ArticleJournal of gambling studies2023
Using artificial intelligence algorithms to predict self-reported problem gambling with account-based player data in an online casino setting.
Article in Journal of gambling studies, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Learning the Boundary Between Involvement and Severity: A Multi-Target Machine-Learning Analysis of Emotion Dysregulation, Impulsivity, Gambling Involvement, and Problem Severity in Psychiatric Outpatients.Clinical neuropsychiatry · 2026Article
- Professionals' Perspectives On the Role of Advanced Technologies in Responsible Gambling.Journal of gambling studies · 2026Article
- Predicting problem gambling among online sports and race bettors: Assessing the value of machine learning using behavioural and self-reported data.Journal of behavioral addictions · 2026Article
- The Need for Benchmarks to Advance AI-Enabled Player Risk Detection in Gambling.Journal of gambling studies · 2026Article
- Integration of artificial intelligence-based solutions into electronic gaming machines for responsible gambling: a case study of South Africa.Frontiers in artificial intelligence · 2026Article
- Money and mental health: a scoping review of financial variables, data sources, and analytical methods.Frontiers in public health · 2026Article
- A scoping review of routinely collected linked data in research on gambling harm.NPJ digital medicine · 2025Article
- Use of artificial intelligence within the gambling field: a scoping review protocol.F1000Research · 2025Article
- Overtime: Long-Term Betting Trajectories Among Highly-Involved and Less-Involved Online Sports Bettors.Journal of gambling studies · 2024Article
- Behavioural Tracking and Profiling Studies Involving Objective Data Derived from Online Operators: A Review of the Evidence.Journal of gambling studies · 2024Review
- Self-reported Deposits Versus Actual Deposits in Online Gambling: An Empirical Study.Journal of gambling studies · 2024Article
- Individual risk factors and prediction of gambling disorder in online sports bettors - the longitudinal RIGAB study.Frontiers in psychiatry · 2024Article
- Characteristics and prediction of risky gambling behaviour study: A study protocol.International journal of methods in psychiatric research · 2023Article
- Gambling harm prevention and harm reduction in online environments: a call for action.Harm reduction journal · 2023Review
- Development and validation of a prediction model for online gambling problems based on players' account data.Journal of behavioral addictions · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.