This special session focuses on the cutting-edge research and technological innovation of artificial intelligence and large language models driven financial economic intelligence. The session centers on AI fundamental theories, model architectures, algorithm systems and technical applications in financial scenarios, including but not limited to: large model fine-tuning, representation learning and reasoning mechanisms for financial tasks, multi-modal AI for financial analysis, reinforcement learning and neural network models for quantitative investment, intelligent risk control algorithms, AI-driven credit evaluation and asset pricing, financial forecasting and early warning based on deep learning, trustworthy AI, explainable AI and privacy computing in finance, fintech and regulatory technology empowered by AI, as well as AI governance and risk control for intelligent financial systems.
We welcome high-quality original papers, technical reports and application works that highlight AI algorithm innovation, model optimization, system implementation and technical breakthroughs in financial economic scenarios. By promoting interdisciplinary research and industry–academia collaboration, this session seeks to advance the theoretical development, technical iteration and practical deployment of AI in financial applications, explore key technologies for safe, reliable and interpretable intelligent finance, and boost the technological innovation and sustainable development of AI-driven financial and economic systems.
本次专题论坛聚焦人工智能与大模型前沿技术驱动的金融经济智能化研究，以金融场景下的人工智能基础理论、模型架构、算法体系与技术应用为核心，涵盖范围包括但不限于：面向金融任务的大模型微调、表征学习与推理机制，金融多模态智能分析技术，智能风控算法体系，人工智能驱动的信用评估与资产定价，可信人工智能、可解释性AI与隐私计算在金融领域的应用，人工智能赋能的金融科技与监管科技，以及智能金融系统的AI治理与风险控制。
我们热忱欢迎聚焦金融经济场景下AI算法创新、模型优化、系统实现与技术突破的高质量原创论文、技术报告与应用成果投稿。通过跨学科研究与产学研协同创新，本次论坛致力于推动人工智能在金融领域的理论发展、技术迭代与实践落地，探索安全可靠、可解释的智能金融关键技术，助力人工智能驱动的金融与经济体系技术创新与可持续发展。
Organizer: Dr. Jinshui Huang, Southwest Petroleum University, China
Jinshui Huang, Ph.D. in Management, Lecturer and Master' s Supervisor at Southwest Petroleum University, Member of the Association for Information Systems (AIS). His main research interests include Financial Intelligence, Financial Large Models and AI Agent Construction.
In recent years, he has published 10 papers as the first author in SCI journals and international academic conferences. He presides over 1 project of Fundamental Research Funds for the Central Universities , and serves as a key participant for 2 General Program projects of the National Natural Science Foundation of China. He has deeply participated in national and provincial‑ministerial‑level research projects including the Key R&D Program of the Ministry of Science and Technology of China, ad‑hoc commissioned projects of UNESCO, and special research projects of the People' s Bank of China.He has served as Session Chair for international academic conferences including ICEME 2026, ICDLT 2026 and CCAI 2026. He is a member of the Technical Program Committee (TPC) for artificial intelligence conferences such as ICAIBD, ICBIM, CCAI, ICBDB, ICMLT and ICHMI. Additionally, he works as an anonymous reviewer for top‑tier international management conferences (ICIS, HICSS, PACIS, AMCIS) and multiple SCI/SSCI journals.
Co-organizer: Dr. Xiaoman Jin, Hebei University of Economics and Trade, China
Xiaoman Jin, female, born in 1995, is a Lecturer at Hebei University of Economics and Business. She is a native of Handan, Hebei Province, and holds a Ph.D. in Economics from Southwestern University of Finance and Economics. She serves as a Master’s supervisor and is a member of the China Computer Federation (CCF). She has acted as an anonymous reviewer for multiple SSCI journals and academic conferences. Her long-term research interests focus on the integration of finance and artificial intelligence, particularly the role and impact of emerging AI technologies in capital markets, and innovative perspectives on classic issues in finance. In recent years, She has served as the principal investigator for more than ten provincial and ministerial level research projects.
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