Track Co-Chairs
Gang Wang
Professor
wgedison@hfut.edu.cn
Hefei University of Technology
Wei Xu
Professor
weixu@ruc.edu.cn
Renmin University of China
Jingling Ma
Lecturer
majingling1626@zufe.edu.cn
Zhejiang University of Finance and Economics
Brief Introduction
Generative AI (GAI) has emerged as a groundbreaking technology with immense potential in various fields, including risk detection, monitoring, and prevention. The evolution from traditional risk analytics and modelling technologies to those under GAI signifies a pivotal advancement in the discipline of risk management, making substantial contributions to protecting the interests of stakeholders, and maintaining the stable development of the market.
Advanced GAI models, such as GPT-4, possess the capability to discern potential risk clues embedded in extensive multi-modal data, contributing to the early detection of various types of risks. In addition, the seamless integration and adaptive learning capabilities of GAI models empower organizations with the proficiency to continuously monitor their risk landscape in real time. This ongoing surveillance allows for the timely identification of evolving risk factors, and the dynamic adjustment of risk management plans. Furthermore, GAI facilitates the implementation of robust risk-mitigation strategies. These strategies are informed by a deep analytical exploration to the decision-making process, thereby optimizing organizational responses to prevent risks.
Encompassing all the above, we welcome submissions that discuss the latest advancements, case studies, and innovative practices pertaining to the integration of GAI within risk analytics and modelling. By highlighting the latest advancements and best practices, this track aims to advance the understanding and application of GAI in risk detection, monitoring, and prevention. The scope of topics for submission is broad and inclusive, encompassing but not limited to the following areas of interest.
Topics
1. Impacts of GAI on risk analytics and modelling
2. Risk (eg. default, fraud, and credit risk) behavioral analysis with GAI
3. GAI-driven early risk detection modeling
4. Integrating multi-modal data (eg. text, and image) for risk management
5. GAI-based risk detection, monitoring, and prevention
6. Personalized risk management solutions benefited from GAI
7. Decision support in risk management with GAI
8. New perspective on AIGC-involved risk management strategies
9. GAI-enabled intelligent risk management platform
10. Challenges and opportunities of GAI in risk analytics and modelling