Generative AI-based approach for player behavior analysis and gray area identification

Background: Detecting exploitative or unethical player behavior in online gaming platforms is challenging due to ambiguous gray-area actions that are neither clearly legitimate nor illegal. Methods: explainability. Datasets from a massively multiplayer online role-playing game (MMORPG) spanning 88 days (49,739 player sessions) were augmented to address class imbalance. Anomaly detection using an Encoder-Decoder GAN for Anomaly Detection (EGBAD) approach generated anomaly-aware features. A stacked ensemble model combining Random Forest, XGBoost, and Artificial Neural Networks was developed, with SHAP and LIME providing explanations for predictions. Results: The proposed framework achieved 95.98% accuracy, 0.915 ROC-AUC, and 0.90 macro F1-score, outperforming baseline models. The integration of CTGAN improved minority class recall by 5-7 percentage points, while EGBAD-derived anomaly features enhanced gray-area detection. Human-in-the-loop triage for low-confidence predictions (6.8% of cases) achieved 75% human-AI agreement with reduced false positives (21% decrease) and false negatives (17% decrease). Discussion: The framework successfully balances automated detection with human oversight, providing transparent, interpretable decisions for player behavior moderation while maintaining fairness and reducing wrongful enforcement actions.