A Deep Tabular Learning Framework for E-Commerce Delivery Delay Prediction and Cost-Sensitive Operational Risk Mitigation

Mitigating delayed delivery risks is a critical imperative for e-commerce platforms to prevent financial losses and optimize customer lifetime value. However, extracting actionable intelligence from heterogeneous tabular logistics data remains a persistent challenge. Therefore, the specific objective of this research is to develop a deep tabular learning framework capable of accurately predicting last-mile delivery delays and translating these predictions into cost-sensitive operational intervention strategies. To achieve this, we propose the Gated Feature Interaction Multilayer Perceptron (GFI-MLP), which leverages dual-branch gated interactions and hidden-channel recalibration to learn complex fulfillment risk representations. Validated on a public e-commerce logistics dataset (Kaggle), GFI-MLP achieves an AUC of 0.8464 and an F1-score of 0.8236, significantly outperforming eight baseline models (including Gradient-Boosting and Tabular Transformer architectures). Bridging algorithmic innovation with operational management, we introduce a dynamic cost-benefit simulation to translate predictive probabilities into quantifiable economic value. Ultimately, GFI-MLP provides a robust, data-driven tool to minimize proactive intervention costs and mitigate operational risks.