In the rapidly evolving landscape of financial technology, large language models (LLMs) are poised to revolutionize the banking industry in unprecedented ways. At the recent National Wealth Development Research Cooperation Platform 2025 Spring Summit, China Merchants Bank Chief Information Officer Zhou Tianhong shared valuable insights on how AI finance solutions will reshape banking operations and customer experiences.
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Banking as an Information Processing Industry
Zhou emphasized that banking, at its core, is an information processing industry that manages and analyzes financial data. This perspective opens vast opportunities for AI finance applications to transform traditional banking models.
“Large language models have incredible potential to reshape our industry’s fundamental operations,” Zhou explained during his presentation on “Artificial Intelligence and the Future of Finance.”
Four Major Impacts of AI Finance on Banking
1. Personalized Service Models
Traditional banking has historically focused on high-value clients due to limited human resources. With AI finance tools powered by LLMs, banks can now deliver truly personalized services to every customer, regardless of their portfolio size.
“The democratization of personalized financial services through AI finance solutions will allow us to create thousand different experiences for a thousand different customers,” Zhou noted. This shift marks a dramatic departure from the tiered service models that have dominated banking for decades.
2. AI-Human Collaborative Work Environment
The banking sector has traditionally been labor-intensive, but LLMs are changing this paradigm. AI finance assistants can now handle routine, repetitive tasks, enabling human employees to focus on more complex and strategic responsibilities.
Zhou clarified that human employees will remain essential: “In banking, human employees are irreplaceable for conveying service warmth, planning complex operational tasks, monitoring service quality, and most importantly, taking responsibility for business processes.” The future lies in effective collaboration between human employees and AI finance tools.
3. Enhanced Customer Interaction Models
China Merchants Bank processes over 95% of its transactions through mobile banking, making the user interface critically important. Traditional graphic-based interfaces often create navigation challenges, especially for accessing less frequently used features.
“LLMs have allowed us to evolve from purely graphical interfaces to a hybrid ‘graphics + chat’ interaction model,” Zhou explained. This AI finance approach significantly improves user experience by allowing natural language queries to navigate complex banking functions.
4. Democratized Data Analysis
Traditional banking data analysis has been hindered by high technical barriers, inefficient processes, and limited interactivity. AI finance solutions powered by LLMs dramatically lower these barriers.
“The data agent we’ve developed at China Merchants Bank can understand the specific data problems our employees need to solve,” Zhou shared. “It can process our entire data warehouse and respond to business analytics needs in real-time.” This transformation makes sophisticated financial data analysis accessible to all employees, not just specialized analysts.
The Future of AI Finance in Banking
As financial institutions continue to integrate LLMs into their operations, we can expect to see a fundamental shift in how banking services are delivered and experienced. The combination of personalized service, AI-human collaboration, intuitive interfaces, and democratized data analysis will create more efficient and customer-centric banking environments.
For financial institutions looking to stay competitive, embracing AI finance technologies isn’t just an option—it’s becoming a necessity. As Zhou’s insights demonstrate, the banks that successfully leverage these technologies will be positioned to deliver superior service while optimizing operational efficiency.
