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Value of big data to finance: observations on an internet credit Service Company in China

Author

Listed:
  • Shaofeng Zhang

    (100Credit Financial Information Service Co., Ltd.)

  • Wei Xiong

    (100Credit Financial Information Service Co., Ltd.)

  • Wancheng Ni

    (Institute of Automation, Chinese Academy of Sciences)

  • Xin Li

    (Department of Information Systems, City University of Hong Kong)

Abstract

Background his paper presents a case study on 100Credit, an Internet credit service provider in China. 100Credit began as an IT company specializing in e-commerce recommendation before getting into the credit rating business. The company makes use of Big Data on multiple aspects of individuals’ online activities to infer their potential credit risk. Methods Based on 100Credit’s business practices, this paper summarizes four aspects related to the value of Big Data in Internet credit services. Results 1) value from large data volume that provides access to more borrowers; 2) value from prediction correctness in reducing lenders’ operational cost; 3) value from the variety of services catering to different needs of lenders; and 4) value from information protection to sustain credit service businesses. Conclusion The paper also discusses the opportunities and challenges of Big Data-based credit risk analysis, which needs to be improved in future research and practice.

Suggested Citation

  • Shaofeng Zhang & Wei Xiong & Wancheng Ni & Xin Li, 2015. "Value of big data to finance: observations on an internet credit Service Company in China," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-18, December.
    • Handle: RePEc:spr:fininn:v:1:y:2015:i:1:d:10.1186_s40854-015-0017-2
    DOI: 10.1186/s40854-015-0017-2
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    References listed on IDEAS

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    2. Huidong Sun & Mustafa Raza Rabbani & Muhammad Safdar Sial & Siming Yu & José António Filipe & Jacob Cherian, 2020. "Identifying Big Data’s Opportunities, Challenges, and Implications in Finance," Mathematics, MDPI, vol. 8(10), pages 1-19, October.

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