CHEMTTL: A Domain-Adaptive Multi-Task Ensemble Framework for Scalable E-Commerce Fraud Detection

Fraud detection in e-commerce remains a challenging task due to class imbalance and the high-dimensional nature of transactional data. This paper proposes CHEMTTL (Comprehensive Hybrid Ensemble with Multi-Task and Transfer Learning), a novel deep learning framework aimed at improving fraud detection performance. By integrating transfer learning for feature extraction, multiple ensemble models (LightGBM, XGBoost, CatBoost, and MLP), an attention-based fusion mechanism, and multi-task outputs, CHEMTTL addresses the challenges of fraud detection. The model combines task-specific losses and domain adaptation through Maximum Mean Discrepancy (MMD), enhancing both predictive accuracy and generalization across domains. Experimental results demonstrate the superiority of CHEMTTL over traditional models, such as RF, GBDT, CNN, and LSTM, in various performance metrics. Ablation studies validate the contribution of each component in enhancing the model’s overall performance.