Sam Andersson: From Risk Classification to Evolving Risk States: Predictive Analytics and Temporal Modeling with Player Account Data
Tid: On 2025-01-22 kl 15.15 - 16.00
Plats: Cramér room, Campus Albano, House 1, floor 3
Medverkande: Sam Andersson (KI)
Abstract
This seminar presents two complementary approaches for characterizing gambling-related risk using player account data.
In Study 1, we develop a supervised learning pipeline to classify players into “low-risk” and “high-risk” groups. Emphasizing both predictive stability and chronological integrity, the pipeline integrates SHAP (SHapley Additive exPlanations) and Generalized Matrix Learning Vector Quantization (GMLVQ) for feature selection, and employs XGBoost (Gradient Boosting) within a nested forward-chaining cross-validation scheme to prevent information leakage. Hyperparameter optimization is conducted via Bayesian search strategy, and a temporal ablation study tests the pipeline’s stability under varying degrees of historical data availability.
In Study 2, we shift from static classification to modeling the evolution of gambling behaviors. We combine a Temporal Convolutional Network (TCN)-based Variational Autoencoder for feature extraction from sequences of player account activity with a Dynamic Bayesian Network to infer hidden “risk states” that offer a data-driven alternative to traditional definitions of problem gambling. In addition to the latent representations learned through the TCN-VAE, we incorporate select observable features—overlapping with those from Study 1—to capture critical behavioral signals. By merging both latent and observable dimensions, we hope to provide a more nuanced depiction of player risk trajectories than standard rule-based methods.
The overall aim is to demonstrate how predictive models and temporal representations can leverage player account data to advance the identification and understanding of gambling risks and the dynamics of problematic play.