“断直连”政策下平台个人信用评分的包容审慎监管

Abstract
Figure/Table
References
Related Citation (0)

Download: PDF (547 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Platform personal credit scoring is an innovative online credit governance tool in the platform economy, featuring extensive information processing, inclusive coverage, and diverse application scenarios. However, it faces challenges such as credit regulatory arbitrage risks, increased risks to personal privacy and information protection due to the use of alternative data, and risks of algorithmic discrimination. Given these characteristics and challenges, the attributes of platform personal credit scoring should be determined on a case-by-case basis, and inclusive and prudent regulation should be implemented. Firstly, personal credit scoring by platforms should be regulated by classification. The scope of application of the “disconnection” policy should be clearly defined as the scenarios where platforms, as third parties, provide personal credit scoring to financial institutions outside the platform. The number of personal credit information licenses should be moderately increased to coordinate the conflicts of interest caused by the “disconnection” policy. Secondly, it is necessary to prevent the risks associated with the use of alternative data, balancing financial inclusiveness and personal information protection. The term “other relevant information” should be interpreted restrictively to bring alternative data under credit information supervision. Thirdly, compulsory information disclosure regulations should be adopted to enhance the transparency of credit scoring, and reputation mechanisms should be used to encourage platforms to voluntarily improve the transparency of credit scoring. Finally, the personal credit scoring algorithms of platforms should be included in the regulatory sandbox. The risk of algorithmic discrimination should be regulated in advance through algorithm filing, and a credit scoring algorithm regulatory system that balances efficiency and fairness should be established.

Key words： the “Cutting Direct Connections” policy personal credit score on the platform inclusive and prudent regulation alternative data algorithm fairness

Received: 21 August 2025

PACS:	D912.28
	DF438

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	YANG Jian-xun

Cite this article:

YANG Jian-xun. On the Inclusive and Prudent Regulation of Platform Personal Credit Scores under the “Cutting Direct Connections” Policy[J]. , 2025, 42(6): 89-98.

URL:

https://xb.szu.edu.cn/EN/ OR https://xb.szu.edu.cn/EN/Y2025/V42/I6/89

[1] 蒋凯元,陈学锋,王笑等.大数据征信与智能评估:方法及应用案例[M].北京:人民邮电出版社,2022.17.
[2] 叶明,邱威棋.“秘密自治”的数字平台信用评分:隐忧与规制[J].北京工业大学学报(社会科学版),2023,(4):118-134.
[3] 姚佳. 征信的准公共性与大数据运用[J].银行家,2018,(12):135-137.
[4] 王俊山,王玥.对我国个人信用评分及监管的分析与思考[J].金融发展研究,2021,(1):86-89.
[5] 吴晶妹. 从信用的内涵与构成看大数据征信[J].首都师范大学学报(社会科学版),2015,(6):66-72.
[6] 杨帆. 作为新型征信的信用评分:规制困境与基本立场[J].现代经济探讨,2021,(8):124-132.
[7] 杨明基. 经济金融词典[M].北京:中国金融出版社,2015. 1049.
[8] 刘权. 数字经济视域下包容审慎监管的法治逻辑[J].法学研究,2022,(4):37-51.
[9] 黄锫. 论我国人工智能领域包容审慎监管的法治维度[J].财经法学,2025,(2):94-109.
[10] (美)布莱恩·阿瑟.技术的本质:技术是什么,它是如何进化的[M].曹东溟,王健译.杭州:浙江人民出版社,2018. 213.
[11] 胡凌. “非法兴起”:理解中国互联网演进的一个视角[J].文化纵横,2016,(5):120-125.
[12] (美)乔希·劳尔.美国征信史[M].刘新海,刘志军译.北京:中国金融出版社,2024.
[13] 廖凡. 论金融科技的包容审慎监管[J].中外法学,2019,(3):797-816.
[14] 邵婉莹. 英国Equifax因泄露客户个人信息被处罚的案例及启示[J].中国信用卡,2025,(1):61-64.
[15] 郑瑜.处罚+通报金融机构个人信息保护持续承压[EB/OL].http://www.cb.com.cn/index/show/jr6/cv/cv12523371281,2025-08-20.
[16] Nikita Aggarwal.The Norms of Algorithmic Credit Scor-ing[J].Cambridge Law Journal,2021,80(1):42-73.
[17] Janine S.Hiller, Lindsay Sain Jones. Who’s Keeping Score? Oversight of Changing Consumer Credit Infrast-ructure[J].American Business Law Journal,2022,59(1):61-122.
[18] 刘颖. 数字经济中算法消费者的生成机理、保护方案与制度建构[J].深圳大学学报(人文社会科学版),2023,(2):112-122.
[19] 杨帆. 网络消费者信用评价的法治困境及其破解[J].大连理工大学学报(社会科学版),2022,(5):91-100.
[20] 葛莉. “断直连”政策下个人征信数据治理问题研究[J].哈尔滨商业大学学报(社会科学版),2025,(1):118-128.
[21] Christine Menglu Wang, et al.From Credit Information to Credit Data Regulation: Building An Inclusive Sustai-nable Financial System in China[J].Washington Internat-ional Law Journal,2024,33(2):270-313.
[22] (英)菲利普·莫利纽克斯,尼达尔·沙姆洛克.金融创新[M].冯健等译.北京:中国人民大学出版社,2003.132.
[23] 杨帆. 平台信用治理:信用分规则的利益失衡及其破解路径[J].南京社会科学,2022,(7):93-102.
[24] Aaron Chou.What’s in the Black Box:Balancing Financ-ial Inclusion and Privacy in Digital Consumer Lending[J].Duke Law Journal,2020,69(5):1183-1218.
[25] 阳建勋. 《民法典》时代自然人涉税信息保护的理念重塑与制度完善——以《税收征管法》修订为视角[J].税务研究,2021,(2):61-67.
[26] Sahiba Chopra.Current Regulatory Challenges in Consu-mer Credit Scoring Using Alternative Data-Driven Met-hodologies[J].Vanderbilt Journal of Entertainment & Tec-hnology Law,2021,23(3):625-648.
[27] 梁慧星. 民法解释学[M].北京:中国政法大学出版社,2000.219.
[28] 张欣,朱芸阳,孔祥稳.人工智能法学前沿案例评析[M].北京:对外经济贸易大学出版社,2023.
[29] 张凌寒. 算法自动化决策与行政正当程序制度的冲突与调和[J].东方法学,2020,(6):4-17.
[30] (美)约翰·D﹒多纳休,理查德·J.泽克豪泽.合作:激变时代的合作治理[M].徐维译.北京:中国政法大学出版社,2015.115.
[31] (美)朱迪·弗里曼.合作治理与新行政法[M].毕洪海,陈标冲译.北京:商务印书馆,2010.449.
[32] Hilary J.Allen. Sandbox Boundaries[J].Vanderbilt Journal of Entertainment & Technology Law,2020,22(2):299-322.
[33] 周辉,李延枫.《人工智能示范法》释义[M].北京:中国社会科学出版社,2024.
[34] John Hillman.Smart Regulation: Lessons from the Artif-icial Intelligence Act[J].Emory International Law Review,2023,37(4):755-826.
[35] Jerome De Cooman.Humpty Dumpty and High-Risk AI Systems: The Ratione Materiae Dimension of the Proposal for an EU Artificial Intelligence Act[J].Market and Com-petition Law Review,2022,6(1):49-88.