With the rapid advancement of artificial intelligence technology, big data and AI have become deeply integrated into the financial sector. This integration not only drives innovation in financial education and research but also serves as a key force in building a financial powerhouse and responding to global technological competition. From policy direction to practical application, artificial intelligence is reshaping the development landscape of the financial industry across multiple dimensions.

I. Strategic Significance of AI-Finance Integration

At the national strategic level, General Secretary Xi Jinping explicitly outlined the goal of building a financial powerhouse during the Central Financial Work Conference, emphasizing the need to advance five major initiatives including technological finance and digital finance. The development of digital finance and technological finance heavily relies on AI technology, while a robust financial system serves as the foundation for China to play a leading role in technology and sustainable development.

From a global competition perspective, artificial intelligence has become a core arena for major power rivalry. OpenAI's 2022 launch of ChatGPT marked a critical breakthrough in general artificial intelligence development. This technology rapidly integrated into daily life and industry applications—for instance, Xiamen University has deployed AI teaching assistants and intelligent academic management systems in education, providing reference for technological implementation in finance. Currently, while the United States holds an early advantage in AI hardware and software, China's national mobilization system and market-driven approach have fostered distinctive AI enterprises like Alibaba's Tongyi Qianwen and DeepSeek. Notably, DeepSeek has demonstrated strong technological innovation capabilities by achieving efficient AI computation at relatively low cost through its MOE framework and reinforcement learning framework.

II. Applications of Artificial Intelligence in Core Financial Domains

The core of the financial industry lies in information processing and decision-making. By enhancing information analysis efficiency and assisting complex decision-making, artificial intelligence is driving comprehensive transformation across financial operations. Key applications are concentrated in the following areas:

(1) Supporting Financial Decision-Making and Market Analysis

Financial decisions heavily rely on forecasting and judgment, requiring the processing of massive information volumes within complex, uncertain environments. AI serves as a “quadruple assistant” for decision-making, management, information processing, and analysts, significantly boosting decision efficiency and accuracy:

In capital markets, AI rapidly analyzes policy texts and market data to extract sentiment signals for trading decisions. For instance, after the State Council Financial Stability and Development Committee meeting in March 2022 released favorable policies, AI swiftly identified positive sentiment from meeting content to inform investors. Simultaneously, regulators can leverage AI to anticipate market reactions and optimize policy communication effectiveness.

In traditional financial research, AI overcomes the limitation of “reliance on structured data” by processing unstructured data such as text and social media information. Policy documents and market commentary that previously required time-consuming interpretation by professionals can now be analyzed by AI within seconds, with results matching the precision of professional graduate-level research.

(2) Monitoring Market Sentiment and Corporate Dynamics

Sentiment is a critical factor influencing financial markets. Artificial intelligence, through big data technology, has achieved precision and efficiency in sentiment monitoring:

Market Sentiment Level: Traditional sentiment monitoring relies on telephone surveys, suffering from low response rates and high noise levels. AI, however, can utilize text analysis, lexical methods, and other techniques to construct specialized financial sentiment dictionaries. It generates financial sentiment indices to accurately predict market sentiment and stock market volatility.

Corporate Manager Sentiment Level: The words and decisions of executives at listed companies significantly impact corporate operations. AI can monitor entrepreneur sentiment, particularly irrational states like excessive optimism—research indicates such sentiments often lead to overinvestment, irrational mergers and acquisitions, and ultimately affect profitability. AI-generated sentiment indicators can serve as a counter-monitoring tool for corporate risk surveillance.

(3) Optimizing Policy Communication and Expectation Management

Financial regulation must balance “risk prevention” and “promoting development,” with policy communication and expectation management serving as critical levers. AI applications in this domain primarily manifest in two aspects:

Enhancing Policy Interpretation Efficiency: China's policy information is primarily disseminated through documents and public speeches. Traditional interpretation requires professional involvement and is time-consuming. AI can rapidly analyze central bank monetary policy reports and regulatory documents, summarize policy characteristics, and help investors and the public accurately grasp policy intent. It also assists regulators in anticipating policy impacts on the market. For instance, AI research indicates that new concepts introduced in policy documents may trigger short-term market volatility, necessitating enhanced regulatory explanations to prevent misinterpretation.

Strengthening price forecasting capabilities: Prices are influenced by both objective factors and public perception, exhibiting self-fulfilling characteristics (e.g., the egg-buying frenzy during the Xiamen typhoon). By analyzing narrative information from media reports and social media discussions, AI tracks shifts in public price expectations. The resulting price forecasting models achieve approximately 40% higher accuracy than traditional models, demonstrating particularly strong performance in long-term housing price and medium-to-short-term oil price predictions.

III. Opportunities and Challenges of Artificial Intelligence Reshaping the Financial Industry

(1) Opportunities: Breaking Down Professional Barriers to Advance Inclusive Finance

AI's “emergent intelligence” is breaking down financial expertise barriers. Financial terminology is abstract and logically complex, traditionally requiring graduate-level training to comprehend. Yet current large models have developed preliminary capabilities to handle such complexities, simulating economists' economic forecasting and even exhibiting cognitive and empathetic traits akin to professional brokers.

Simultaneously, AI aids in advancing financial inclusion. By developing specialized financial AI systems, the cognitive barriers to high-threshold financial products can be lowered. For instance, regarding complex products like futures, AI systems can integrate market news and transaction data to generate clear buy/sell recommendations. Concurrently, they can conduct investor education, helping ordinary investors grasp product logic and improve portfolio diversification.

(II) Challenges: Mitigating Technical Risks and Compliance Issues

While empowering finance, artificial intelligence also introduces novel risks, requiring focus on two key areas:

“Artificial Stupidity” and Compliance Risks: Research from the Wharton School reveals that AI assisting capital transactions may autonomously pursue profit maximation through speculative activities or insider trading. This risk stems from algorithmic self-exploration rather than user directives, necessitating enhanced oversight to prevent non-compliance arising from algorithmic self-adjustment.

Data and Signal Risks: AI relies on massive datasets for training, potentially leading to over-sampling issues where “random noise” is mistakenly identified as “market signals,” compromising decision accuracy. Furthermore, general AI systems trained on internet text still fall short of financial professional standards. Fine-tuning techniques and retrieval-enhanced technologies are needed for further optimization.

IV. Development Directions for Financial-Specific AI Systems

To fully leverage AI's value, targeted development of financial-specific AI systems is essential, focusing on core directions including:

Building a Professional Data Foundation: Integrate multi-source data including stock prices, trading volumes, financial metrics, and internet text to establish a financial-specific database exceeding hundreds of millions of entries, providing precise and comprehensive foundational data for AI training.

Enhancing Technical Optimization: Utilizing efficient fine-tuning techniques and retrieval-enhanced generation technologies to improve AI's comprehension of specialized financial issues, thereby narrowing the knowledge gap with financial practitioners and professional researchers.

Focusing on Scenario-Based Applications: Developing functionalities around core scenarios such as investment decision-making, risk monitoring, and investor education. For example, the futures market intelligent analysis system dynamically monitors risks, generates trading strategies, and simultaneously disseminates product knowledge, achieving an integrated “analysis + education + risk control” solution.