Identifying Gambling Risk Profiles from Online Behavioral Data: An AI Cluster-Based Empirical Study - Xavier, Arthur C. S.; M. Junior, Milton T.; Garcia, Hendrik W. C.; Barreto, Adriana F. G.; Ferreira, Tiago A. E. (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), 2026)
This study investigates whether behavioral risk profiles can be identified using clustering techniques applied to real-world player tracking data. Only in 2023, more than 7% of the Brazilian population was classified as high risk for problematic gambling. Behavioral indicators were extracted from the transactional data of more than 11,000 gamblers from a Brazilian online operator and used to train a K-Means clustering model. The resulting clusters were evaluated using a daily monitoring dataset and compared with a group of voluntary self-excluded players. The results show that approximately 89% of these players were classified in the high-risk clusters at least once, indicating that the proposed indicators capture behavioral patterns associated with gambling risk.