E-commerce fraud risk prediction based on RUSBoost algorithm
2021 International Conference on Electronic Information Engineering and Computer Communication, EIECC 2021
; 12172, 2022.
Article
in English
| Scopus | ID: covidwho-1923084
ABSTRACT
In the context of the era of big data, the emergence of e-commerce platforms has brought many opportunities and risks. Due to the COVID-19, e-commerce has achieved unprecedented development, and e-commerce fraud has severely damaged the healthy economic environment. This paper uses the RUSBoost algorithm to build an e-commerce fraud risk prediction model, and verifies the predictive performance of the model through data experiments. The results show that it has a high accuracy rate for identifying e-commerce fraud. If the model is applied to e-commerce, the losses caused by ecommerce fraud could be avoided in time. At present, there are fewer e-commerce fraud risk prediction models and have a wide development prospection. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
2021 International Conference on Electronic Information Engineering and Computer Communication, EIECC 2021
Year:
2022
Document Type:
Article
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