Predicting High-Risk Gambling Based on the First Seven Days of Gambling Activity After Registration Using Account-Based Tracking Data

Bibliographic Data

ID15440357
AuthorsMichael Auer (0000-0002-6303-596X), Mark D Griffiths (0000-0001-8880-6524, Nottingham Trent University, corresponding author)
Year2023
Volume22
Issue6
Pages3397-3413
Publication date2023-05-02
Open AccessYes
TypeARTICLE
VenueInternational Journal of Mental Health and Addiction (JOURNAL)
Journal identifiersISSN: 1557-1874 • E-ISSN: 1557-1882
PublisherSpringer Science+Business Media (PUBLISHER • DE)
DOI10.1007/s11469-023-01056-4
OpenAlexW4367692423
LanguageEN
Citations received3
References cited28

In recent years, several European regulators have introduced mandatory player tracking to identify potentially problematic online gambling. The present study’s aim was to investigate the possibility of predicting future high-risk gambling based on a short time window (i.e., the first seven days after the registration for an online gambling site). The authors were given access to a secondary dataset comprising 37,986 gamblers who registered at a European online gambling operator between January 1 and April 30, 2022. The study examined the association between gambling behavior during the first week after registration and high-risk gambling during the first 90 days after registration. A logistic regression model with high-risk gamblers (based on the first three months of gambling data after initial registration) as the dependent variable and age, gender, and the first week’s gambling behavior as independent variables explained 40% of the variance. Age, gender, and seven player tracking features from the first week after registration were significant. Machine learning models confirmed the high correlation between the first week of gambling and a high-risk classification during the first three months after registration. The most important features reported by a Random Forest and a Gradient Boost Machine model were the total amount of money deposited, the number of deposits, the amount of money lost, and the average number of deposits per session. The study showed that high-risk gambling during the first three months of a player’s lifetime can be predicted very early after registration. These findings suggest that gambling operators should initiate preventive measures (such as limit setting, mandatory play-breaks, personalized messaging) and monitor gambling behavior at a very early stage after a gambler’s initial registration

Health psychology · Logistic regression · Machine learning · Public health · Random forest · Tracking (education · Variance (accounting · Computer Science · Demography · Gambling Behavior and Treatments · Medicine · Psychology · Artificial Intelligence

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