Machine Learning-Based Approach for the Gambling Problem Identification

Watching sports matches is a beloved pastime for every fan, and predicting the results and betting on which team will win adds to the excitement. Unfortunately, for some users, a lack of self-control can turn initially innocent play into a long-term problem. For this reason, problem gambling is among the crucial issues facing modern societies. Bookmaker companies, therefore, put considerable effort into implementing responsible gambling practices; however, identifying at-risk individuals remains a major challenge. Our paper focuses on identifying users who may currently have, or soon develop, potential issues with gambling. To achieve this, we introduce a set of machine learning methods combined with preprocessing tools that allow us to initially acquire and anonymize user data. This anonymized database is then used to identify high-risk groups. We tested our approach on a large dataset that was obtained and preprocessed specifically for this study. The experiments were conducted on actual data and verified by specialists responsible for identifying gambling problems. Using our method, we successfully identified and detected the early signs of potential gambling problems in multiple users.