In-Database Feature Extraction to Improve Early Detection of Problematic Online Gambling Behavior

Abstract

This study involves a comprehensive analysis of an anonymized dataset provided by a Swiss online casino that adds to the identification of reliable early indicators for problematic online gambling. Targeting gambling addiction prevention, our objective was to model and evaluate behavioral characteristics that signal early stages of problem gambling. We scrutinized player behaviors against a list of gamblers previously excluded for problematic gambling, using this as our target variable. Our approach combined traditional gambling risk indicators, as outlined in the existing literature, with innovative exploratory feature engineering and feature selection. This involved computing moving aggregates over specific periods to capture nuanced gambling patterns. All features were evaluated by assessing mutual information with the target variable as well as the collinearity of each pairwise combination of features. Based on our data analysis, we found that the total losses in the previous seven days, total deposits in the previous 15 days, total duration played in the previous seven days, stakes (amount bet per game) over the previous seven days, and making a deposit 12 h after a loss (chasing) were the most informative and independent risk indicators. To assess the accuracy of these indicators for early detection of problematic gambling and accordingly for responsible gambling interventions, we combined them in a linear regression model and compared its performance with the casino's currently used model. We found that a binary decision model based on a linear combination of these indicators provided better recall, greater precision, and more timely decisions than the benchmark.