<![CDATA[The rapid growth of e-commerce increases the exposure to fraudulent sellers, while reactive reporting mechanisms generally operate only after consumers suffer losses. This study develops a proactive fraud-risk screening model to assist in identifying potential fraud-risk indications among Shopee sellers from store-profile metadata and to explain the model's decisions. Primary data were collected from Indonesian Shopee seller profiles using a manual-hybrid approach with Playwright. The initial dataset contained 460 profiles and was reduced to 457 clean records after duplicate removal. The final dataset comprised 355 Safe stores and 102 Fraud stores (labeled heuristically using external indicators) with 11 predictor features. XGBoost was trained with class weighting through a scale_pos_weight value of 3.48 and an 80:20 stratified split. Evaluation on 92 test records produced 95.65% Accuracy, 84.00% Precision, 100.00% Recall, and 91.30% F1-Score. SHAP analysis identified historical_sold (48.42%), store age (18.73%), and rating_star (14.76%) as the three most influential features. The trained model was subsequently integrated into a Chrome Extension prototype to provide predictions and SHAP-based rationales. The results indicate that a seller-centric metadata approach can support early fraud-risk detection; however, the 84.00% precision confirms that the output should be interpreted as a risk screening indication rather than an absolute determination of fraud, assisting consumers in making safer decisions.]]>
