Golden Top Think Tank | Zhang Xiaoyan: "Fifteenth Five-Year Plan" Opens New Situation, Financial Industry Faces New Opportunities to Enhance Service Quality and Efficiency

CNR Beijing, March 12 — During the 2026 National Two Sessions, many representatives and committee members discussed topics such as “Deepening the Implementation of Special Actions to Boost Consumption” and “Development Trends of Large-Scale Financial Models.” In response, Zhang Xiaoyan, Vice Dean of Tsinghua University PBC School of Finance and Chair Professor of Finance, was interviewed by CNR. Zhang pointed out that during the 14th Five-Year Plan, the financial technology industry will undergo comprehensive upgrades, ushering in systemic reforms and new growth opportunities.

Financial Industry Should Launch Initiatives to Boost Consumption

Zhang believes that the financial industry should focus on key policy implementation points, forming a progressive path from product innovation and scene development to ecological construction.

In terms of product innovation, it is important to align fiscal policies with industrial development. Financial institutions can leverage policy benefits such as expanded interest subsidies to online consumer credit, timely incorporating relevant consumer credit products into subsidy schemes, and designing “trade-in loans” that directly deliver subsidies to consumers, simplifying application processes.

Regarding scene development, the focus should be on “deep cultivation” to expand consumption scenarios. Under policies encouraging increased income for low-income groups and staggered vacations, develop scene-based solutions like “Flexible Employment Guarantee Loans.” Using lightweight digital financial tools, activate consumption potential in county and rural markets, truly reaching areas with demand.

In ecological construction, financial institutions should upgrade from single credit supply to strategic collaboration within a “policy + finance + technology” ecosystem. Utilizing AI and big data, achieve automatic calculation and disbursement of policy subsidies, provide credit management and consumption planning services, and guide rational, high-quality consumption.

Zhang emphasized the importance of remaining vigilant against excessive debt risks to ensure the sustainability of credit, thereby truly achieving policy goals that enable residents to “consume, dare to consume, and be willing to consume.”

AI Will Achieve a Qualitative Leap in Financial Applications

Regarding the application and development trends of large models in finance, Zhang predicts that AI will undergo a qualitative leap in the financial sector.

Currently, AI applications in banking mainly focus on “internal operations, intelligent customer service, risk control, and compliance” to improve efficiency. In the future, large models will play a significant role in enhancing financial services in three key areas: personalized wealth management, autonomous intelligent agents, and real-time dynamic pricing.

Looking ahead, there will be “tailored” intelligent financial advisors for individual users. AI will no longer just give advice but will automatically adjust assets with user authorization. Ordinary investors will also be able to enjoy exclusive investment banking services at very low costs.

Meanwhile, AI will evolve from a “supporting tool” to an independent intelligent agent. Future complex financial operations will no longer require humans to operate across various systems. Multiple AI agents will automatically coordinate—opening letters of credit, reviewing customs documents, hedging exchange rate risks—running through the entire process seamlessly, achieving truly uninterrupted financial workflows.

Additionally, core risks of credit and insurance products will be dynamically priced in milliseconds. Future AI will capture real-time data such as supply chain information, social media sentiment, and even satellite images, making risk pricing highly sensitive and precise.

Zhang also warned about the risks of excessive AI application. She pointed out that “AI hallucinations” could trigger new systemic risks through micro-decision errors and macro herd effects.

She emphasized that “AI talking nonsense seriously” could lead to mistakes in fundamental financial decisions. Since large models are essentially probabilistic predictors, they may generate false data. If hallucinations cause AI to falsely report high profits for severely loss-making companies, systems might automatically issue large loans based on this, leading to bad debts once problems emerge. Homogenization of models could also trigger deadly “herd effects.” If major banks’ underlying models are similar, collective hallucinations could cause multiple institutions to simultaneously sell or withdraw loans, instantly triggering liquidity crises.

To prevent systemic risks caused by AI hallucinations, Zhang suggests that China needs to build a systemic governance framework covering technological management, regulatory oversight, and industry standards.

On the technical side, efforts should be made to improve the reliability and verifiability of generated content by optimizing training data quality, introducing retrieval-augmented generation (RAG) techniques, and strengthening fact-checking mechanisms to reduce false information, while also enhancing interpretability of key models.

Regulatory measures should establish risk classification management for generative AI, imposing stricter access and evaluation standards in high-risk fields such as finance, healthcare, and public governance. Models should undergo safety assessments and compliance filings before deployment.

Industry-wise, companies should develop algorithm governance and model auditing mechanisms, improve industry standards, and use technologies like digital watermarks and content labeling to enhance traceability of generated content.

Financial Technology Industry Will Enter a New Phase of Upgrading

2026 marks the beginning of the 14th Five-Year Plan. Zhang predicts that during this period, the fintech industry will undergo a comprehensive upgrade, experiencing systemic reforms and new growth opportunities.

First, fintech will evolve from a tool assisting humans to an “autonomous intelligent agent” capable of handling complex tasks independently, fundamentally reshaping operations across the front, middle, and back offices of finance.

Second, the integration of fintech with the real economy will deepen. Future fintech will serve key areas such as inclusive finance, green finance, tech finance, and pension finance, leveraging digital technologies to improve service accessibility and precision, and promoting more effective allocation of financial resources to vital sectors of the economy.

Third, greater emphasis will be placed on security and governance capabilities. As AI becomes more widely used in finance, issues like algorithm transparency, model risk management, and data security will attract more attention. Regulatory bodies are likely to further improve relevant systems, forming a supervisory framework centered on risk assessment, technical audits, and continuous monitoring.

In new business models, “Intelligent Data-Driven Finance” will give rise to a complete “data assetization” industry chain. High-quality data sets, as the “fuel” for large models, will gain strategic importance. Under this trend, “data assetization” will become an independent sector—through data rights confirmation, pricing, and circulation mechanisms, transforming dynamic enterprise data into assessable, pledgeable assets. Innovative models like “computational data asset trusts” have already emerged, and public data products such as meteorological and statistical data are accelerating maturity. In the future, risk assessment data, clinical diagnostic data, and other fields will rapidly develop driven by market demand.

Zhang stated that AI agents will become “new colleagues” in financial decision-making, pushing services from “digitization” toward “intelligent digitization.” AI will shift from “support” to “authorization,” with these systems no longer just summarizing reports but acting as “semi-autonomous digital colleagues” integrated into core processes—handling routine transactions, compliance checks, and even providing customized services directly to investors under human supervision.