For years, the success of a Chinese banking app was measured by its utility—how quickly a user could transfer funds or pay a bill. However, as 2026 marks a pivotal shift in the sector, the focus is moving from basic functionality toward the nebulous territory of artificial intelligence trust. At a recent industry summit in Shanghai, Analysys Qianfan unveiled the 'Three-Degree User Experience Management Standard 2.0,' a framework designed to codify exactly what makes an AI-driven financial service 'good.'
The financial sector has reached a saturation point where nearly 22% of all registered large language models in China are now deployed within banking and securities apps. With 68% of these models integrated into both front-end and back-end operations, traditional metrics like page-load speed and button placement have become insufficient. The industry now requires a dual-layered evaluation architecture that separates general app stability from the specific, value-added performance of AI assistants.
This new standard introduces 15 quantitative indicators to measure 'Value, Perception, and Trust.' It specifically addresses the pain points of the AI era: Does the AI provide actionable investment advice? Is the human-machine interaction seamless? Most importantly, can the user trust the machine with core financial decisions? The framework is no longer just a retrospective report card but a forward-looking tool designed to guide product iteration before a single line of code is finalized.
Accompanying the new standards are two specialized AI 'Expert Agents' designed to automate the diagnostic process. In a live demonstration, one agent analyzed a wealth management interface and identified twelve critical experience flaws within minutes—a task that previously required weeks of manual audit by human experts. This automation signifies a transition toward 'continuous evaluation,' where financial institutions can test AI reliability and risk transparency in real-time throughout the product lifecycle.
This shift reflects a broader trend in China’s digital economy, where the focus on 'stock user management' has replaced the 'growth at all costs' era. As regional data from East China suggests, retaining sophisticated users now depends on high-quality, personalized AI interactions. For the global fintech industry, China’s move to standardize AI value may serve as a blueprint for how financial institutions navigate the complex trade-off between automated efficiency and the preservation of consumer trust.