits of using machine learning
The use of machine learning in detecting problem gambling offers several significant advantages:
Enhanced detection accuracy
ML algorithms can process vast amounts of data quickly and accurately, identifying patterns that traditional methods may overlook. This leads to more precise detection of at-risk individuals, enabling timely interventions.
Real-time monitoring
One significant advantage of ML systems is their ability to monitor user behaviour in real time. Continuous data analysis allows for immediate identification of risky behaviours, which can be crucial in preventing further harm.
Personalized support
Based on analyzing individual gambling patterns, platforms can provide tailored resources and limit-setting options. Personalized interventions, such as deposit limits or self-exclusion options, empower players to take control of their gambling habits.
Challenges and limitations
While the use of machine learning in problem gambling detection is promising, it is not without challenges:
Data privacy concerns
Data collection and analysis can be a significant privacy concern for players. Users must trust that their personal information is handled securely and that data is used responsibly, ensuring worldwide compliance with data protection regulations.
Potential biases in algorithms
The efficacy of ML algorithms relies entirely on the data they were trained on. Therefore, biases in the training data can lead to inaccurate predictions, resulting in false positives or negatives. Continuous monitoring and updating of algorithms are necessary to mitigate these issues and ensure fair assessments.
Tips for safe and responsible gambling
Promoting responsible gambling is a shared responsibility among operators, regulators and players.
Here are some practical tips for safe and responsible gambling:
- Choose licensed and regulated sites: Opt for gaming platforms licensed and regulated by reputable authorities. This ensures the site adheres to strict standards for fair play and player protection.
- Set limits: Establish personal limits on deposits, wagers and gambling time. Many platforms offer tools to help players set these limits.
- Recognize warning signs: Be aware of the warning signs of problem gambling, such as chasing losses, gambling to escape problems or lying about gambling habits.
- Keep support in mind: There are plenty of organizations like GamCare and the National Council on Problem Gambling available that support individuals struggling with problem gambling.
The future of ML and problem gambling
Machine learning in gambling is expected to expand as technology advances. Future developments may include enhanced predictive models, improved user interfaces and increased collaboration between industry stakeholders and regulators.
These advancements aim to create a safer and more responsible gambling environment, assisting operators and players.
Combatting problem gambling with machine learning
Machine learning plays a vital role in detecting and mitigating problem gambling, offering a blend of advanced technology and human-centred support. While challenges such as data privacy and algorithm biases remain, the benefits of ML in promoting responsible gambling are promising.
As the gambling industry continues to grow, ongoing research and collaboration among stakeholders is essential to balance the popularity of gambling with the safety and well-being of its participants.
DISCLAIMER: The information presented on this page/site is intended for entertainment purposes only. Please be aware that gambling entails inherent risks, and it's crucial to acknowledge this when utilizing online gambling platforms.
You must be 19+ years old and physically located in Canada to wager. Please check your local laws to ensure online gambling is available and legal where you live.
If you think you are developing a gambling problem or suspect somebody you know does, call the National Gambling Helpline at 1-626-960-3500 to speak with a professional.
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