2024, Volume 26, Issue 3
Strategic Study of CAE >> 2024, Volume 26, Issue 3 doi: 10.15302/J-SSCAE-2024.03.020
Financial Risk Monitoring and Early-Warning System in the Context of Digital Transformation
1. School of Computer Science, Fudan University, Shanghai 200433, China;
2. Institute of Financial Technology, Fudan University, Shanghai 200433, China
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Abstract
Currently, the digital transformation of the financial industry in China has moved from multiple breakthroughs into a new stage of deepening and high-quality development, which necessitates a collaborative governance mechanism that coordinates multiple parties including governments, enterprises, and individuals. In view of the new, complex, and potentially harmful financial risks arising in the context of digital transformation, the financial industry urgently needs to improve its financial risk monitoring and earlywarning capabilities to effectively protect financial security. This study analyzes the progress of digital transformation of the financial industry as well as the implications and characteristics of new financial risks through literature research and theoretical analysis. It also investigates the mainstream financial risk monitoring and early-warning technologies in China and abroad, and clarifies the prominent problems regarding risk characterization and recognition, transmission and tracking, and inference assessment. Furthermore, we propose the overall framework, innovative research methods, and improving paths for the financial risk monitoring and earlywarning system in the context of digital transformation. This study reveals that financial risks have new characteristics in the context of digital transformation, such as faster update and iteration, higher risk frequency, and stronger concealment. Existing financial risk monitoring and early-warning technologies have numerous deficiencies and face multiple challenges in dealing with new financial risks, such as difficulty in characterizing, tracking, and assessing risks. Therefore, to improve the financial risk prevention capability of China and guarantee national financial security, it is proposed to develop cross-industry sharing standards for financial data, establish a knowledge representation paradigm and a cross-industry transmission mechanism of financial risks, and build a large model regarding financial risk monitoring and early warning.
Keywords
financial industry ; digital transformation ; financial risk ; monitoring ; early warning ; machine learning ; data mining
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References
[ 1 ]
占比达10%!中国数字经济"长"得快 [EB/OL]. (2024-05-24)[2024-05-30]. https://www.gov.cn/yaowen/liebiao/202405/content_6953472.htm.
Accounting for 10%! China´s digital economy "growing" fast [EB/OL]. (2024-05-24)[2024-05-30]. https://www.gov.cn/yaowen/liebiao/202405/content_6953472.htm.
[ 2 ]
中华人民共和国国务院. "十四五"数字经济发展规划 [EB/OL]. (2021-12-12)[2024-02-28]. https://www.gov.cn/zhengce/content/2022-01/12/content_5667817.htm.
State Council of the People´s Republic of China. The 14th Five-Year Plan on digital economy development [EB/OL]. (2021-12-12)[2024-02-28]. https://www.gov.cn/zhengce/content/2022-01/12/content_5667817.htm.
[ 3 ]
中国人民银行印发《金融科技发展规划(2022—2025年)》 [EB/OL]. (2022-01-04)[2024-02-28]. http://www.pbc.gov.cn/goutongjiaoliu/113456/113469/4438627/index.html.
The People´s Bank of China issued the Fintech development plan for 2022—2025 [EB/OL]. (2022-01-04)[2022-06-06]. http://www.pbc. gov.cn/goutongjiaoliu/113456/113469/4438627/index.html.
[ 4 ]
关于银行业保险业数字化转型的指导意见 [EB/OL]. (2022-01-10)[2024-02-28]. https://www.gov.cn/zhengce/zhengceku/2022-01/27/content_5670680.htm.
Guidance on digital transformation in the banking and insurance industry [EB/OL]. (2022-01-10)[2024-02-28]. https://www.gov.cn/zhengce/zhengceku/2022-01/27/content_5670680.htm.
[ 5 ]
全国金融标准化技术委员会. 证券期货业科技发展"十四五"规划 [R]. 北京: 全国金融标准化技术委员会, 2021.
China Securities Industry Standardization Technical Committee. The 14th Five-Year Plan for the development of science and technology in the securities and futures industry [R]. Beijing: China Securities Industry Standardization Technical Committee, 2021.
[ 6 ]
新华网. 重构业务流程, 提升服务质效——高质量推进金融业数字化转型 [EB/OL]. (2022-09-23)[2024-02-28]. http://www.news.cn/tech/20220923/2169750efbd7412dbebf9a464e479135/c.html.
Xinhua Net. Reconstructing business processes and enhancing service quality and efficiency—Promoting digital transformation of the financial sector with high quality [EB/OL]. (2022-09-23)[2024-02-28]. http://www.news.cn/tech/20220923/2169750efbd7412dbebf9a464e479135/c.html.
[ 7 ]
中国证券业协会. 证券公司数字化转型实践报告及案例汇编(2022) [R]. 北京: 中国证券业协会, 2022.
Securities Association of China. Compilation of digital transformation practice reports and cases of securities firms (2022) [R]. Beijing: Securities Association of China, 2022.
[ 8 ]
侯鹏, 李智鑫, 张飞, 等. 金融数据安全治理智能化技术与实践 [J]. 网络与信息安全学报, 2023, 9(3): 174‒187.
Hou P, Li Z X, Zhang F, et al. Technology and practice of intelligent governance for financial data security [J]. Chinese Journal of Network and Information Security, 2023, 9(3): 174‒187.
[ 9 ]
井超, 黄晟, 李卓然. 《关于银行业保险业数字化转型的指导意见》对金融机构IT基础架构新要求的解读 [J]. 网络安全和信息化, 2022 (4): 20‒21.
Jing C, Huang S, Li Z R. Interpretation of new requirements for IT infrastructure of financial institutions in Guiding opinions on digital transformation of banking and insurance industry [J]. Cybersecurity & Informatization, 2022 (4): 20‒21.
