China's banks are training massive AI models
China’s largest state-owned and joint-stock banks are embedding AI deeply into their core processes, from audits to credit risk analysis, building models that go far beyond customer service bots
ICBC, CCB, and others now log billions of AI calls annually
Models generate millions of lines of code, credit reports, audit memos
Tech budgets are diverging, with 6 banks cutting back in 2024
Banks shift from experiments to full-stack platforms and commercialization
China’s banking sector is entering a new phase of AI adoption—one defined not by pilot projects or experimental chatbots, but by full integration of large language models into critical business workflows.
According to a recent survey by TMTPost based on 2024 earnings reports, ten of the country’s top banks—including the “Big Six” state-owned banks and leading joint-stock players like Ping An Bank and China Merchants Bank—have rolled out generative AI in hundreds of operational scenarios.
But while some banks are scaling up and even exporting AI tools to smaller institutions, others are dialing back on tech spending. The mixed picture reflects the broader tension facing Chinese banks: how to sustain AI investments while under margin pressure.
From chatbot to credit desk: where AI is really working
ICBC (Industrial and Commercial Bank of China) is a case in point. It reports over 200 AI-powered use cases, logging more than 1 billion AI calls annually.
Applications include algorithmic credit advisors, forex trading assistants, and intelligent risk detection. Its proprietary "Industrial Smart Ocean" model supports workloads once handled by 45,000 employees.
At Bank of China, the DeepSeek R1 model now powers automated coding, internal memos, and business intelligence.
In one month alone, it generated 13.37 million lines of code, with over 3,600 internal users. AI research output has reached 8.7 million words.
CCB (China Construction Bank) has built an enterprise-grade system anchored on DeepSeek-R1 and open-source models. It supports investment analysis, front-line customer service, and employee training.
As of 2024, it launched 168 distinct use cases, with over 7,000 model deployments and 193 scenarios in production. Sixteen core banking processes now rely on AI tools.
China Postal Savings Bank took a different route—focusing on full-stack self-reliance. Its “Youzhi” model suite is entirely controllable in-house, with capabilities extending from code assistants and document audits to intelligent trading bots.
In 2024, it produced 1.1 million lines of code with over 5,000 development helpers, and reduced document generation time by 90%.
Enterprise AI, re-architected
AI isn’t just an app—it’s an architecture. At CCB, engineers built a MaaS (Model-as-a-Service) platform powering all internal models and workflows, spanning everything from credit analysis to knowledge retrieval.