Research on Financing Risk Assessment and Optimization of Digital Economy Enterprises Combined with Deep Learning Technology

Simon Fraser University, Burnaby, Canada

^* Corresponding author: songjichao2002@gmail.com

Abstract

In the past few years, the meteoric rise of artificial intelligence, especially the pervasive adoption of deep learning, has sparked a boom in digital economy enterprises. These companies have emerged left and right, breathing new vitality into economic growth and transforming the landscape of modern business. However, due to rapid development and innovation, digital economy firms confront numerous risks and obstacles during the financing process. This article focuses on how deep learning technology can evaluate and optimize the financing risks of digital economy firms, with the goal of providing an efficient and accurate risk control approach to support enterprises’ healthy and long-term growth.

Deep learning technology, as a strong data analysis tool, has demonstrated extraordinary potential in the context of financing risk assessment. This paper develops a deep neural network model for assessing financing risk by examining the financing environment and risk characteristics encountered by digital economy firms. To begin, essential input data such as financial statistics, market performance, and the enterprise's management team background are retrieved from past financing situations. Second, create deep learning structures such as multi-layer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) to mine enormous volumes of data and properly identify financial threats that businesses may face. Furthermore, the model's output results can be used to optimize the enterprise's finance strategy, such as recommending reducing the financing amount, prolonging the financing cycle, or altering the financing structure in high-risk situations.