New internet technologies such as the internet of things and big data are playing a significant role in achieving the comprehensive and deep interconnection of all factors of production safety.Risk management has also received sustained attention in all fields and at all levels.However,in the risk management of high-risk profession,risk transfer still has some limitations such as the absence of risk costs and a risk probability model.Starting from the dilemma of risk management,this paper proposes a possible big data solution to the paradox of risk management and explores the applications and case studies of big data in the field of risk management.Industry regulators can serve as the framework builder of big data and the supervisor of data collection and utilization to discover potential risk factors through data modeling and monitoring,and thus avoid related problems from the root.The operational management side presents subjective and objective dimensions of risk management.Subjectively,as a rational economic person,the managers will make rational rather than reasonable judgments based on contradictory risk management methods and profit-seeking decision-making analysis.Objectively,the application of big data will delimit a reasonable threshold range for high-risk operation units so as to balance a reasonable risk premium.The attitude of risk transferers to risks depends on the risk management and pricing.The application of big data will make the field of risk management become indexable and digital to a certain extent,and.make mine value data to meet their comprehensive and personalized needs for reasonable risk transfer.Finally,this paper concludes that establishing the big data system requires clarifying the dominance of regulators,strengthening the participation of managers,and optimizing the supplementary and connective role of transferees.In addition,this paper provides application advices regarding the strategy of establishing the big data system.