Data-Warehouse-Enhanced Machine Learning Framework for Multi-Perspective Fraud Detection in Multi-Stakeholder E-Commerce Transactions

Abstract

E-commerce fraud has grown increasingly complex due to the involvement of multiple stakeholders—buyers, sellers, logistics providers, and payment gateways—leading to sophisticated cross-entity fraud patterns that traditional detection systems struggle to identify. While modern machine-learning techniques offer improved predictive capabilities, their effectiveness is often limited by fragmented, siloed datasets that fail to capture multi-perspective behavioural signals. This paper proposes a Data-Warehouse-Enhanced Machine Learning Framework that consolidates heterogeneous stakeholder data into a unified analytical environment, enabling richer feature engineering and scalable fraud modeling. The framework integrates multiple machine-learning algorithms—Random Forest (RF) for robust supervised classification, Long Short-Term Memory (LSTM) networks for temporal transaction modeling, Graph Neural Networks (GNNs) for capturing relational and cross-stakeholder dependencies, and One-Class SVM for anomaly detection under extreme class imbalance. Experimental evaluations demonstrate that the warehouse-enhanced multi-perspective learning approach significantly improves fraud-classification accuracy, reduces false positives, and enhances temporal and relational pattern discovery compared to non-warehouse and single-perspective baselines. The proposed system provides an effective and scalable foundation for next-generation fraud detection in multi-stakeholder e-commerce ecosystems.