Toward an Efficient and Effective Credit Scorer for Cross-Border E-Commerce Enterprises

Building an efficient and effective credit scorer for enterprises is an important and urgent demand in the cross-border e-commerce industry. In this paper, we present a framework to build a credit scorer using e-commerce data integrated from various sources. First, an improved dependency graph appro...

Full description

Saved in:
Bibliographic Details
Published inScientific programming Vol. 2023; pp. 1 - 9
Main Authors Xu, Chang, Guo, Ruize, Zhang, Yulai, Luo, Xinyuan
Format Journal Article
LanguageEnglish
Published New York Hindawi 12.01.2023
Hindawi Limited
Subjects
Boolean
Credit scoring
Datasets
Electronic commerce
International trade
Logistics
Machine learning
Prejudice
Reconciliation
Regression analysis
Small business
China
Online AccessGet full text

Cover

More Information
Summary:Building an efficient and effective credit scorer for enterprises is an important and urgent demand in the cross-border e-commerce industry. In this paper, we present a framework to build a credit scorer using e-commerce data integrated from various sources. First, an improved dependency graph approach is proposed to recognize distinct records in the dataset. Then, we apply logistic regression using a prejudice remover regularizer to train the model, preceded by predictor preparation through binning and evaluating their information value. Lastly, we build the credit scorer according to the coefficients of the model. We implement our framework on a dataset from the official customs database and a large cross-border e-commerce platform. The empirical results demonstrate that the scorer built by our methodology can be used to effectively evaluate enterprises, while also removing prejudice against small and medium enterprises to a certain extent.
ISSN:1058-9244
1875-919X
DOI:10.1155/2023/5281050