A Deep Learning Framework for Ensuring Responsible Play in Skill-based Cash Gaming

Games of skill are an excellent source of recreation, entertainment and relaxation. However when these games are played with real money, the important facet of responsible game play must be addressed rigorously and efficiently by game providers. Ensuring game prudence, whereby users play real-money skill games only for entertainment purposes, and do so well within their resourceful means, necessary for the wellness of players, and also for the sustained engagement and retention of players in the system. To this end, in this work we present a deep learning model that helps identify players who are on the verge of displaying irresponsible or addictive game play in a fully unsupervised manner. We use a combination of long short-term memory and adversarial auto-encoder networks to analyze game play along three tell-tale dimensions of immoderation, namely, money, time and despair. Our model provides a state of the art solution for identifying a precise set of problem gamers in skill-based cash games well ahead of time, effectively addressing the challenges of (i) extreme class imbalance, (ii) sparse and incomplete ground truth, (iii) overlapping behavioral patterns between risky and non-risky but highly engaged players.