This article is a review of research examining behavioural markers that may signal problem gambling. The authors discuss the use of data science and machine learning to analyze behavioural tracking data. Studies have reported a range of behavioural markers of problem gambling. These include both monetary markers (e.g., bet size) and non-monetary markers (e.g., number of days gambled). These behavioural markers may be combined with other factors, such as payment information, to identify high-risk gamblers and predict problem gambling. Recent research has also done more detailed analyses of gambling behaviour within a session to capture loss chasing. The authors provide insights into current research and challenges that need to be overcome to move the field forward.
