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Abstract

The purpose is to make full use of data mining and machine learning technology under big data to improve the ability of trade financial enterprises to cope with the risk of excessive financialization. In view of the above needs, based on previous studies, genetic algorithm (GA), neural network and principal component analysis (PCA) methods are used to collect and process the data, and build a risk assessment model of excessive financialization of financial enterprises. The performance of the model is analyzed through the data of specific cases. The results suggest that the data mining technology based on back propagation neural network (BPNN) can optimize the input variables and effectively extract the hidden information from the data. The specific examples show that most of the current enterprises do not have greater financial risk. However, most of the financial enterprise indexes show that the actual enterprise assets are gradually financialized. The total accuracy rate of financial risk assessment model based on deep belief network (DBN) is over 91%, and the accuracy of the model can reach 80% even if the sample size is small. Therefore, the financial risk assessment model proposed can effectively analyze the relevant financial data, and provide reference for the financial decision-making research of financial enterprises.

۱ Introduction

With the continuous development of the world economy in recent years, more and more enterprises are active in the fnancial market, which promotes the sustainable development of the national economy. However, the following problems are the fracture of many enterprise capital chains due to the poor risk awareness, fnancial market turbulence, commercial fraud or mismanagement. Small enterprises are on the verge of bankruptcy because of their poor ability to resist risks, which is mainly because more enterprises do not invest in the real economy but pay more attention to virtual fnancial markets (Anginer et al., 2018). With the advent of big data era, although the risk control model of enterprises plays a certain role in credit management, due to the lack of deep understanding of data, the corresponding model accuracy is low, and it is unable to evaluate the borrower comprehensively (Florio & Leoni, 2017). Due to the more in-depth research on big data tools by many scholars, more and more methods are applied to the fnancial feld (Trelewicz, 2017). Traditional risk control model has problems such as single dimension and limited assessment ability. However, using data mining method can analyze the data of fnancial industry in depth, involving many dimensions and more comprehensive assessment (Moradi & Mokhatab Rafei, 2019). With the establishment of distributed database and the gradual maturity of various data platform architectures, more data are stored and calculated, and the dimensions of data analysis are also wider (Medvedev et  al., 2017). Especially with the construction of Hadoop cloud computing platform, the distributed computing ability of big data has been greatly improved, and the data foundation and computing ability are more efcient. Compared with the traditional risk control model, the model based on big data has great changes in the amount of information and accuracy (Ding et al., 2019; Ivanov et al., 2019). Big data can improve the credit system and help trade enterprises reduce credit risk (Saura et  al., 2019; Urbinati et  al., 2019). Data mining can use the network data to improve the scoring model and optimize the approval process through the integration of algorithms, so as to form a sustainable closed-loop management mode (Aldridge, 2019). Therefore, the use of data mining and its application in fnancial risk assessment has become one of the hot spots in this feld.