Abstract

The rise of online gambling has increased concern around identifying behavioural addiction in digital environments. Current predictive systems offer limited interpretability and justification for individual-level risk assessments as they often operate as black boxes. This study proposes a hybrid framework that combines a popular machine learning model (XGBoost) with a language-based Retrieval-Augmented Generation (RAG) system to address the current challenges. A combination of user-level behavioral and demographic data was used as input for a trained XGBoost classifier and SHAP (SHapley Additive exPlanations) was also applied to find which features contribute the most to addiction after evaluation. These insights were then incorporated into a Large Language Model (LLM)-based RAG pipeline using sentence-transformer embeddings and FAISS vector retrieval to generate individualized text justifications for each user classification. Through label refinement based on SHAP-ranked feature thresholds and targeted model tuning, the system achieved improved generalization and classification stability, resulting in an AUC of 0.87 while preserving clear, human-readable explanations via the RAG pipeline. This approach demonstrates the potential of integrating structured and unstructured AI techniques in addiction research and risk screening to support more accountable and understandable behavioral health interventions.

Keywords: Online gambling, Hybrid framework, XGBoost, RAG system, LLM