Using “Markers of Harm” to Track Risky Gambling in Two Cohorts of Online Sports Bettors

Assessing the Risk of Online Gambling Products: A Replication and Validation of Behavioural Markers of Harm Using the Problem Gambling Severity Index

In this paper, we investigate the validity of objective operator data as proxy indicators for riskier gambling as based upon an independent self-report measure. Such work is important to strengthen the validity of gambling research involving objective behavioral indicators of harm used to detect higher risk gambling or product choices. To address these aims, a total of 21,464 individual customers from a single international operator completed the Problem Gambling Severity Index (PGSI). These data were then mapped to measures of player gambling activity and a range of objective behavioural markers of harm. The results confirmed that people scoring 8 + on the PGSI were found to have higher levels of gambling involvement (participation, days active and expenditure) on a range of gambling products, with differences generally larger for casino than wagering activities. Importantly, this group was also more likely to have a higher incidence of behavioural markers of harm (e.g., declined deposits). The data allowed for the replication of a previous study using such markers to detect differences in product risk, but further validated their use in a variety of analytical contexts by showing a concordance between self-reported and objective risk measures.

Overtime: Long-Term Betting Trajectories Among Highly-Involved and Less-Involved Online Sports Bettors

Online sports gambling involvement is discontinuous in nature, with small groups of highly involved gamblers exhibiting betting behavior that is distinctly greater than other gamblers. There is some question about whether these groups, defined by exceedingly high levels of play, also have equivalently high rates of gambling problems, and whether they maintain these play levels over time. The current study builds on past work by examining the long-term trajectories of play and voluntary self-exclusion patterns across two years among a cohort of 32,262 highly-involved and less-involved online sports gamblers. We also examine the relative importance of betting behavior change as a risk factor for gambling problems by testing whether high involvement as compared to escalation of involvement is a better predictor of future self-exclusion. Measures included betting activities, transactional activities, and self-exclusion activities on a European online betting platform between February 2015 and January 2017. Results showed that bettors who were most highly involved in the first 8 months of the study in terms of number of bets and net loss were more likely to continue gambling on the platform in months 9-24 than others. Bettors who were most highly involved in the first 8 months of the study in terms of net loss and amount wagered were more likely to use self-exclusion than others, and more likely to have multiple self-exclusions. Escalations in frequency of play and average bet size within the first 8 months emerged as significant predictors of self-exclusion, even when controlling for high involvement.

Behavioural Tracking and Profiling Studies Involving Objective Data Derived from Online Operators: A Review of the Evidence

Studies involving the analysis of objective data from online operators attempt to address common concerns about biases in self-report research. This paper surveys the progress in this area of research over the last 15 years. The findings highlight many areas of achievement, including: the development of a set of behavioural markers that reliably differentiate variations in gambler risk. Online gamblers can be grouped into clusters based on the intensity and frequency of gambling; behavioural variability; or, signs of over-commitment (e.g., deposit frequency or expenditure patterns). Behavioural indicators have also been successfully used to predict proxies of harm such as self-exclusion or account closures. However, relatively few studies have combined objective data with self-report data to achieve independent validation of the risk-status of gamblers. Evidence also supports the potential value of short-term responsible gambling interventions involving the use of voluntary and mandatory limits, messages and behavioural feedback. Less work has, on the other hand, addressed the comparative risk of different online gambling products. The findings suggest the need for further validation of findings against independent measures of gambling risk; consistent definitions of indicators; a greater focus on the differentiation of product risk; and, on the long-term impact of RG interventions.

Using artificial intelligence algorithms to predict self-reported problem gambling with account-based player data in an online casino setting

In recent years researchers have emphasized the importance of artificial intelligence (AI) algorithms as a tool to detect problem gambling online. AI algorithms require a training dataset to learn the patterns of a prespecified group. Problem gambling screens are one method for the collection of the necessary input data to train AI algorithms. The present study's main aim was to identify the most significant behavioral patterns which predict self-reported problem gambling. In order to fulfil the aim, the study analyzed data from a sample of real-world online casino players and matched their self-report (subjective) responses concerning problem gambling with the participants' actual (objective) gambling behavior. More specifically, the authors were given access to the raw data of 1,287 players from a European online gambling casino who answered questions on the Problem Gambling Severity Index (PGSI) between September 2021 and February 2022. Random forest and gradient boost machine algorithms were trained to predict self-reported problem gambling based on the independent variables (e.g., wagering, depositing, gambling frequency). The random forest model predicted self-reported problem gambling better than gradient boost. Moreover, problem gamblers showed a distinct pattern with respect to their gambling based on the player tracking data. More specifically, problem gamblers lost more money per gambling day, lost more money per gambling session, and deposited money more frequently per gambling session. Problem gamblers also tended to deplete their gambling accounts more frequently compared to non-problem gamblers. A subgroup of problem gamblers identified as being at greater harm (based on their response to PGSI items) showed even higher values with respect to the aforementioned gambling behaviors. The study showed that self-reported problem gambling can be predicted by AI algorithms with high accuracy based on player tracking data.