Financial Intelligence & Data Science Lab (FinD Lab) WeChat Official Account Is Now Live!
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🌟 1. About the Lab
FinD Lab is part of the Key Laboratory of Intelligent Information Processing, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS). It is also affiliated with the State Key Laboratory of AI Safety, ICT, CAS, Beijing, China. The team has long worked at the intersection of artificial intelligence and financial security.
🚀 Core Focus The team focuses on the central question of how AI can understand and safeguard complex financial systems. Centered on financial risk modeling driven by user online behavior, the group continues to publish high-quality research in top data mining and natural language processing venues, including IEEE TKDE, KDD, WWW, SIGIR, and ACL.
👥 Team Scale The team currently includes one professor, one assistant professor, one postdoctoral researcher, and more than 20 Ph.D. and master’s students. The group maintains an open and collaborative atmosphere. In recent years, multiple members have been selected for talent programs such as the Beijing Outstanding Young Scientists Program, the Beijing Nova Program, and the CAS Youth Innovation Promotion Association.
💼 Real-World Deployment The team’s technologies have been deployed at scale in key institutions and companies, including the Shenzhen Stock Exchange, Ant Group, Alibaba, Tencent, and ByteDance. They have created practical impact in scenarios such as security in digital inclusive finance, securities-market risk monitoring, and transaction fraud prevention.
🔍 2. Research Directions
Driven by major national needs in financial security, the lab focuses on two core questions:
- AI for Financial Security: how to use AI to build stronger capabilities for financial risk perception and prevention.
- AI Safety in Finance: how to ensure the safety and reliability of AI in high-stakes financial scenarios.
On this foundation, the lab has formed three mutually reinforcing research directions:
📍 1. Behavior Data × Knowledge-Driven Intelligent Risk Modeling (Micro to Meso) For real-world financial scenarios characterized by high noise, strong adversarial behavior, and weak labels, the team studies user behavior modeling, multimodal information fusion, and knowledge-enhanced learning to improve individual and group-level risk identification.
📍 2. AI-Driven Risk Reasoning and Cross-Domain Evolution Modeling (Meso to Macro) To understand how risks evolve from signals into events and then spread further, the team studies risk propagation mechanisms, causal reasoning, and situation projection, aiming to build interpretable and simulatable frameworks for macro-level risk analysis.
📍 3. Safety Evaluation and Enhancement of Financial Intelligence Models (Model Level) For problems such as model failure, adversarial attacks, and decision bias in financial scenarios, the team develops systematic safety evaluation frameworks and enhancement mechanisms, helping financial AI move from being merely usable to being trustworthy.
💡 In short, we care not only about whether models are effective, but also whether they are reliable, interpretable, and deployable in the real, complex world.
👨🏫 3. Faculty and Mentors
Prof. Xiang Ao has long worked on financial intelligence, data mining, and natural language processing. He has published more than 100 papers in venues such as The Innovation, IEEE TKDE, KDD, WWW, SIGIR, ACL, and ICLR, including over 50 CCF-A papers. He has been listed among the Stanford University & Elsevier global Top 2% Scientists. He is a CCF Distinguished Member and Distinguished Speaker, and serves as a standing committee member of the CCF Technical Committee on Digital Finance and chair of its Young Scholars Committee. As the first contributor, he received the 2024 CCF Science and Technology Progress Award, Second Prize.
Homepage: https://aoxaustin.github.io/
Dr. Yang Liu works on AI safety, graph machine learning, and their applications. He received his Ph.D. in engineering from ICT, CAS, in June 2023. He has published more than 30 papers in venues such as IEEE TKDE, KDD, WWW, ICLR, and CIKM, with over 1,800 Google Scholar citations. He has led multiple projects, including a Young Scientists Fund from the National Natural Science Foundation of China and a General Program from the China Postdoctoral Science Foundation.
Homepage: https://ponderly.github.io/
Dr. Dingyue Wang works on data governance and data mining. He received his Ph.D. from the Academy for Advanced Interdisciplinary Studies, Peking University. He has published six papers as first author or co-first author in leading international journals, including Molecular Cell, Genome Biology, Plant Cell, and Nucleic Acids Research.
📢 Join Us
FinD Lab welcomes students who are passionate about research. The team is continuously recruiting master’s students and direct-track Ph.D. students interested in financial intelligence, data mining, AI risk governance, and data circulation. Because positions are limited, interested students are encouraged to contact us early and send their CVs to [email protected].
We emphasize problem-driven research, system-building ability, and long-term growth, and encourage students to build research depth through real and complex problems.
- GitHub: https://github.com/ICT-FinD-Lab
- Address: Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Zhongguancun, Haidian District, Beijing, China
- Original WeChat article (Chinese): https://mp.weixin.qq.com/s/agP3z2ziP87NrJIP2ilY9Q
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