Background and aims: Gambling disorder is characterized by problematic gambling behavior that causes significant problems and distress. This study aimed to develop and validate a predictive model for screening online problemgamblers based on players? account data. Methods: Two random samples of French online gamblers in skill-based (poker, horse race betting and sports betting, n 5 8,172) and pure chance games (scratch games and lotteries, n 5 5,404) answered an online survey and gambling tracking data were retrospectively collected for the participants. The survey included age and gender, gambling habits, and the Problem Gambling Severity Index (PGSI). We used machine learning algorithms to predict the PGSI categories with gambling tracking data. We internally validated the prediction models in a leave-out sample. Results: When predicting gambling problems binary based on each PGSI threshold (1 for low-risk gambling, 5 for moderate-risk gambling and 8 for problem gambling), the predictive performances were good for the model for skill-based games (AUROCs from 0.72 to 0.82), but moderate for the model for pure chance games (AUROCs from 0.63 to 0.76, with wide confidence intervals) due to the lower frequency of problem gambling in this sample. When predicting the four PGSI categories altogether, performances were good for identifying extreme categories (non-problem and problem gamblers) but poorer for intermediate categories (low-risk and moderate-risk gamblers), whatever the type of game. Conclusions: We developed an algorithm for screening online problem gamblers, excluding online casino gamblers, that could enable the setting of prevention measures for the most vulnerable gamblers.  Kulcsszavak: gambling, prediction model, machine learning, problem gambling, online gambling