Research on Enterprise Financial Risk Early Warning and Control Based on Big Data Analysis
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Abstract
With the growth in complexity and unpredictability of the business environment, early warning techniques of financial risks are faced with the following issues: slowness in recognition and lack of accuracy to process multi-source data, dynamically responsive, and description of nonlinear relationships. In a bid to solve these problems, this paper proposes the concept of big data analytics and machine learning to develop a combined model of financial risk early warning and control. This model manages to use a single data system created through the combination of heterogeneous data that is provided by many sources, a model based on the XGBoost approach toward adaptive screening of primary risk factors and modeling of the non-linear relationships, and combining time series analysis and dynamical threshold approaches to describe the risk evolution and dynamically modify early warning boundaries. Experimental findings indicate that the model has an accuracy of 0.913, precision of 0.901 and recall of 0.887 in financial risk identification, which is much better than the traditional methods. False alarm rate reduced to 0.182 to 0.098, false negative rate reduced to 0.214 to 0.121, lead time increased to 2.4 periods (as opposed to 1.2 periods) and the overall reduction rate in risk was 26.7%. It can be of great benefit in enhancing accuracy of identification and timeliness of early warning and can also offer technical support in corporate financial risk management.
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