Research On Financial Early Warning Of Our Property Insurance Companies

Every year, there are a large number of enterprises getting into financial distress all over the world. Financial distress not only causes huge losses to the enterprises, but also causes negative influence on social economy. Thus, it becomes a hot spot of economic problems. At present, the domestic and foreign scholars on the definition of financial distress have yet to agree. It’s generally believed that financial distress should include the following situation: lack of cash flow, insolvency, failure, bankruptcy, and so on. Because the legal system is complete in western country, the study on financial distress usually puts“bankruptcy” as a general definition standard. Domestic scholars general use "Special Treatment" as a standard definition, but it is limited to the listed companies. For the insurance industry in our country, there are no bankrupt or the special processing insurance companies, so the defining standard does not conform to the research of this paper.On the basis of predecessors’ research, and according to the ST company standard of CSRC, the requirements of “Law of Insurance” and “Temporary Administrative Regulation on Insurance”, the definition of “bankruptcy” in “Bankruptcy Law”. In this paper, in reference of financial distress events, defines the concept of financial distress and puts forward a relatively perfect concept. Then, on the analysis of the reason of financial distress, and using the regulators to insurance company at home and abroad for reference,and the same time in line with the six principles of practicality, integrity, standardization,easily accessible, timeliness and independence, the article builds index system of our property insurance companies’ financial distress. Then, uses the factor analysis evaluation dimension as BP neural network’s input factors; at the same time, uses the comprehensive score of each insurance company’s financial condition as a financial distress quantitative definition. Then, this paper uses BP neural network model to build early warning system,and validated, found that the model prediction is accurate. Finally, the article proposes suggestions for going out of financial distress and the shortage in the article.