Application strategy and value analysis of digitalization in risk prevention and control of power marketing business
TL;DR
This study proposes a digital risk control philosophy featuring " data-driven, process embedding, closed-loop collaboration and intelligent evolution", and constructs an all-chain strategic system of "intelligent identification-embedded interception-closed-loop rectification-platform empowerment".
Abstract
Faced with multiple challenges in the power marketing business, including diversified market entities, varied user demands, complex business processes, and personalized services, the traditional risk prevention and control model is unable to adapt to complex and systemic risks. This study proposes a digital risk control philosophy featuring "data-driven, process embedding, closed-loop collaboration and intelligent evolution", and constructs an all-chain strategic system of "intelligent identification-embedded interception-closed-loop rectification-platform empowerment". Applied to the scenario of abnormal electricity price identification, the Stacking ensemble learning model achieves an AUC of 0.983 and a recall rate of 92.7%. Digital risk control enables shifts from reactive to proactive, experience to data, exhibiting three-tiered value evolution and cross-domain transferability. Enhanced by model fusion and regional adaptation, it boosts practicality and generalization, providing pathways for robust power grid risk systems.