Behavioral patterns in iGaming across territories: Psychiatric and AI-driven insights via the internet of behavior.

Abstract

BackgroundIn the digital era, iGaming has become a rapidly expanding phenomenon that generates complex behavioral patterns and increases the risk of problematic gambling. The integration of technologies such as Artificial Intelligence of Things and the Internet of Behavior enables a shift from reactive to preventive approaches in public health protection.ObjectiveThis study combines clinical practice, AI, machine learning and the Internet of Behavior concept to analyze player behavior patterns and develop machine learning models for early detection of addiction risk through behavioral markers.MethodsBehavioral data were collected from an online game supplier operating through 52 operators in Republika Srpska, Croatia, Romania, Brazil, Somalia, and Mali. Psychiatric expertise and clinical experience were applied to identify harmful behavioral markers, which served as inputs for training MLP neural networks. Models trained per country classified player behavior into recreational, risky, and problematic categories.ResultsThe analysis included 109,418 players across three continents, aggregating 5,135,179,510 online slot game bets. Results revealed significant cross-country variation in risky and problematic gambling, shaped by socio-economic, cultural, and regulatory factors.ConclusionThe integration of AI-driven behavioral analysis with psychiatric insight provides a robust framework for early risk detection and personalized interventions supporting responsible gaming.