E-Commerce Enterprises Financial Risk Prediction Based on FA-PSO-LSTM Neural Network Deep Learning Model

Abstract

The rapid development of Internet information technology has made e-commerce enterprises face complex and changing financial problems. Combining artificial intelligence algorithms and dynamic monitoring of financial risks has been a current research hotspot. Based on this, this paper conducts an empirical study with a sample of listed Chinese e-commerce enterprises from 2012 to 2022. Firstly, using factor analysis (FA) to obtain the common factors between the original financial and non-financial indicators has the effect of reducing the overfitting risk of the model. Secondly, the mean square error (MSE) of the output and predicted values of the Long Short-Term Memory neural network (LSTM) is used as the fitness function of the intelligent swarm optimization algorithm, and then the Particle Swarm Optimization (PSO) algorithm is used to optimize the learning rate (LR) and the number of hidden layer neurons in the Long Short-Term Memory (LSTM) neural network. Finally, a financial risk prediction model based on FA-PSO-LSTM deep learning is constructed, and multiple benchmark models are introduced for comparative analysis on each evaluation index. The study shows that for nonlinear multivariate data with complex structure, the fused deep learning model proposed in this paper achieves the lowest values in mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). This indicates that the model has the best prediction effect, which is helpful to help managers make relevant decisions efficiently and scientifically and make the enterprise sustainable.

Purchasing in the digital age: A meta-analytical perspective on trust, risk, security, and e-WOM in e-commerce

“…All of these risks are linked to the uncertainty and possibility of negative outcomes in online transactions. Financial risk refers to the potential loss of money that consumers may suffer due to fraud or non-delivery of products [ 31 , 32 ]. Product risk entails uncertainty about the quality and authenticity of products bought online [ 28 ].…”

Financial Risk Early Warning Model for Listed Companies Using BP Neural Network and Rough Set Theory

“…Chen et al (2023) constructed a financial risk prediction model using factor analysis -Particle Swarm Optimization -Long Short-Term Memory (FA-PSO-LSTM) deep learning. They introduced multiple benchmark models for comparative analysis of various evaluation indicators [16].…”

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

“…This sub-category consists of two studies conducted in different years [21], [30]. Both studies share a common objective of developing models for predicting financial risks in businesses or companies.…”

“…Some studies combine their algorithmic basis with optimization techniques to achieve maximum performance. The most commonly used technique is Particle Swarm Optimization (PSO), found in two studies [21], [24], although both studies have different objectives for its utilization. Other swarm optimization techniques include Chaotic Salp Swarm Optimization-based Feature Selection (CSSO-FS) for feature selection, and Quantum Behaved Particle Swarm Optimization (QPSO) to enhance model performance [18].…”