Spotting Sneaky Scammers: Malicious Account Detection from a Chinese Financial Platform

With the rapid development of e-commerce, malicious accounts have become a threat, especially in the business field. Therefore, how to efficiently detect malicious accounts has become one an important issue requiring resolution. Present research on malicious detection mainly uses the basic statistics of the original data to build models, and ignores malicious activities in the business field. To address the context-independent nature of business activities and their lack of social structure, this study constructs an online model for detecting malicious accounts in the business field based on a stacking ensemble strategy with BiGRU-Conv1D-Capsule, XGBoost, and LightGBM as individual learners and AdaBoost as a meta-learner. Experimental results show that the stacking ensemble model constructed in this study has better predictive power than the typical shallow learning and baseline deep learning models. In addition to the basic feature, the behavior sequence of users is also applied in the proposed model. Overall, our experiments based on a real dataset from a financial platform show that the TPR, AUC, and F1 of the stacking ensemble model can reach 0.8258, 0.9860, and 0.8922 respectively, which outperforms all baseline models.