The Application of Large Language Models in The Workflow of the Chinese Internet Credit System and Accountability Analysis
DOI:
https://doi.org/10.62051/ijgem.v3n3.04Keywords:
Large Language Models, Internet credit, P2P lending, AI applicationAbstract
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.
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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
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
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
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
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
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
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
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.
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
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
Verstein A (2011) The Misregulation of Person-to-Person Lending., November 21, 2011, SSRN, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1823763
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
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