Applying Data Science to Behavioral Analysis of Online Gambling

Abstract

Purpose of Review: Gambling operators’ capacity to track gamblers in the online environment may enable identification of those users experiencing gambling harm. This review provides an update on research testing behavioral variables against indicators of disordered gambling. We consider the utility of machine learning algorithms in risk prediction, and challenges to be overcome. Recent Findings: Disordered online gambling is associated with a range of behavioral variables, as well as other predictors including demographic and payment-related information. Machine learning is ideally suited to the task of combining these predictors in risk identification, although current research has yielded mixed success. Recent work enhancing the temporal resolution of behavioral analysis to characterize bet-by-bet changes may identify novel predictors of loss chasing. Summary: Data science has considerable potential to identify high-risk online gambling, informed by principles of behavioral analysis. Identification may enable targeting of interventions to users who are most at risk.