? The high threshold of the risk control system limits the expansion of the membership base.
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? 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.
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? It is necessary to strengthen risk control measures during promotional activities such as Double Eleven to protect consumer rights and interests.
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? 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.
Especially during major promotional events such as Double Eleven, risk control measures are strengthened to protect consumer rights and interests.
By sharing risk control results with risk control BPs, industry information sharing and cooperation are promoted.
The automated operation model provides a reliable risk prevention and control guarantee for business development.
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