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      Risk evaluation for C2C E‐commerce via an improved credit counting method

      한글로보기

      https://www.riss.kr/link?id=O113145138

      • 저자
      • 발행기관
      • 학술지명
      • 권호사항
      • 발행연도

        2021년

      • 작성언어

        -

      • Online ISSN

        2476-1508

      • 등재정보

        SCOPUS;ESCI

      • 자료형태

        학술저널

      • 수록면

        n/a-n/a   [※수록면이 p5 이하이면, Review, Columns, Editor's Note, Abstract 등일 경우가 있습니다.]

      • 구독기관
        • 전북대학교 중앙도서관  
        • 성균관대학교 중앙학술정보관  
        • 부산대학교 중앙도서관  
        • 전남대학교 중앙도서관  
        • 인천대학교 학산도서관  
        • 숙명여자대학교 중앙도서관  
        • 서강대학교 로욜라중앙도서관  
        • 충남대학교 중앙도서관  
        • 한양대학교 백남학술정보관  
        • 이화여자대학교 중앙도서관  
        • 고려대학교 도서관  
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      부가정보

      다국어 초록 (Multilingual Abstract)

      E‐commerce has become a popular way for shopping, especially during the Covid‐19 pandemic. The key of the E‐commerce is the credit evaluation, for instance the client to client (C2C) mode E‐commerce. In this paper, an improved credit scoring method is proposed. The improved credit scoring method uses the interval distribution of commodity prices to add points for successful transactions and deduct points for failed transactions by using credit grade deduction coefficients and credit level, which can solve credit hype and periodic deception. Through the analysis of credit risk, a risk calculation method for online transactions is designed by using the improved credit scoring model. The proposed risk calculation method can evaluate the risk of current transactions based on historical transaction conditions and current transaction prices. The simulated experiments show that the improved credit scoring method has better anti‐credit speculation and periodic deception effects, and the risk assessment method is more accurate and effective for risk analysis and prediction in C2C E‐commerce transactions.
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      E‐commerce has become a popular way for shopping, especially during the Covid‐19 pandemic. The key of the E‐commerce is the credit evaluation, for instance the client to client (C2C) mode E‐commerce. In this paper, an improved credit scoring m...

      E‐commerce has become a popular way for shopping, especially during the Covid‐19 pandemic. The key of the E‐commerce is the credit evaluation, for instance the client to client (C2C) mode E‐commerce. In this paper, an improved credit scoring method is proposed. The improved credit scoring method uses the interval distribution of commodity prices to add points for successful transactions and deduct points for failed transactions by using credit grade deduction coefficients and credit level, which can solve credit hype and periodic deception. Through the analysis of credit risk, a risk calculation method for online transactions is designed by using the improved credit scoring model. The proposed risk calculation method can evaluate the risk of current transactions based on historical transaction conditions and current transaction prices. The simulated experiments show that the improved credit scoring method has better anti‐credit speculation and periodic deception effects, and the risk assessment method is more accurate and effective for risk analysis and prediction in C2C E‐commerce transactions.

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