Research On The Credit System Of Construction Industry Under The Background Of Big Data

The building industry has achieved remarkable results in the development of the national economy.In the construction of the credit system,the improvement of the construction industry credit system is crucial to the establishment of the entire social credit system.As important participants in the construction market----the construction enterprises are not honest and trustworthy in qualification acquisition and application,market transaction,production,and fulfillment of social responsibilities.Therefore,how to improve the credit system of the construction industry,improve the untrustworthiness of the construction market,and strengthen the management is of great significance for the construction enterprises to improve their own advantages,establish a corporate brand awareness,and thus create classic heritage products.Firstly,this paper takes 109 construction enterprises from China Association of Construction Enterprise Management who participated in the credit evaluation of construction industry in 2017 as the research object,introduces the current credit situation of construction enterprises on credit organization,financial risk,engineering safety management,and summarizes the current problems in the construction industry credit.Secondly,combined with the credit system indicators of China Association of Construction Enterprise Management,the analysis of the extracted indicators of 109 enterprises shows that the big data is effective in the prediction of construction companies on solvency and market competition.This paper examines the function of big data in the field of future credit risk regulation and risk control in the construction industry.Finally,according to the above elaboration,introduction and argumentation,it puts forward some suggestions for improving the credit system of the construction industry from the aspects of strengthening the supervision,unifying the social credit code,improving the classification and so on.