Large language models (LLMs) have demonstrated tremendous potential in various natural language processing tasks, including language generation, machine translation, and question answering. In the financial services industry, LLMs have the potential to significantly impact tasks such as investment research, risk management, sentiment analysis and customer service. Moreover, LLMs can help automate the process of analyzing financial reports and extracting key insights that can aid businesses in making informed and intelligent decisions.

This symposium provides a platform for researchers, practitioners, and industry experts from around the world to share new ideas, exchange research findings, and discuss the challenges and opportunities in the field of LLMs for financial services. The symposium will cover two themes: 1) potential applications and best practices of LLMs for financial services; and 2) challenges that need to be addressed to make them efficient, effective, and trustworthy.

The symposium will also feature invited talks by leading researchers and industry experts, as well as panel discussions on the latest trends and challenges in the field. We welcome researchers, practitioners, and industry experts from academia and industry to submit their work and participate in this exciting event.

List of Topics

• Techniques:
  • Multimodal modeling of financial data using LLMs
  • Preprocessing and cleaning of financial data for use with LLMs
  • Integration of LLMs with other AI technologies in financial services
  • Novel architectures and training techniques for LLMs in financial services.
  • Scalability and efficiency of LLMs in financial services
  • Cross-lingual and multilingual LLMs in financial services
  • Human-in-the-loop approaches for LLMs in financial services
• Applications:
  • Financial forecasting using LLMs
  • Sentiment analysis and opinion mining for financial data
  • LLM-based trading algorithms and decision-making systems
  • Analysis of financial news and social media using LLMs
  • Semantic analysis of financial reports and filings
  • Explainable AI in financial services using LLMs
  • Transfer learning and domain adaptation for LLMs in financial services
  • Case studies and success stories of LLMs in financial services
• Challenges:
  • Evaluation of LLMs for financial services
  • Social economics and trustworthiness for LLMs in financial services
  • Ethical and legal considerations in the use of LLMs in financial services
  • Privacy and security concerns in the use of LLMs for financial data
  • Bias and fairness considerations in the use of LLMs for financial services

Committees

Program Committee

• Liyuan Chen: Chief Operating Officer of E Fund Management, Chairman of the E Fund FinTech Executive Committee.
• Shuoling Liu: Head of Institute of Innovation at E Fund Management, Secretary-General of E Fund FinTech Executive Committee, Member of the FinTech Special Committee of AMAC.
• Yongpeng Tang: MD, Head of Application Development at E Fund Management, Member of E Fund Fintech Executive Committee, Senior FinTech expert.
• Lu Dai: PhD, Chairman of E Fund Fintech Technical Committee, Senior Fintech expert

Venue

The conference will be held at Doubletree By Hilton Zhuhai Hengqin, Guangdong, China

Contact

All questions about submissions should be emailed to tangfangkai@efunds.com.cn