Author(s)

Yaoyi Ying

Corresponding Author

Yaoyi Ying

Abstract

With the development of big data technology and the digital transformation of enterprises, the limitations of traditional enterprise credit assessment and risk control modeling methods are increasingly prominent. This paper focuses on the research on credit assessment and risk control modeling method of digital transformation of enterprises driven by big data. By analyzing the relevant theoretical basis, a big data-driven enterprise credit assessment system is constructed, and the assessment indicators are selected from the dimensions of finance, operation, innovation and network behavior, such as the coefficient of variation of cash flow from operating activities in recent three years, market share growth rate, etc., and the index weights are determined by comprehensive analytic hierarchy process and entropy method. At the same time, using machine learning (ML) and deep learning (DL) technology, the wind control model is constructed through data collection and pretreatment, model selection and training, and the training effects of different models are compared. Among them, the accuracy of LSTM model is 0.82, the recall rate is 0.80, and the F1 value is 0.81. The research results provide scientific and feasible methods and theoretical support for enterprises to realize accurate credit assessment and effective risk prevention and control in the process of digital transformation.

Keywords

Big data; Digital transformation of enterprises; Credit assessment; Wind control modeling; Digital age