The danger of losing funds on an investment or business enterprise is referred to as financial risk. Credit risk, liquidity risk, and operational risk are some of the most prevalent and different financial hazards. Financial risk control is an organizational activity that seeks to detect, monitor, and manage exposure to different risks associated with the usage of financial services. Conventional accounting risk control has relatively great timeliness and unpredictability; therefore, traditional accounting risk identification has numerous difficulties, such as high error rate, low precision, lengthy time, and risk. As a result, this research provides a unique financial risk control strategy in the Internet environment based on big data. First, the decision tree algorithm is used to classify the users who can face financial risks. Simultaneously, the user’s personal credit information is established and represented mathematically, and the 5C mathematical model is obtained. In this way, the user’s credit degree can be judged more accurately, and the user’s credit rating can be obtained, allowing quantitative analysis of users to be realized. Second, construct and weight the financial risk system index of the Internet in accordance with the existing financial system. Finally, the Internet financial risk control model based on big data is built, and the financial risk early warning threshold range is determined to achieve Internet financial risk control. The experimental findings reveal that the method’s risk identification error rate is minimal, accuracy is high, and control time is quick. It can match the actual risk level, which shows that the control effect of this method is good.

Taking the data of the sixteen prefecture-level cities in Shandong Province as an example, this paper attempts to construct regional credit environment indicators and quantify these indicators. The Analytic Hierarchy Process (AHP) method and Fuzzy Comprehensive Evaluation (FCE) have been used to empower the indicators. Meanwhile, this paper has obtained the AHP score through calculation. The corresponding credit ranking has been given using the factor analysis. The research result shows that the credit environment evaluation scores of Shandong eastern coastal cities centered on Qingdao and neighboring cities centered on provincial capital Jinan are often higher than those of inland cities such as southwestern Shandong and northwestern Shandong. The construction of regional credit environment should be based on economy, culture and government. Three aspects go hand in hand. On this basis, this paper puts forward the feasible countermeasures and suggestions for constructing regional credit environment from the perspective of policy.

As the world’s largest importer, trading of iron ore occupies a pivotal position in China’s international trade. In order to seek the decision power of deciding the price for iron ore, China’s Dalian Commodity Exchange (DCE) listed iron ore futures in October 2013,which has become the world’s largest iron ore financial derivatives trading market now. Based on VECM and state-space perspective, this paper aims to explore the price discovery function of iron ore futures on the DCE. Comprehensive analysis from the views of long-term equilibrium relationship, short-term information shocks and dynamic contribution share are made in this paper. The empirical results show that: firstly, from the perspective of cointegration test, there is a long-term equilibrium relationship between the futures prices in DCE and the spot prices; secondly, when facing with short-term information shocks, iron ore futures in DCE have an obviously price discovery function by the analysis of impulse response and variance decomposition; finally, by the way of state-space and Kalman filter algorithm, the long-term equilibrium relationship dynamic contribution for price discovery function of DCE's iron ore futures remains stable between 60% and 70% now.