Application of Deep Learning in Financial Management Evaluation

Lei Xu,Tongguang Ni,Dongli Peng,Wenlei Shi

doi:10.1155/2021/2475885

Abstract

The competition among enterprises is becoming increasingly fierce. The research on the financial management evaluation model is helpful for enterprises to find possible risks as soon as possible. This paper constructs the financial management evaluation model based on the deep belief network and applies the analytic hierarchy process to determine the weight of financial management evaluation indicators, which is compared with other classical deep learning evaluation methods, such as KNN, SVM-RBF, and SVM linear. It has achieved an accuracy of more than 81%, showing a satisfactory prediction effect, which is of great significance to formulate corresponding countermeasures, strengthen financial management, improve the capital market system, and promote high-quality economic development. In addition, aiming at the problem of abnormal financial data, this paper uses the new sample dataset obtained by principal component analysis for convolution neural network model learning, which enhances the prediction accuracy of the model and fully shows that deep learning is feasible in the research of financial management prediction, and there is still a lot of space to explore.

Highlights

With the development of society, financial intelligence increasingly affects our life and has a great impact on the traditional financial work, which is a topic of concern to enterprises all over the world
By importing data into the database or taking the existing data in the database as the analysis object [1], financial intelligence processes the data according to the financial management model and uses the high-speed and accurate computing power of the computer to quickly obtain the enterprise operation diagnosis report, so as to form a fast and reliable basis for business decisionmaking [2]
Using reasonable deep learning technology can solve the problem of efficient automatic data analysis in the financial industry, provide valuable prediction information for managers, and provide reliable early warning for healthy institutional operation [3]

Summary

Introduction

With the development of society, financial intelligence increasingly affects our life and has a great impact on the traditional financial work, which is a topic of concern to enterprises all over the world. Using reasonable deep learning technology can solve the problem of efficient automatic data analysis in the financial industry, provide valuable prediction information for managers, and provide reliable early warning for healthy institutional operation [3]. In the selection of financial management evaluation indicators, existing studies mainly focus on which indicators can accurately predict enterprise crisis [4]. It has experienced the common application stage of multidimensional indicators from single financial ratio indicators and multivariable financial ratio indicators to the combination of financial indicators and nonfinancial indicators [5]. Erefore, scholars began to apply neural network, support vector machine and other models based on artificial intelligence methods to financial crisis early warning. It is different from the qualitative analysis. e work is difficult, and the accuracy is low. e model research has higher reliability and uses the analytic hierarchy process to determine the enterprise evaluation index system. is model interprets the historical data, links the characteristics of the data with the financial situation of the enterprise, and uses the existing data to analyze and predict the future, so as to ensure the accuracy of the prediction

Related Work

Evaluation Modelling Based on Deep Belief Network

Results and Safety

Conclusion