Topological Data Model’s Application In Measuring Operational Risk Of Motor Vehicle Insurance

Historical data shows that all of the property company’s bankruptcy and business failures are related to mismanagemen, operational risk has become the greatest threat to the property insurance company solvency. However, due to the lack of historical data and imperfect models, the current management of operation risk in the insurance company is still in recognition stage. Meanwhile, the operating conditions of auto insurance directly affects of the development of the company and even survival.Based on the above background, to promote the stable development of auto insurance market and even the entire property insurance market, there are important theoretical and practical significance in the research on operational risk existing in management motor vehicle insurance business.Based on the the rapid development of motor vehicle insurance business in recent years as the background. The starting point of the article is by analysising the sources of operational risk of auto insurance business in the theory. This paper focuses on using of topological data model from a qualitative point of view, assessing the form of operational risk in the core business processes of auto insurance, with the expert estimation and Easyfit software from the quantitative point of view of measuring their initial loss of strength, loss probability and process efficiency. The ultimate goal is to obtain the total loss distribution of operational risk in the year and allocation regulatory capital for it. Measurement results show following conclusions:first, the topological data model can indeed well documented operational risk loss events; second, the core business processing systems of auto insurance insurance are still relatively sound, but there are serious problems in the convergences between processes; furthermore, the core business of auto insurance operational risk events caused by high frequency, but the loss of intensity distribution has a strong fat tail, therefore, the use of Monte Carlo simulation of operational risk derived fat tail distribution of total losses is not obvious, so the amount of configuration capital required is small for preventing unexpected loss. Finally, the author gives the relevant policy recommendations for management operational risk of motor vehicle insurance that property insurance companies should improve the auto insurance processes; strengthen risk management capabilities and establish an industry information sharing mechanism.