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Accelerating Ethical AI for Responsible Gambling

The Need for Responsible Gambling Programs

Betting money on the outcome of an uncertain event fuels excitement and is popular around the world. People indulge in gambling either for recreational purposes, socializing, or for the sheer thrill of it. The pandemic led to the rise of online casinos, accelerating the growth of the gambling industry. The industry, which was growing at a steady rate of 2.1%, is now projected to grow at a CAGRof  11.49% over the next four years.

Gambling is entertaining if played within the boundaries of ethical, emotional, and financial well-being. Otherwise, the consequences are far-reaching and acute, ranging from a propensity towards stealing to domestic violence and drug abuse, ruining families and lives. Artificial intelligence (AI) helps address these issues by enabling responsible gambling programs to help protect players from over-indulgence and other adverse effects.

Responsible gambling ensures that a gambler does not engage in the activity to such an extent that it poses dangers to their emotional, mental and financial health. Government authorities, casino businesses, mental health organizations, and NGOs, such as the Responsible Gambling Council (RGC), have been working to mitigate these gambling risks. Organizations similar to the International Center for Responsible Gambling (ICRG) have been conducting research for over 25 years into various facets of gambling and establishing boundaries. Their recent funding of $40 million shows the impetus of the gambling ecosystem in providing treatment and help to those affected by gambling disorders. 

In modern casinos, trained personnel monitor individuals who engage in destructive gambling. However, it is difficult for them to keep track of all the customers. They also lack resources to analyze the relevant data points and extract insights for making better decisions. 

Some companies use rule-based systems where the system detects erratic players based on a defined set of specific rules. However, if a business owner adds more regulations, it leads to a conflict or overlapping of rules, which may result in inaccurate results. Moreover, these rules are set, as opposed to the learning that occurs in the case of an AI-model deployment, which makes a strong case for the use of efficient and advanced ethical AI.

Ethical AI for Responsible Gambling

The primary objective for ethical AI and responsible gaming to have a broader positive impact on society. Unlike rule-based systems, the AI model takes specific inputs and generates more effective, feasible, and accurate solutions with continuous reiteration. We need to define data points to input into the model for its training. Some of these data points include:

  1. Behavioral features during the entire gameplay. For example, a player X who deposits more money after winning around ten games or a player Y who deposits more money than they can afford after losing around fifteen games will raise flags.
  2. Betting frequency like bets per day, bets per week and so on with variance. 
  3. Past betting results and the likelihood of placing a bet that is detrimental to the players.
  4. Patterns of deposition of money into the gaming account and checking it on a weekly, bi-weekly or monthly basis.
  5. Time expenditure like time spent in a day or a week, variance in average time spent, distinct games per session, etc.

After training the model over these data points, it predicts whether the player is addicted or not. It also predicts the likelihood of a player getting addicted to gambling in the future. Businesses leverage AI to drive personalized messages and caution these players to play ethically. If they do not heed the caution, the business owners can set up a customized time or money limit for the players. 

The inferences of the AI model can be presented to clinicians and therapists engaging with problematic gamblers, and use their inputs to improve or continuously retrain the model. Human expertise identifies intricacies in the players’ behavior that the model may have missed, thus enhancing the model’s accuracy. 

AI’s Role in Implementing an Efficient Responsible Gambling Program

Identifying Problematic Gamblers: Once the model is trained using the data points, unsupervised learning models like Isolation Forest are used to detect anomalies/outliers. This model uses a random forest in which each decision tree is randomly grown. The dataset is divided using recursive partitioning and as it is gradually fragmented, each observation is progressively isolated from the others. The anomalies are assigned a score of -1 while regular data points are given a score of +1. 

When the process is complete, the organization can determine the addicted players as outliers in the dataset. They can get help for these players, gaining credibility as a business who puts player well-being first. This will lead to higher consumer confidence, improved brand perception, and increased customer loyalty.

We used a dataset, behavioral characteristics of internet gamblers who trigger corporate responsible gambling interventions, to efficiently segregate addicted players from non-addicted players via Isolation Forest. A snapshot of our test is produced here:

The first table shows the addiction behavior via key metrics like net loss and euros per bet while the second table gives the details of non-addicted players. The difference between the two is clearly depicted, for example, the net loss of addicted players is roughly 14.5 times more than the net loss of non-addicted players.

Detection of Underage Gamblers: Underage gambling is a legal offense, and gambling businesses need to implement checks to comply with the legal regulations of their operating areas. AI can be used in automating the identification of underage gamblers via video intelligence.

The classification model can be used in two ways for this function – Binary Classification and Multi-class Classification. Binary Classification divides the entire dataset into two-class labels to allow gambling above a particular age, for instance, people above and below eighteen.

Multi-class Classification is used when there are multiple class labels. For example, this classification model segments the players into different age brackets of 18-25, 26-34, etc. While Convolutional Neural Networks can be used to execute either binary or multi-class classification, regression analysis is used to predict the exact age of the customer, helping in customer segmentation.

Ethical Revenue Acceleration: Hyper-personalization ensures that the financial interests of the gambling businesses are considered while implementing a Responsible Gambling program. Models like Clustering can be deployed here to segment players and identify responsible and loyal players who will add a new stream of ethical revenue generation to the business. The exhaustive list of behavioral and transactional variables like days gambled, sessions per day, bets per day, total money bet, net profit/loss can be used to effectively cluster the players. 

Implementing a successful AI-powered Responsible Gambling program correlates itself with revenue generated ethically. Gambling organizations could focus their marketing efforts on casual and unaddicted players segments, while protecting addicted players.

Needless to say, a responsible gambling organization would attract more patrons by word of mouth.

Safer Player Engagement with Conversational AI: The AI chatbot enables personalized messaging to the players. The messages from the chatbot make them aware of the gameplay, the probability of losing money and directing the addicted players to help. 

The best-in-class Natural Language Processing capabilities need to be leveraged to achieve this. While NLP helps in deciphering text-based queries, Audio Speech Recognition using Convolutional Neural Networks or Recurrent Neural Networks can be used to bridge the right correlation between the context of the query and input analysis as the chatbot should generate a precise answer to the query to offer timely and efficient customer support. 

Quantiphi and Responsible Gambling

As part of a PoC for a client engagement Quantiphi, the team has developed pre-built models to tackle the above facets.

  • Post deployment, the Isolation Forest model, separated the addicted players from the non-addicted players, which was around 7.33%. 
  • Identification of underage gamblers was done by Binary Classification with 94% accuracy.The process of spotting underage gamblers can be automated whilst being compliant with Data protection regulations. (Kaggle Dataset)
  • Clustering to capture the players that can guarantee ethical revenue generation to the company and, at the same time, play responsibly.

Conclusion

Gambling addiction affects the lives of players and their families.  A recent move by UK’s Gambling Commission to minimize the harms of irresponsible gambling only tells us that Responsible Gambling is no longer an optional thing to do, but an imperative on leading Gambling businesses, and this is exactly where AI comes in. AI-based responsible gambling programs seamlessly integrate into the gambling ecosystem to change things for the better. From detecting problematic underage gamblers accurately, eliminating human bias, and driving the players to help, AI can be used to ensure a fair and safe gambling environment. It can also be used to generate a stream of ethical revenue and boost growth.

Quantiphi has built a robust AI solution portfolio to help gambling businesses address the most pressing issues and make gambling safe and ethically profitable.

Get in touch to learn more and explore how we can help you tackle your gambling challenges.

Written byAman Kureel

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