According to study conducted by gov.uk, an estimated 0.5% of the UK population met the criteria for problem gambling, while an estimated 3.8% of the population are categorised as at-risk gamblers. That's potentially more than 2.5 million people.
The report states, "The evidence implies that problematic gambling should be considered a public health issue because it is connected with harms to people, their families, close friends, and society at large."
The paper asserts that gambling-related suffering affects people, families, livelihoods, and life itself and imposes substantial costs on society, potentially "in excess of £1.27 billion" annually. In recent years, operators in the United Kingdom have been subjected to stricter laws and enforcement, resulting in an increase in social responsibility fines of over £40 million per year.
Consequently, the problem gambling issue is here to stay and, at least in terms of regulation, will be felt more in Europe than in the decentralised US business, which will ultimately catch on. What does the future hold, though? Obviously, the issue must be brought to light, knowledge gaps must be filled, and awareness must be raised, but will this be enough? To complete a task, operators require the proper equipment to help them meet the challenge. Having a predictive model in place will soon be required.
In the coming weeks, the UK Gambling Commission will require operators to "take prompt action when vulnerability signs are found." That is to say, operators are expected to implement predictive models. Can technology play a vital role in combating the issue?
How predictive models function
A widespread misunderstanding regarding responsible gambling is that operators can only respond to it and not forecast it. This is false, and we need to know it. Our services have always included predictive models.
Modelling predictive customer behaviour is the application of mathematical and statistical tools to historical and transactional data in order to predict future customer behaviour. The benefits are considerable. By anticipating high-risk players in advance, operators can:
Create daily lists of potentially at-risk players for account managers or support teams to monitor.
Utilize the risk potential of gamers to optimise marketing initiatives and limit the number of players who engage in unhealthy behaviours.
Produce quarterly reports and track trends.
Models for predicting responsible gambling differ, but typically include the following fundamental guidelines:
Exploring historical data and constructing a definition based on trends to identify at-risk players. For instance, an operator classified their at-risk gamers by assigning a weighted score between 1 and 10 to a variety of player behaviours, such as time spent on site and bonus usage. The more a player's weighted average, the greater his or her risk.
Understanding the data - The more data given to an algorithm for machine learning, the more precise its outputs.
Selecting the variables - Once the dataset is balanced, the process of selecting the variables can commence. It is vital for the accuracy and success of the prediction model to choose the proper variables.
By clustering players into groups based on the gross amount or trendline slope of daily wagers, operators can divide players into two categories: those with a high possibility of becoming at-risk and those with a low likelihood.
The machine learning system will run and identify gamers who are expected to become at-risk. Self-optimizing models permit changes in player preferences and industry trends to influence the design of the model.
Using the at-risk predictive model, divide players into three levels based on their potential of becoming at-risk: low, medium, and high. On occasion, promotional campaigns may be offered to players with a low risk threshold. In contrast, medium-risk players can receive 30% of the promotional efforts that low-risk players received, while high-risk players can only receive informative and instructive advertisements.
Seatbelts and gambling responsibly
Did you know? Before 1966, many vehicles in the United Kingdom were manufactured without seat belts. Many automobile manufacturers offered seat belts as options. What about the mandatory seat belt law? This only went into effect in 1983, but it seems so obvious, almost natural. Seatbelt usage has become second nature.
The same holds true for responsible gambling in the United Kingdom and Europe (the United States will take longer, but will eventually catch up), and all supporting variables are in place. A custom machine learning system can enable gaming operators to gain a deeper understanding of their player pool, their trends, and the behaviours they demonstrate prior to becoming risky. These insights can optimise marketing campaigns and increase player retention over the long run.
The industry is becoming increasingly conscious of the potential harm to its players and business, and it will respond voluntarily or not. When they do, technology will be prepared to play a crucial part in addressing the problem.
By fLEXI tEAM