Identifying Gambling Risk Profiles from Online Behavioral Data: An AI Cluster-Based Empirical Study

• Arthur C. S. Xavier Futuro Tech
• Milton T. M. Junior Futuro Tech
• Hendrik W. C. Garcia FPS
• Adriana F. G. Barreto Futuro Tech
• Tiago A. E. Ferreira UFRPE

DOI: https://doi.org/10.5753/sbcas.2026.21559

Resumo

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.

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