
• The high threshold of the risk control system limits the expansion of the membership base.
• There is a need for more accurate identification and differentiation between members and category merchants to protect the rights and interests of consumers and stores.
• It is necessary to strengthen risk control measures during promotional activities such as Double Eleven to protect consumer rights and interests.
• Data-driven risk control model: Develop a member identification model using high-dimensional spatial behavioral features and clustering algorithms (such as DBSCAN, OPTICS, etc.) to achieve accurate classification.
• Model accuracy verification: Ensure the high accuracy and reliability of the risk control model through algorithm engine libraries (such as LOF, MDCA, etc.) and verification techniques.
• Personalized risk management: Apply federated learning technology to customize personalized risk control models for merchants and members, enhancing the adaptability and effectiveness of risk management.
• Automated risk prevention and control: Implement automated interception and risk disposal processes, update risk lists in a timely manner, and effectively prevent and manage potential risks.
The full-scale consumer and member merchant identification model significantly improves the efficiency and accuracy of selection.
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