In recent years, the variety of car insurance models rose increasingly. Including the range of GPS supported contracts that observe the driving behavior of the insured, assisted by GPS locators, and transfer them to the insurance company. By analyzing the data, the insurance companies try to create a profile of the policyholder and to adjust the insurance fee to the respective driving behavior such as speeding, breaking, turn speeds and much more. However, this calculation assumes that people who spend more time in cars are automatically more vulnerable to accidents and small damages. They assume that there is a direct correlation between time spent in the car and the risk of an accident. Here, however, it was forgotten that experience plays a very important role. The more time you spend driving, the more experience you have gained with hazards or problem situations. The handling of the vehicle itself is best learned by experience and thus reduces the chance of parking damage or similar. The aim of the thesis is to verify or disproof the current approach of insurance companies.
To this end, several methods are used to combine multiple perspectives on the topic as possible. In addition to a survey, data is automatically collected by means of web scraping and also manually by means of several random sampling tests. After evaluating the data quality, the results obtained are summarized and evaluated. In addition to statistical evaluations in PSPP, the focus is also on logical or obvious relationships. Finally, all aspects are merged and the underlying assumption was mostly refuted as studies showed that people driving regularly also have the highest percentage of accidents. But this group of drivers also shows the most stable and predictable values while people driving irregularly show much bigger irregularities.
Most surveillants stood up against permanent monitoring of driving habits including all types of test groups.
During the data collection of the thesisit had to be stated that web scapping of RSS Feeds provides very little usable data.