Adaptive Merchant-Centric Risk Control via Unbiased Decision-Making and Dynamic Optimization in E-Commerce

Abstract

In the domain of merchant-oriented risk control decisions within e-commerce, balancing the effectiveness of risk management with merchant satisfaction remains a critical challenge. Strict risk control strategies, while effectively mitigating risks, often lead to increased merchant dissatisfaction. Conversely, loose policies could enhance the merchant experience but raise the likelihood of incidents, potentially incurring substantial financial losses. Additionally, determining personalized risk control strategies for different merchants to achieve optimal overall risk management effectiveness is crucial. Given the high uncertainty in the outcomes of different risk control decisions, manual strategy allocation and real-time adjustments are commonly implemented in practice, leading to significant human and resource costs. In this work, we present a novel automated risk control decision framework that utilizes unbiased data-driven decision-making and dynamic optimization to automate the allocation and adjustment of risk control strategies. Our proposed solution adapts to various online business requirements, demonstrating exceptional risk management performance and significantly reducing overall costs. This approach has been extensively deployed and validated in Alibaba's risk control operations, achieving large-scale automated risk control decisions.