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Insurance is one of the effective means of risk management． With the development of human society， risks are also diversified， new and universal． In order to achieve accurate risk control and better play the functions of loss compensation and risk transfer， insurance needs to deeply integrate with the new quality productivity represented by big data．
The social security system emphasizes people-oriented， and the core of service is the needs of the people． Focusing on the practical needs of the people， hitting the social pain points is the goal of the digital transformation and upgrading of the social security system． Social security institutions take big data as a technical means to build a technology center， a business center， an AI center， and a data center， and use data empowerment to run through the whole process of social security from the aspects of optimizing payment channels， expanding social security types， and simple insurance methods， to effectively promote the refined transformation and upgrading of social security services．
The country are making experiments constantly to explore the integration of big data and social insurance through pilots． In 2021， Jiangxi Province built “ silent authentication” based on big data and biometric identification， and 2.58 million elderly residents in the province enjoyed the “ no perception， no disturb ” authentication service． The “ big data accurate positioning ＋ biological face recognition” technology enables the input object to be automatically authenticated without cooperating with the action， which makes the masses in the certification cycle free from the inconvenience brought by the traditional authentication method，and greatly facilitates the elderly， sick， and disabled groups．
The emergence of big data has broken the temporal and spatial barriers of the operation of the social security system in the past， and the social security model based on big data is more diversified， while the social security resources that residents can enjoy are also tending to be inclusive． With the blessing of the data platform， social security has shown new characteristics of multi-level， multi-functional， cross-regional and cross-platform， and the efficiency of the social security system has been greatly improved．
After the launch of the big data platform， residents can understand the whole process of social security relationship transfer and follow-up through apps such as“ Palm 12333” or Alipay with one click， which greatly saves the procedure cost and promotes the flow of labor．Qingdao， Shandong Province， based on big data analysis， to create “ industrial injury insurance intelligent integrated service”， can achieve remote identification of industrial injury，intelligent remote identification， occupational disease or industrial injury remote synchronous identification， to achieve cross-regional， efficient social security services， to solve the past industrial injury identification， evidence difficulties， slow compensation process and a series of problems， comprehensively improve the efficiency of social security operation．
Big data can not only help insurance companies accurately identify insurance needs， but also stimulate customers' potential insurance needs． Due to the costs， manpower， return and other problems， traditional commercial insurance companies often ignore the sick group， the elderly group， the sinking market， and the group in demand is difficult to effectively participate in insurance． Under the power of big data， insurance companies can use big data to accurately depict customers， accurately locate customer needs， and carry out personalized insurance customization services． Customers can realize flexible insurance on the Internet platform according to their own insurance needs and economic conditions， which not only overcomes the high cost of traditional insurance services， but also promotes the insurance awareness of the masses to a certain extent， improves the availability of insurance， and then improves the insurance coverage rate and enriches the insurance protection level．
Big data can stimulate insurance companies to optimize and innovate insurance product systems． For example， in the field of agriculture， the use of big data platform to establish a fine prediction model， break through the past crop diseases， disaster due to irregular damage， livestock growth cycle uncertainty and other problems， overcome the problem of rapid disaster claims， through insurance technology to expand the types of insured risks， with the wide coverage of insurance to help rural revitalization． In terms of coping with climate change， a large number of property insurance companies， based on big data actuarial models， use the Internet of Things， artificial intelligence， remote sensing technology， etc．， to accurately monitor abnormal areas of climate change， predict extreme weather in specific areas， and introduce preventive measures to effectively prevent climate change risks． The transformation goal of “loss compensation” to “risk reduction” under the background of big data has been realized．
The traditional insurance business process involves marketing， insure， underwriting， approve， loss assessment， claims and other processes． Due to the uneven quality of agents， obscure insurance contract terms， difficult to understand the real situation of the insured and other factors， the traditional insurance business has disadvantages such as high costs， long time， adverse selection and serious insurance fraud．
With the wide application of big data， various insurance companies are accelerating their own digital transformation． App has increasingly become an important channel for insurance companies to form a consumption relationship with users and enhance user stickiness． Insurance companies use big data to analyze customer characteristics， habits and preferences， analyze and predict customer needs， and lay an important foundation for finding target customers and launching personalized products． Insurance companies based on big data technology can greatly save labor costs， skip the agent and customer conversion level to realize the connection between insurance companies and target objects， and achieve the maximum benefit at the minimum cost． At the same time， the mutual benefit between users and insurance companies can be promoted through big data． Users can understand the terms of the insurance contract with one click through the app； Insurance companies can make judgments on users' real information based on big data， credit ratings， machine learning and other means． Big data connects users with insurance companies， solves the problems of insurance fraud and adverse selection in the past， and promotes the stable development of the insurance market．
At present， big data has been used in exploration and loss determination， precision marketing， differentiated pricing， dynamic premium determination and so on． For example， Clover Health， an emerging health insurance company in the United States， has set up a special data lab to integrate and form a clinical database by utilizing big data such as patients' personal data， diagnosis and treatment data and health data to conduct in-depth analysis on the data of disease testing， treatment and medication during treatment． This method is applied to health insurance． Through the basic data of patients， such as age and gender， disease data， pathological data， etc．， a multidimensional mathematical model is established to distinguish different disease risks of individuals， intervene users' behavior through risk coefficient， design products through risk analysis， and provide different insurance services to patients accordingly． Provide technical support for the promotion of differentiated pricing． In addition， the application scenario of big data also appears in the rapid claims and anti-fraud， with the help of image recognition technology， the photos uploaded by the insured and other proof materials are compared in a huge database， and then combined with the cultivated credit history to judge， make accurate judgments through these factors in a short time， and complete the claim review online． To a certain extent， it helps insurance companies reduce the behavior and probability of insurance fraud， and greatly reduces the underwriting risk of insurance companies．