[10]
董方冉. 五大上市险企2022年报盘点 财险保费实现稳增长 [J]. 中国金融家, 2023 (4): 78‒79.
Dong F R. Top 5 listed insurance companies 2022 annual report roundup, property insurance premium achieves stable growth [J]. China Financialyst, 2023 (4): 78‒79.
[11]
董宇冉. 电信网络金融诈骗新特点及其防控策略 [J]. 金融, 2023, 13(2): 354‒360.
Dong Y R. New characteristics of telecommunication network financial fraud and its prevention and control strategy [J]. Finance, 2023, 13(2): 354‒360.
[12] Communication Fraud Control Association. Telecommunications fraud increased 12% in 2023 equating to an estimated $38.95 billion lost to fraud [EB/OL]. (2023-11-13)[2024-02-28]. https://cfca.org/telecommunications-fraud-increased-12-in-2023-equating-to-an-estimated-38-95-billion-lost-to-fraud/.
[13]
张晓朴. 系统性金融风险研究: 演进、成因与监管 [J]. 国际金融研究, 2010 (7): 58‒67.
Zhang X P. A study on systemic financial risk: Evolution, causes and supervision [J]. Studies of International Finance, 2010 (7): 58‒67.
[14]
杨子晖, 陈雨恬, 张平淼. 重大突发公共事件下的宏观经济冲击、金融风险传导与治理应对 [J]. 管理世界, 2020, 36(5): 13‒35.
Yang Z H, Chen Y T, Zhang P M. Macroeconomic shock, financial risk transmission and governance response to major public emergencies [J]. Management World, 2020, 36(5): 13‒35.
[15] Rizwan M S, Ahmad G, Ashraf D. Systemic risk: The impact of COVID-19 [J]. Finance Research Letters, 2020, 36: 101682.
[16] Ouyang Z S, Yang X T, Lai Y Z. Systemic financial risk early warning of financial market in China using attention-LSTM model [J]. The North American Journal of Economics and Finance, 2021, 56: 101383.
[17]
付刚. 宏观审慎管理与系统性金融风险防范思考 [J]. 金融发展研究, 2010 (10): 60‒63.
Fu G. Research on macro-prudential management and systemic financial risk prevention [J]. Journal of Financial Development Research, 2010 (10): 60‒63.
[18]
陶玲, 朱迎. 系统性金融风险的监测和度量——基于中国金融体系的研究 [J]. 金融研究, 2016 (6): 18‒36.
Tao L, Zhu Y. On China´s financial systemic risks [J]. Journal of Financial Research, 2016 (6): 18‒36.
[19]
柴洪峰, 李智鑫, 王意帆, 等. 基于人工智能的金融风险监测技术发展趋势 [J]. 新金融, 2024 (3): 4‒10.
Chai H F, Li Z X, Wang Y F, et al. The development trend of financial risk monitoring technology based on artificial intelligence [J]. New Finance, 2024 (3): 4‒10.
[20] Ometov A, Bezzateev S, Mäkitalo N, et al. Multi-factor authentication: A survey [J]. Cryptography, 2018, 2(1): 1.
[21] Ni P F, Wang Q X. Internet and telecommunication fraud prevention analysis based on deep learning [J]. Applied Artificial Intelligence, 2022, 36(1): 2137630.
[22]
鲁明勇, 游俊. 湘西州非法集资危机形成的原因及演化过程分析 [J]. 吉首大学学报(社会科学版), 2010, 31(2): 105‒111.
Lu M Y, You J. On the reasons and process of illegal fund-raising in Xiangxi autonomous prefecture [J]. Journal of Jishou University (Social Sciences Edition), 2010, 31(2): 105‒111.
[23]
陈哲, 范效军. 问题金融控股公司的风险处置——基于"安邦系"和"明天系" 的案例研究 [J]. 银行家, 2019 (8): 19‒23.
Chen Z, Fan X J. Risk resolution for distressed financial holding companies: Case studies based on the Anbang and Tomorrow systems [J]. The Chinese Banker, 2019 (8): 19‒23.
[24]
吴云, 朱玮. 虚拟货币的国际监管: 以反洗钱为起点走出自发秩序 [J]. 财经法学, 2021 (2): 79‒97.
Wu Y, Zhu W. Anti-money laundering regulation over virtual currencies: A starting point to depart from autonomous order [J]. Law and Economy, 2021 (2): 79‒97.
[25]
孟博, 刘加兵, 刘琴, 等. 智能合约安全综述 [J]. 网络与信息安全学报, 2020, 6(3): 1‒13.
Meng B, Liu J B, Liu Q, et al. Survey of smart contract security [J]. Chinese Journal of Network and Information Security, 2020, 6(3): 1‒13.
[26]
方乐, 李伟群. 个人金融信息保护视阈下金融数据共享规则的完善 [J]. 金融法苑, 2021(3): 77‒88.
Fang L, Li W Q. Improvement of financial data sharing rules under the threshold of personal financial information protection [J]. Financial Law Forum, 2021(3): 77‒88.
[27] Liew S P, Takahashi T, Ueno M. PEARL: Data synthesis via private embeddings and adversarial reconstruction learning [C]. Virtual-only: The Tenth International Conference on Learning Representations, 2022.
[28] Ashta A, Herrmann H. Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance [J]. Strategic Change, 2021, 30(3): 211‒222.
[29]
柴洪峰, 王帅, 涂晓军, 等. 智能化金融科技创新监管工具: 理念、平台框架和展望 [J]. 智能科学与技术学报, 2020, 2(3): 214‒226.
Chai H F, Wang S, Tu X J, et al. Intelligent innovative regulatory tools on financial technology: Concept, platform framework, and prospects [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(3): 214‒226.
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