Article
E-Commerce Fraud Detection
The rapid expansion of digital payment ecosystems has intensified the threat of e-commerce fraud, demanding detection systems that are both accurate and interpretable. This paper presents FraudGuard, an intelligent web-based fraud detection application built using the Flask framework. FraudGuard employs a hybrid decision mechanism that integrates rule-based heuristic scoring with machine learning inference from pre-trained ensemble models including XGBoost and a stacking classifier. Transaction risk is assessed across multiple dimensions: transaction amount thresholds, time-of-day windows, geodesic distance between consecutive transaction locations, and international or high-risk location indicators. Raw transaction attributes are transformed into a structured feature vector encompassing log-transformed amounts, temporal flags, and distance-derived indicators. The rule-based engine generates an initial fraud score along with human-readable explanatory reasons, while the machine learning component augments this score with probabilistic estimates from ensemble models. A unified fraud score bounded between 0 and 100 determines a binary fraud classification using a configurable threshold. User authentication is secured via SHA-256 password hashing and session management. Experimental evaluations on representative test scenarios confirm that the hybrid engine consistently assigns elevated scores to suspicious transactions and low scores to legitimate ones. FraudGuard bridges the gap between offline machine learning experimentation and interactive web deployment, serving as both a practical prototype for fraud analysts and an educational reference for integrating ML models into production Flask applications
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