An Early Risk Warning of Cross-Border E-Commerce Using BP Neural Network

Zhichao Deng,Xu Xiao,Meiji Yan,Mian Ahmad Jan

doi:10.1155/2021/5518424

Abstract

In this paper, we propose an early warning model of credit risk for cross-border e-commerce. Our proposed model, i.e., KPCA-MPSO-BP, is constructed using kernel principal component analysis (KPCA), improved particle swarm optimization (IPSO), and BP neural network. Initially, we use KPCA to reduce the credit risk index for cross-border e-commerce. Next, the inertia weight and threshold of BP neural network are searched using MPSO. Finally, BP neural network is used for training the data of 13 different enterprises of cross-border e-commerce’s credit risk. To analyze the efficiency of our proposed approach, we use the data of five different enterprises for testing and evaluation. The experimental results show that the mean absolute error (MAE) and root mean square error (RMSE) of our model are the lowest in comparison to the existing models and have much better efficiency.

Highlights

According to the Wall Street Journal, on September 22, 2014, Alibaba, the giant of cross-border e-commerce [1, 2] was listed as the largest IPO ever with a financing amount of 25 billion USD. ough this achievement was mainly due to the development of cross-border e-commerce applications, the credit risk still exists for these applications
Due to the larger number of financial indicators of listed enterprises, some researchers mainly focused on using the principal component analysis (PCA) for the reduction of Mobile Information Systems dimensions, but PCA is used for dimension reduction using linear transformation, which is not effective for solving nonlinear problems. ere are some nonlinear problems in the credit risk indicators of cross-border e-commerce, which have been proved by previous studies [11]
I.e., kernel principal component analysis (KPCA)-MPSO-BP, is discussed in detail here. e basic idea of this model is as follows: firstly, the eigenvalues and eigenvectors of the indicators of cross-border e-commerce credit risk are calculated. en, the kernel principal component eigenvectors with cumulative contribution rate greater than 85% are calculated, and MPSO is used to search the weights and thresholds of BP neural network. e current position and fitness of the particles in the swarm are expressed by the weights, thresholds, and errors of the BP neural network and the results are compared

Summary

Introduction

According to the Wall Street Journal, on September 22, 2014, Alibaba, the giant of cross-border e-commerce [1, 2] was listed as the largest IPO ever with a financing amount of 25 billion USD. ough this achievement was mainly due to the development of cross-border e-commerce applications, the credit risk still exists for these applications. Numerous factors were derived that have a direct impact on the credit risk for cross-border e-commerce applications In this context, using the financial information disclosed for cross-border e-commerce by listed companies and selecting better and reliable enterprises for conducting research on credit risk seems a viable option. Ere are some nonlinear problems in the credit risk indicators of cross-border e-commerce, which have been proved by previous studies [11] In this context, neural network seems a much better option but it has few main drawbacks associated with it such as overfitting, poorly generalized performance, and a much slower convergence speed, among others. To solve the aforementioned problems, we use KPCA, MPSO, and BP neural network for establishing a framework for early warning of credit risk in cross-border e-commerce applications.

Combination Model

Empirical Analysis

Findings

Conclusion