Trustworthy semi‐supervised anomaly detection for online‐to‐offline logistics business in merchant identification

Abstract

AbstractThe rise of online‐to‐offline (O2O) e‐commerce business has brought tremendous opportunities to the logistics industry. In the online‐to‐offline logistics business, it is essential to detect anomaly merchants with fraudulent shipping behaviours, such as sending other merchants' packages for profit with their low discounts. This can help reduce the financial losses of platforms and ensure a healthy environment. Existing anomaly detection studies have mainly focused on online fraud behaviour detection, such as fraudulent purchase and comment behaviours in e‐commerce. However, these methods are not suitable for anomaly merchant detection in logistics due to the more complex online and offline operation of package‐sending behaviours and the interpretable requirements of offline deployment in logistics. MultiDet, a semi‐supervised multi‐view fusion‐based Anomaly Detection framework in online‐to‐offline logistics is proposed, which consists of a basic version SemiDet and an attention‐enhanced multi‐view fusion model. In SemiDet, pair‐wise data augmentation is first conducted to promote model robustness and address the challenge of limited labelled anomaly instances. Then, SemiDet calculates the anomaly scoring of each merchant with an auto‐encoder framework. Considering the multi‐relationships among logistics merchants, a multi‐view attention fusion‐based anomaly detection network is further designed to capture merchants' mutual influences and improve the anomaly merchant detection performance. A post‐hoc perturbation‐based interpretation model is designed to output the importance of different views and ensure the trustworthiness of end‐to‐end anomaly detection. The framework based on an eight‐month real‐world dataset collected from one of the largest logistics platforms in China is evaluated, involving 6128 merchants and 16 million historical order consignor records in Beijing. Experimental results show that the proposed model outperforms other baselines in both AUC‐ROC and AUC‐PR metrics.