Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development, ICSETPSD 2023, 17th-18th November 2023, Coimbatore, Tamilnadu, India

Research Article

Multi Factor Risk Assessment Algorithm in Digital Economy Platform

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  • @INPROCEEDINGS{10.4108/eai.17-11-2023.2342802,
        author={Di  Zhao and Yimei  Cao and Lulu  Mei},
        title={Multi Factor Risk Assessment Algorithm in Digital Economy Platform},
        proceedings={Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development, ICSETPSD 2023, 17th-18th November 2023, Coimbatore, Tamilnadu, India},
        publisher={EAI},
        proceedings_a={ICSETPSD},
        year={2024},
        month={1},
        keywords={multi factor risk evaluation algorithms digital economy risk warning},
        doi={10.4108/eai.17-11-2023.2342802}
    }
    
  • Di Zhao
    Yimei Cao
    Lulu Mei
    Year: 2024
    Multi Factor Risk Assessment Algorithm in Digital Economy Platform
    ICSETPSD
    EAI
    DOI: 10.4108/eai.17-11-2023.2342802
Di Zhao1,*, Yimei Cao1, Lulu Mei1
  • 1: School of Economics and Management, Shaanxi Fashion Engineering University, Xian, Shaanxi, 712046, China
*Contact email: 3101127244@qq.com

Abstract

With the rapid development of the digital economy, the platform economy has become an important component of today's society. A digital economy platform refers to a form of economic activity that conducts transactions and provides services through online platforms. The platform economy has advantages such as efficiency, convenience, and openness, but it also faces risks such as data security, user privacy, and market competition. Therefore, how to effectively evaluate and manage risks in the platform economy has become an important issue in the field of digital economy. This article aimed to explore the application of multi factor risk assessment algorithms in digital economy platforms, in order to improve the risk management level of the platform and promote the healthy development of the platform economy. This article conducted research on user risk, internal risk, transaction risk assessment, and data security risk assessment. Through algorithm comparison and investigation, relevant data on internet digital platforms were obtained and the performance of evaluation algorithms was evaluated. Experimental data showed that the Relief algorithm performs best in enterprise competition risk classification, with an accuracy of 80% in data security, 79% in user trust, 84% in legal and regulatory accuracy, and 85% in talent and technology accuracy.