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The emergence of online gambling has raised concerns about potential gambling-related harm, and various measures have been implemented in order to minimise harm such as identifying and/or predicting potential markers of harm. The present study explored how the nine DSM-5 criteria for gambling disorder can be operationalised in terms of actual online gambling behaviour using account-based gambling tracking data. The authors were given access to an anonymised sample of 982 gamblers registered with an online gambling operator. The data collected for these gamblers consisted of their first three months’ gambling activity. The data points included customer service contacts, number of hours spent gambling, number of active days, deposit amounts and frequency, the number of times a responsible gambling tool (such as deposit limit) were removed by the gamblers themselves, number of cancelled withdrawals, number of third-party requests, number of registered credit cards, and frequency of requesting bonuses through customer service (i.e., the number of instances of ‘bonus begging’). Using these metrics, most of the DSM-5 criteria for gambling disorder can be operationalized (at least to some extent) using actual transaction data. These metrics were then applied to a sample of online gamblers, and through cluster analysis four types of online gambler based on these metrics (non-problem gamblers, at-risk gamblers, financially vulnerable gamblers, and emotionally vulnerable gamblers) were identified. The present study is the first to examine the application of the DSM-5 criteria of gambling disorder to actual gambling behaviour using online gambling transaction data and suggests ways that gambling operators could identify problem gamblers online without the need for self-report diagnostic screening instruments.

Applying the DSM-5 Criteria for Gambling Disorder to Online Gambling Account-Based Tracking Data: An Empirical Study Utilizing Cluster Analysis