The Analysis Of Vehicle Navigation Data And Its Application In Automobile Insurance Industry

With the continuous development of information science and technology, more and more devices can generate data, and hardware storage devices are getting cheaper, therefore we entered the era of explosive data growth. Big data have sprung up in all walks of life, many industries have begun to analyze big data and get amazing value from the analysis, such as the Internet industry, large retail supermarkets. Although a large part of the industries ushered in big data, they simply store the data, and did not exploit the values of the data. If big data could be properly used, it will be able to expand the competitive advantage of a company. In contrast, it will take risk and gradually fall behind in the competition.Recently, the rapid development of China's automobile market is leading to the booming of car navigation software and While the use of in-vehicle navigation software is gaining its popularity,big data begins to be used in car navigation. Big data will bring an opportunity, but also a challenge. How to get the value from the navigation data becomes problems for the car navigation software companies. Meanwhile, with the expansion of China's auto market, the competition of the Chinese car insurance market has become increasingly fierce. The competition of auto insurance mainly focuses on service and price, and essentially on the competition of risk assessment capability when the auto insurance pricing strategy widely used today is difficult to assess the real risk of the policy-holders.This paper combines the situation of the risk assessment in auto insurance field with the characteristics of car navigation big data, and by analysis of car navigation big data, it assesses the users'statistical driving situation, the results of which is referred to as the driving statistical coefficient of safety. The driving coefficient comprehensively considers the user's driving speed, area, time, distance, etc., which practically reflects the users'driving behavior and habits. Comparing with China's current premium rate used by insurance companies, the coefficient is closer to users'real driving risk. The auto insurance companies can regard it as a major premium rate pricing factor or as a minor premium rate factor in premium rate adjustment, and they can also combine the rate coefficient with traditional factors to improve and innovate the varieties of auto insurance services and pricing strategies.This paper designs the method of obtaining driving statistical safety coefficient, which includes the selection of assessment index and weight. In addition, this paper designs how to get those index data from big data and tries to obtain the coefficient in accordance with the method of design mentioned above through examples of the real data provided by a certain in-vehicle navigation software company.