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

Abstract

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.

Individual risk factors and prediction of gambling disorder in online sports bettors - the longitudinal RIGAB study

“…Sociodemographic characteristics in our sample were quite similar to those in other studies ( 41 , 63 ) and the prevalence of GD in our initial sample (23%) was comparable to other studies combining player tracking data and GD measures (18 - 27%, 27 – 29 ), suggesting a representative sample. In addition, we relied on self-report data, which is subject to recall bias.…”

Identifying Risks and Ethical Considerations of AI in Gambling: A Scoping Review

“…After screening the titles and abstracts, we deemed 44 records as potentially eligible for inclusion. From these, we excluded 26 for not meeting the eligibility criteria: 17 were not sufficiently related to the gambling field (e.g., Kim & Werbach, 2016;Uusitalo et al, 2021), 7 lacked a discussion of risks and/or ethical concerns (e.g., Auer & Griffiths, 2023;McAuliffe et al, 2022), and 4 were unidentified duplicates. We selected 16 studies to include in the final review.…”

“…Player data and AI support a variety of commercial use-cases including recommendation systems, fraud detection, and customer relationship marketing (Auer & Griffiths, 2023;Chui et al, 2018). They also assist stakeholders to curb the potential negative impacts of gambling (Ghaharian et al, 2022).…”

Using machine learning to retrospectively predict self‐reported gambling problems in Quebec

“…Two recent studies [28, 29] moved toward rectifying these problems, recruiting large samples of online gamblers and asking them to complete the Problem Gambling Severity Index (PGSI) [30]. Both studies found that machine learning models yielded good classification performance when predicting PGSI scores from online gambling behaviours (area under the receiver operating characteristic curve [AUCs] ≈ 73%; see Model performance assessment below).…”