← Back to Volume 3, Issue 3

The Application of Large Language Models in The Workflow of the Chinese Internet Credit System and Accountability Analysis

Cheng Sun
Vol.3, No.3, pp.27-34, 2024 DOI: 10.62051/ijgem.v3n3.04 July 28, 2024
Download PDF
Abstract: This study commences with the challenges and shortcomings faced by traditional Chinese internet credit systems in an information society as a point of departure, examining the rationale behind the necessity for these enterprises to leverage large language model technologies. By dissecting the operational processes and logic of Chinese internet credit companies, the research delineates the methods and advantages of applying large language models across the front-end, mid-end, and back-end of workflows. Furthermore, it investigates the impact of such technological adoption on corporate risk management and accountability issues.
Keywords: Large Language Models, Internet credit, P2P lending, AI application
APA Citation: Cheng Sun (2024). The Application of Large Language Models in The Workflow of the Chinese Internet Credit System and Accountability Analysis. International Journal of Global Economics and Management, 3(3), 27-34. https://doi.org/10.62051/ijgem.v3n3.04

References

  1. Zhang, B. (2022). Application of Innovative Risk Early Warning Model Based on Big Data Technology in Internet Credit Financial Risk. Journal of Information Technology Research (JITR), 15(1), 1-12. http://doi.org/10.4018/JITR.299920
  2. Du, G., Liu, Z., & Lu, H. (2021). Application of innovative risk early warning mode under big data technology in Internet credit financial risk assessment. Journal of Computational and Applied Mathematics, 386, 113260. https://doi.org/10.1016/j.cam.2020.113260
  3. Fan, S., Shen, Y., & Peng, S. (2020). Improved ML-based technique for credit card scoring in internet financial risk control. Complexity, 2020, 1-14. https://doi.org/10.1155/2020/87062852
  4. Xiong, T., Ma, Z., Li, Z., & Dai, J. (2022). The analysis of influence mechanism for internet financial fraud identification and user behavior based on machine learning approaches. International Journal of System Assurance Engineering and Management, 13(Suppl 3), 996-1007. http://doi.org/10.1007/s13198-021-01181-0
  5. Yang, G. (2022). Research on Financial Credit Evaluation and Early Warning System of Internet of Things Driven by Computer-Aided Technology. Comput. Aided. Des. Appl, 19(S6), 158-169. https://doi.org/10.14733/cadaps.2022.S6.158-169
  6. Yoon, Y., Li, Y., & Feng, Y. (2019). Factors affecting platform default risk in online peer-to-peer (P2P) lending business: an empirical study using Chinese online P2P platform data. Electronic Commerce Research, 19, 131-158. https://doi.org/10.1007/s10660-018-9291-1
  7. Zhou, G., Zhang, Y., & Luo, S. (2018). P2P network lending, loss given default and credit risks. Sustainability, 10(4), 1010. https://doi.org/10.3390/su10041010
  8. Herzenstein, M., Andrews, R. L., Dholakia, U. M., & Lyandres, E. (2008). The democratization of personal consumer loans? Determinants of success in online peer-to-peer lending communities. Boston University School of Management Research Paper, 14(6), 1-36.
  9. Desai, D., Jain, A., Naik, D., Panchal, N., & Sawant, D. (2021, May). Invoice processing using RPA & AI. In Proceedings of the International Conference on Smart Data Intelligence (ICSMDI 2021). http://dx.doi.org/10.2139/ssrn.3852575
  10. Brownsword, R., & Somsen, H. (2009). Law, innovation and technology: before we fast forward—a forum for debate. Law, Innovation and Technology, 1(1), 1-73. https://doi.org/10.1080/17579961.2009.11428364
  11. Verstein A (2011) The Misregulation of Person-to-Person Lending., November 21, 2011, SSRN, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1823763
  12. Lin, Z., Whinston, A. B., & Fan, S. (2015). Harnessing Internet finance with innovative cyber credit management. Financial Innovation, 1, 1-24. https://doi.org/10.1186/s40854-015-0004-7