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Smart Finance Joint Report: Working with AI, Winning the Future
On January 20, 2018, the 2nd China Financial Technology Innovation Conference took place in Beijing, hosted by the China Cyberspace Security Association, Financial Technology Innovation Alliance, and Industrial and Commercial Bank of China. The event was also supported by tech giants such as Tencent, Baidu, and IBM. During the conference, several key reports were released, including the "Intelligent Financial Joint Report: Advancing with AI, Winning the Future," which highlighted the growing role of artificial intelligence in shaping the future of finance.
Leading financial institutions like ICBC, CCB, ABC, and Guotai Junan Securities, along with research centers such as the Intelligent Finance Technology Center at Tsinghua University’s Interdisciplinary Information Institute, showcased their latest innovations in financial technology. One of the standout projects was the intelligent investment management engine developed by Tsinghua University's team — a high-performance system designed to optimize decision-making in financial markets.
The project, led by Yao Zhizhi, has been evolving since the establishment of the Tsinghua Financial Technology Center in April 2016. By December 2017, the center was upgraded to the Tsinghua Financial Technology Research Institute, and it even launched its own company, Fortune Engine Technology Co., Ltd., to support industry collaboration and technological development.
In addition, the institute partnered with Ant Financial in 2016 to establish the world’s first financial technology laboratory. This initiative led to the formation of a global alliance involving institutions from Tsinghua University, Princeton University, Korea Advanced Institute of Science and Technology, and the Higher Business School of Northern France. These collaborations have focused on financial market simulation, algorithm development, and system design.
Tsinghua researchers emphasized that professional investors rely heavily on risk management, with advanced technologies covering over 85% of U.S. financial institutions. To address this, the university’s system aims to solve critical questions: “What to invest in? Why to invest? How to invest?â€
The technical architecture integrates big data platforms, combining traditional statistical methods with modern machine learning techniques. This dual approach ensures both strong analytical power and model interpretability, which is crucial for financial applications.
At the application level, the system supports risk management for securities companies, public funds, institutional investors, and insurance firms. It also enables more efficient asset-liability management for pension funds and other large-scale financial entities. As one representative noted, "Modern brokers need a full-service risk management engine — without it, they can't survive."
For example, in the U.S., every brokerage relies on a robust risk engine. A real-world case involved KCG, a firm that collapsed after a programming error led to massive losses. This highlights the importance of having a reliable core system for managing risk across all business lines.
The system includes tools like the "MSG Financial Market Simulator," which helps understand market dynamics and conduct joint risk modeling. With new regulations breaking the "rigid redemption" model, the need for accurate, scientific risk analysis has never been greater.
Tsinghua's approach involves deep quantitative analysis of assets, identifying internal risk drivers such as macroeconomic factors, industry trends, and company-specific issues. They have compiled thousands of risk indicators to better understand market movements.
By using machine learning, the system can identify the most relevant risk factors affecting the market. Combined with statistical models, it provides a comprehensive view of income risks across the entire financial system.
As one expert put it, "80% of market risk changes are quantifiable." This insight drives the decision-making optimization engine, which not only enhances investment strategies but also improves risk control and user profiling.
The user portrait system goes beyond basic demographics, leveraging behavioral data and unsupervised learning to predict micro-level actions. This allows for more personalized investment services and better customer engagement.
Ultimately, Tsinghua's intelligent investment engine represents a significant step forward in the integration of AI and financial technology, offering a powerful tool for both institutional and individual investors.