Identifying Problematic Gamblers using Multiclass and Two-stage Binary Neural Network Approaches

Abstract

Responsible gaming has gained traction in recent years due to the harmful nature of compulsive online gambling and the increased awareness on the unfavourable consequences arising from this type of gambling. In Malta, legislation passed in 2018 places the onus of responsibility on online gaming companies has made studying this problem even more important. The focus of this research paper is to apply multistage and twostage artificial neural networks (ANN), and two-stage Bayesian neural networks (BNN), to the responsible gaming problem by training models that can predict the gambling-risk of a player as a multiclass classification problem. The models are trained using data from gambling session histories provided by a gaming company based in Malta. These models will then be compared using different performance metrics. It is shown that, while all approaches considered have their strengths, multiclass artificial neural networks perform best in terms of overall accuracy while the two-stage Bayesian neural network model performs best in classifying the most important class, the one where the players have a high risk of becoming problematic gamblers, and also second best at classifying the medium risk class.