A Deep Learning Model for ERP Enterprise Financial Management System

Abstract

With the advent of the information age, the need for information technology construction is beginning to be realized when working in corporate financial management. The application of ERP systems to financial management has become a major trend in the development of modern society. This can help companies collect financial information in real time and analyze and process the obtained information. This paper first gives the significance and models of the ERP financial management system. Then, a financial risk prediction model based on a deep learning model is designed. The method proposes an improved temporal convolutional network-long and short-term memory network (TCN_LSTM) structure and introduces an optimization algorithm to optimize the parameters of the deep learning model. Finally, several benchmark models and evaluation methods are used for comparative study. The experimental results show that the deep learning risk prediction model has significant superiority in prediction accuracy and stability. The proposed model can help enterprises organize their limited resources, realize the scientific allocation of enterprise resources, and create more benefits for enterprises.

Design and Implementation of Financial Management System Based on Computer Network Technology

“…By considering the code redundancy and system adaptability and stability in the system development process, the system architecture is designed in the sense of scalability, with three elements: presentation layer, logic layer, and data. The multilevel design allows each logic level to be independent of itself, which makes the system more flexible and easier to maintain [14]. The main users of the program are the university's financial personnel.…”

Analysis of financial reporting platforms based on the PIPRECIA-S Method

“…Financial reporting platforms can improve the comparability of financial statements. Comparability of financial statements refers to the degree to which financial statements of different companies can be compared and analyzed (Zhang, 2022). Research has shown that comparability of financial statements is positively related to analyst following, forecast accuracy, and the total amount of information available to users.…”

Unleashing the Potentials of Artificial Intelligence for Micro, Small, and Medium Enterprises: A Systematic Literature Review

“…customer churn using ensemble methods, Random Forest (RF), and Light Gradient-Boosting Machine (LightGBM)[17] Sales Q1(Faritha Banu et al, 2022) Model for predicting and managing customer churn and non-churn in the business sector using advanced selection, optimalization, and classification techniques deep learning-based method to enhance the classification accuracy of sentiment analysis on product comments[19] Sales Q1(Qiu & Chen, 2022) Model for analysing environmental costs using Back Propagation Neural Network (BPNN) optimized by the NSGA-II algorithm[20] Financial Q2(Chen & Long, 2023) Model for predicting financial risks based on a neural network with a combination of Factor Analysis (FA), Particle Swarm Optimization (PSO), and Long Short-Term Memory (LSTM) techniques[21] customer churn using Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR)[22] analysis and goal extraction from customer feedback using a combination of Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN)[23] Sales Q2Li et al, 2022) Implementation of Probabilistic Neural Network (PNN) for environmental cost control classification and Particle Swarm Optimization (PSO) algorithm as the decision-making system[24] Financial Q3(J et al, 2023) Customer churn prediction using a Support Vector Machine algorithm and Hybrid Recommendation Strategy to prevent churn[25] financial risks using an optimized structure combining Temporal Convolutional Network-Long (TCN) with Long Short-Term Memory (LSTM)[26] Financial Q4Wisesa et al, 2020) Model for predicting sales in the B2B context using various machine learning algorithms, with Gradient Boost performing the best[27] Sales ~(Sánchez-Torres et al, 2022) …”