Smart Commerce Fraud Detection Using Artificial Intelligence and Machine Learning

Smart commerce platforms increasingly depend on digital identities, online payments, user reviews, and high-volume order processing, creating multiple opportunities for fraudulent activity. Existing rule-based mechanisms are transparent and easy to implement, but they are static, require frequent manual updates, and can generate false positives when fraud behaviour changes. This paper proposes a smart-commerce fraud detection framework that combines transactional, behavioural, contextual, and review-related signals within a unified artificial intelligence and machine-learning pipeline. The proposed framework uses supervised learning models such as Random Forest and XGBoost for labelled transaction and account classification, Isolation Forest for anomaly detection, and an optional natural-language processing layer for suspicious review identification. Behavioural features include login frequency, transaction velocity, device usage, location consistency, order cancellation rate, address changes, payment method, and review activity. The system incorporates class-imbalance handling, feature engineering, real-time risk scoring, and an explainability layer based on feature importance and SHAP/LIME-style local explanations. The design also supports continuous feedback and future graph-based analysis for coordinated fraud networks. Its methodology covers data acquisition, inspection, preprocessing, feature engineering, model training, evaluation, model selection, prediction, explanation, and deployment. A risk score combines supervised transaction-fraud probability, anomaly score, behavioural risk, contextual inconsistency, and review/account risk. Events are assigned low-, medium-, or high-risk states to support normal processing, additional verification, or blocking/manual review. Evaluation is planned using precision, recall, F1-score, ROC-AUC, PR-AUC, confusion matrix, false-positive rate, and inference time. The paper focuses on a reproducible research framework and does not fabricate experimental performance values; numerical results are to be reported after implementation on the final project dataset. The proposed architecture is intended to improve early fraud detection, reduce false positives, strengthen transaction security, and increase trust in smart-commerce platforms.