Abstract
This paper aims to study enterprise Financial Risk Management (FRM) through Big Data Mining (BDM) and explore effective FRM solutions by introducing information fusion technology. Specifically, big data technology, Support Vector Machine (SVM), Logistic regression, and information fusion approaches are employedto study the enterprise financial risks in‐depth.Among them, the selection offinancial risk indexes has a great impact on the monitoring results of the SVM‐based FRM model; the Logistic regression‐based FRM model can efficientlyclassify financial risks; theinformation fusion‐based FRM model uses a fusion algorithm to fuse different information sources. The results show that the SVM‐based and Logistic regression‐based FRM models can manage and classify enterprise financial risks effectively in practice, with a classification accuracy of 90.22% and 90.88%, respectively; by comparison, the information fusion‐based FRM modelbeats SVM‐based and Logistic regression‐based FRM models by presenting a classification accuracy as high as 95.18%. Therefore, it is concluded that the information fusion‐based FRM is better than the SVM‐based and Logistic regression‐based models; it can integrate and calculate multiple enterprise financial risk data from different sources and obtain higher accuracy; besides, big data technology can provide important research methods for enterprise financial risk problems; SVM‐based FRM model and Logistic regression‐based FRM model can well classify enterprise financial risks, with relatively high accuracy.
Highlights
Today, the fast socio-economic development features a new technological revolutionbyInformation Technology (IT), such as big data [1,2,3], cloud computing [4,5,6], and the Internet of Things (IoT), which is transforming people’s life, work, and the society towards informatization and intellectualization; thanks to IT [7,8,9] and the ecommerce industry based on it, the connection in between people and enterprises is getting ever closer despite their geographic distances and cultural or political barriers
Aiming at the current situation of enterprise financial risk, this paper makesan in-depth study on enterprise financial risk based on Big Data Mining (BDM), Support Vector Machine (SVM), Logistic regression, and information fusion technology
SVM-based Financial Risk Management (FRM) model and Logistic regression-based FRM model can well classify enterprise financial risks with high accuracy; the information fusionbased FRM model can further improve the classification accuracy of enterprise financial risks and shows high reliability and effectiveness; different enterprise risk indexes are analyzed, finding that there is a need for enterprises to strengthenFRMunder big data, especially, the management of liquidity and profitability indexes
Summary
The fast socio-economic development features a new technological revolutionbyInformation Technology (IT), such as big data [1,2,3], cloud computing [4,5,6], and the Internet of Things (IoT), which is transforming people’s life, work, and the society towards informatization and intellectualization; thanks to IT [7,8,9] and the ecommerce industry based on it, the connection in between people and enterprises is getting ever closer despite their geographic distances and cultural or political barriers. Statistics of the development status of ecommerce enterprisesreveals that financial situation and financial risks [10,11,12] can determine how far and high an enterprise can develop; the research of these factors has great practical significance. The Big Data Mining (BDM) approach might be just born to analyze enterprise financial data with its excellent identification effect, thereby being able to give early warning against enterprise financial risks [13,14,15]. To improve the level of enterprise Financial Risk Management (FRM) [16], scholars have conducted numerous studies and developedsome effective theoretical methods. In terms of financial risk theory analysis, there are large numbers of practical cases, some foreign scholars believe that the causes behind the enterprise financial risks are diverse and need specified analysis. Valaskova et al (2018) [17] once argued that financial risks could be solved by regression analysis
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