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Why Online Gambling and Gaming Companies are Rolling the Dice on Machine Learning

Jay PataniIn this special guest feature, Jay Patani is a Tech Evangelist for ITRS Group, examines how online gambling and gaming companies have access to a wealth of big data created by every click of a customer’s mouse. The key to extracting valuable predictive insights from that data will be sophisticated machine learning. Jay Patani is a Tech Evangelist for ITRS Group, advising large Financial Institutions on big data strategy. He focuses particularly on the growing field of streaming analytics. Prior to ITRS, he was responsible for UK operations at a fast-growing startup in Berlin as well as working for a London-based early stage venture capital fund. Originally hailing from London, Jay is currently based in Hong Kong.

Gamblers often rely on hunches or intuition, while the House prefers hard facts. Ultimately though, they’re both all about prediction. Fortunately for online gambling and gaming companies, they have access to a wealth of big data created by every click of a customer’s mouse. The key to extracting valuable predictive insights from that data will be sophisticated machine learning.

Why machine learning?

Machine learning means the ability to learn relationships and patterns within data without being explicitly programmed. It requires large data sets and it requires planning. Different companies have different priorities and goals behind developing machine learning algorithms. One may want to harness player data to inform and improve game design, whereas another company may be more interested in maximizing revenue and identifying the players most likely to spend money.

Let’s use the example of a business that wants to tackle the problem of addictive gambling behaviour in order to keep customers gambling safely, and within the perimeters of regulation. Machine learning algorithms are a good solution as they ‘learn’ patterns and correlations from vast historical data sets of past player behavior and then can predict future outcomes. A key example of this would be whether a player is addicted or not.

In the case of spotting addictive behavior, a gambling company can build a profile of what constitutes normal behavior for each player and machine learning algorithms will identify deviations to the normal behavioral patterns. This can be used to alert a gambling or gaming company when a player exhibits addictive habits so that the company can potentially intervene and take corrective action.


Machine learning models can broadly be categorized into three types: clustering, classification and regression. The gambling example above could be achieved by a classification model, in which the algorithm identifies which class a data observation belongs to out of a set of pre-defined classes.  For example, these algorithms can be used to predict whether a customer is addicted or not; whether a player is a bot or a real player; or whether a customer is likely to de-register or not.

Regression models, on the other hand, find relationships between two or more variables and predict a numeric value, such as how many players will be online at 7pm on Friday or how much a player is likely to spend in their lifetime.

Lastly, clustering models identify similar instances and group them into clusters.  This is often useful for recommendation algorithms, where it is possible to recommend relevant information to a player based on the similar preferences of those in their cluster. It is also a useful tool for data exploration as it automatically highlights commonalities within certain groups of players. It enables detection of extreme or fraudulent behavior where the observation is anomalous and falls outside of the cluster groups.

Machine learning can give online gambling and gaming companies a major boost commercially and help them to act responsibly and compliantly by predicting problem behavior before too much damage is done. It requires significant investment of time and resources but machine learning is a safe bet for those that get it right.


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