BIMSA

This course aims to develop students' professional skills and innovation ability in the field of quantitative risk management, in order to cope with the impact of cutting-edge artificial intelligence technologies on financial formats, risk situations and management technologies. The course focuses on technology empowerment, integrates AI and big data technology, deepens students' understanding of risk management theory, and improves students' quantitative analysis and decision-making ability. Through case analysis, simulation practice and AI scenario examples, students' practical skills and problem-solving ability are strengthened, and interdisciplinary knowledge such as finance, statistics and computer science is integrated to build a systematic knowledge system.

2024年09月13日至 2025年01月10日

Weekday	Time	Venue	Online	ID	Password
周五	09:50 - 12:15	A3-4-301	ZOOM 02	518 868 7656	BIMSA

Finance, Statistics, Computer Science

Part 1: Theoretical basis and high technology
Chapter 1: Definition and classification of risk
Chapter 2: Artificial intelligence methods and financial big data analysis technology
Chapter 3: Financial big data analysis and processing tools practice
Lesson 1: AI quantitative risk management algorithm and model practice based on Python

Part 2: Real-time prevention and control of compliance risks
Chapter 4: Real-time prevention and control of market risks
Chapter 5: Real-time prevention and control of credit risks
Chapter 6: Intelligent management practice lesson of legal risk, economic risk and national risk
Lesson 2: Quantitative risk management based on financial big data crawling and analysis simulation

Part 3: Intelligent supervision of abnormal risks
Chapter 7: Intelligent supervision of price manipulation
Chapter 8: Anti-fraud technology based on AI technology and big data
Chapter 9: Practical lesson of anti-money laundering monitoring method based on AI technology and big data
Chapter 3: Construction and testing simulation of anti-fraud and anti-money laundering model based on Python

Part 4: Monitoring and early warning of systemic risk
Chapter 10: Monitoring and early warning of systemic risk in banking
Chapter 11: Monitoring and early warning of systemic risk in securities market
Chapter 12: Financial crisis prediction and management Practice
Lesson 4: Practical practice of systemic risk monitoring and early warning based on simulation system

[1] Hull, J. Risk Management and Financial Institutions, 6th Edition[M]. John Wiley & Sons, 2023.
[2] Kelliher, C. Quantitative Finance with Python: A Practical Guide to Investment Management, Trading, and Financial Engineering[M]. Chapman and Hall/CRC, 2022.
[3] Karasan, A. Machine Learning for Financial Risk Management with Python[M]. O'Reilly Media, Inc., 2021.
[4] Hubbard, D. W. The Failure of Risk Management: Why It's Broken and How to Fix It[M]. John Wiley & Sons, 2020.
[5] Aziz, S., Dowling, M. Machine Learning and AI for Risk Management[M]. Springer International Publishing, 2019.
[6] Hopkin, P. Fundamentals of Risk Management: Understanding, Evaluating and Implementing Effective Risk Management[M]. Kogan Page Publishers, 2018.
[7] McNeil, A. J., Frey, R., Embrechts, P. Quantitative Risk Management: Concepts, Techniques and Tools-Revised Edition[M]. Princeton University Press, 2015.
[8] 张金清. 金融风险管理实务[M]. 复旦大学出版社, 2018.

Undergraduate , Advanced Undergraduate , Graduate , 博士后 , Researcher

不公开

中文

刘庆富，北京雁栖湖应用数学研究院兼职教授，复旦大学经济学院金融学教授、博士生导师。东南大学管理科学与工程博士、复旦大学金融学博士后、美国斯坦福大学访问学者，2017入选“上海市浦江人才”计划。现任复旦-斯坦福中国金融科技与安全研究院执行院长，复旦-中植大数据金融与投资研究院学术副院长，上海市金融大数据联合创新实验室副主任。主要研究兴趣为金融科技、大数据金融、衍生金融工具、量化投资、科技监管、绿色金融及不良资产处置等。曾在Journal of Econometrics、Journal of International Money and Finance等国内外重要期刊发表论文100余篇；出版专著三部；主持国家自然科学基金委、科技部、教育部等课题20余项。研究成果多次获得会议最佳论文奖或一等奖，学术观点和访谈也被多家主流媒体刊登和转载。此外，还开设《金融大数据分析》、《量化交易》、《随机过程与随机分析》、《金融时间序列分析与软件应用》和《高级计量经济学》等课程。