Predicting self-exclusion among online gamblers: An empirical real-world study

Abstract

AbstractProtecting gamblers from problematic gambling behavior is a major concern for clinicians, researchers, and gambling regulators. Most gambling operators offer a range of so-called responsible gambling tools to help players better understand and control their gambling behavior. One such tool is voluntary self-exclusion, which allows players to block themselves from gambling for a self-selected period. Using player tracking data from three online gambling platforms operating across six countries, this study empirically investigated the factors that led players to self-exclude. Specifically, the study tested (i) which behavioral features led to future self-exclusion, and (ii) whether monetary gambling intensity features (i.e., amount of stakes, losses, and deposits) additionally improved the prediction. A total of 25,720 online gamblers (13% female; mean age = 39.9 years) were analyzed, of whom 414 (1.61%) had a future self-exclusion. Results showed that higher odds of future self-exclusion across countries was associated with a (i) higher number of previous voluntary limit changes and self-exclusions, (ii) higher number of different payment methods for deposits, (iii) higher average number of deposits per session, and (iv) higher number of different types of games played. In five out of six countries, none of the monetary gambling intensity features appeared to affect the odds of future self-exclusion given the inclusion of the aforementioned behavioral variables. Finally, the study examined whether the identified behavioral variables could be used by machine learning algorithms to predict future self-exclusions and generalize to gambling populations of other countries and operators. Overall, machine learning algorithms were able to generalize to other countries in predicting future self-exclusions.