Optimization Model of Financial Risk Measurement of Biotechnology Companies Based on Big Data Mining

Jie Zhu
Liaoning Finance Vocational College, School of Investment and Insurance, Liaoning 110122, China

Abstract:

With the rapid development of big data technology, biotech companies have ushered in new opportunities in data collection, analysis and decision support. However, the risks these companies face in finance and financial management are also increasingly complex and diverse. BDM (Big data mining) technology can improve management efficiency, effectively reduce financial risks and optimize enterprise structure. However, there are also some cases that are out of step with the introduction of BDM technology because of the conservative enterprise management concept and unreasonable internal structure of enterprises. This topic launches the research on the optimization model of biotechnology corporate financial risk measurement based on BDM and secure neural network. In this paper, all netizens are regarded as "sensors" distributed on the network by enterprises. Training a BPNN (BP neural network) is to take the same series of input examples and ideal outputs as "samples" of training, and train the network sufficiently according to certain training algorithms, so that BPNN can learn the basic principles included in the "solution". The research shows that all three algorithms have the best prediction effect in 2021, and the F values can reach 0.8723, 0.9276 and 0.927 respectively. In the long run, SVM (Support Vector Machines) has the best forecasting effect. The final correction rate of the financial risk early warning model can reach 91.036%. Establish a risk early warning index system and implement BDM, analyze the results of BDM, and issue a risk early warning report, which can provide reference for the decision-making of enterprise managers