Retracted] Analysis and Risk Assessment of Corporate Financial Leverage Using Mobile Payment in the Era of Digital Technology in a Complex Environment

Wenjing Wei,Bingxiang Li,Naeem Jan

doi:10.1155/2022/5228374

Abstract

The study aims to improve the enterprise’s ability to respond to financial crises and find some countermeasures to prevent potential financial risks. The enterprise financial risk is assessed, and the automatic summary function of mobile payment platforms based on long short‐term memory (LSTM) is performed to extract the structured data and unstructured texts from its annual report. On this basis, the early warning system model of financial risks is implemented and its accuracy is improved. The structured data and unstructured text in the company’s annual report are extracted. The enterprise financial risk early warning system model is constructed. The accuracy of the enterprise financial risk early warning system has been improved. Firstly, we use the convolutional neural network (CNN) to establish a financial risk prediction system using financial data and test various indicators of the system. Secondly, the financial annual report of the listed company is obtained from the Internet. The required financial statements are obtained in two ways. The first is to set high special treatment (ST) sample weights and delete some non‐ST samples. The second is to delete punctuation marks, interjections, numbers, and so on and process the collected text data. The financial risk prediction model is established using the financial text, and the LSTM + attention mechanism is used to optimize the model. Finally, combining structured financial data and unstructured financial text to establish a forecasting model, the model uses LSTM. Combined with a single‐layer neural network or CNN model, the comparison experiment is carried out in two ways. Experiments show that the CNN or LSTM attention mechanism cannot significantly improve the performance of the system only using financial data or texts. Using the financial data and financial text using the LSTM + CNN model, the F1 value reached 85.29%. Financial data and other indicators in the text have also been greatly improved, and the overall performance is the best. In summary, LSTM using financial data and financial texts combined with CNN to establish a risk prediction system can help investors and companies themselves find possible financial crises in listed companies as soon as possible and help companies deal with their financial risks in a timely manner.

Highlights

With the rapid development of the global economy, many companies have gone public in various countries
From the perspective of investors, we extract structured and unstructured information from the annual reports of listed companies, establish financial risk prediction models using neural networks, and compare different models to conduct experiments. e proposed method greatly improves the forecasting effect and provides a reference for investors to discover the financial risks of listed companies in time and avoid losses
K and F1 are more important, so they need to be improved. e convolutional neural network (CNN) model is more likely to overfit the categories with more data when the data are unbalanced, but there is no more indepth information on the financial indicators. e CNN model’s ability to extract information is limited, so in deeper information extraction, it does not bring more benefits

Summary

Introduction

With the rapid development of the global economy, many companies have gone public in various countries. Wu and Wu established a financial forecasting and early warning model using neural network [4]. Ey used improved particle swarm optimization to establish a financial risk early warning model using neural networks [5]. Matin et al established a model using convolutional recurrent neural network (RNN) combined with unstructured text data, which provided a statistically significant improvement in the performance of financial distress prediction [8]. From the perspective of investors, we extract structured and unstructured information from the annual reports of listed companies, establish financial risk prediction models using neural networks, and compare different models to conduct experiments. E proposed method greatly improves the forecasting effect and provides a reference for investors to discover the financial risks of listed companies in time and avoid losses.

Construction of Prediction Model Using Neural Network

Analysis of Experimental Results Using Financial Data and Financial Text

Evaluation Indicators

Conclusion