Smart Risk Management in Practice
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.
Lecturer
Qing Fu Liu
Date
13th September, 2024 ~ 10th January, 2025
Location
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| Friday | 09:50 - 12:15 | A3-4-301 | ZOOM 02 | 518 868 7656 | BIMSA |
Prerequisite
Finance, Statistics, Computer Science
Syllabus
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
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
Reference
[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.
[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.
Audience
Undergraduate
, Advanced Undergraduate
, Graduate
, Postdoc
, Researcher
Video Public
No
Notes Public
No
Language
Chinese
Lecturer Intro
Qingfu Liu, the professor and doctoral supervisor at School of Economics, Fudan University, was awarded as Shanghai Pujiang Scholar. Prof. Liu obtained a doctorate in management science and engineering from Southeast University, was a postdoctoral fellow at Fudan University, and also a visiting scholar at Stanford University. Prof. Liu is now the executive dean of Fudan-Stanford Institute for China Financial Technology and Risk Analytics, the academic vice dean of Fudan-Zhongzhi Institute for Big Data Finance and Investment, and the vice dean of Shanghai Big Data Joint Innovation Lab. Prof. Liu's research interests mainly include financial derivatives, big data finance, quantitative investment, RegTech, green finance and non-performing asset disposal. He has published more than 80 papers in the Journal of economics, Journal of International Money and Finance, Journal of Management Sciences in China and other important journals at home and abroad, published three monographs, and presided over more than 20 national and provincial research projects. He is currently an associate editor at Digital Finance and an editor at World Economic Papers.