Document Processing · Risk Management · Sales Automation
Postal Savings Bank of China wanted a bank-wide AI PaaS that could coordinate model development, training, and deployment across risk, marketing, operations, and document workflows instead of leaving AI capabilities fragmented by system and department.
Reported impact
Postal Savings Bank of China accelerated approvals, model iteration, and large-scale data processing while giving branches and departments a shared platform for risk, recommendation, document, and AI-native applications.
Postal Savings Bank of China introduced Baidu AI Cloud's AI middle-platform architecture to create 'Postal Savings Brain,' a unified machine-learning platform used across the bank. The platform is backed by the BaiGe Heterogeneous Compute Platform for large-scale training and processing and connects six enterprise data marts plus market and transaction data. Qianfan Large Model Platform adds AI-native functions such as test-case classification, trading assistants, dialogue generation, document analysis, and complaint analysis. The shared environment standardizes data access, modeling, training, and scenario delivery so departments can run independent use cases without duplicating infrastructure. It also supports image-recognition services for reimbursement processing and powers model iteration across credit-card, retail-loan, HR, legal, and financial-market workflows, helping the bank move from isolated models to an integrated 'financial whole-brain' operating model.
Data from six enterprise data marts, market feeds, transaction records, and business-system inputs flows into Postal Savings Brain, where the AI middle platform standardizes data access and modeling workflows. BaiGe provides large-scale training and processing, Qianfan adds generative-AI capabilities, and the resulting models and services feed credit approval, risk-pool modeling, reimbursement image recognition, product recommendation, and other bank applications.
By introducing Baidu AI Cloud's AI middle-platform solution, the bank built Postal Savings Brain, the first bank-wide unified machine-learning platform deployed by a large domestic commercial bank (original: 通过引入百度智能云 AI 中台解决方案，邮储银行构建了国内大型商业银行中首个落地的全行范围统一机器学习平台“邮储大脑”).
Postal Savings Bank wanted to build an AI PaaS 'artificial intelligence brain' to support intelligent applications across the bank's business systems (original: 建设支持全行业务系统智能应用的AI PaaS“人工智能大脑”).
Retail-credit approval processing was compressed from 5 minutes to under 10 seconds and model iteration changed from a 43-day cycle to 10 hours (original: 零售信贷自动化审批判断处理从5分钟压缩至10秒以内…模型迭代时间从43天缩短到10小时).
The AI-platform cluster supports 18 core business systems, 3 supervising departments, 14 supporting business departments, and more than 30 branches (original: AI平台集群支撑18个核心业务系统、3大主管部门、14个支撑业务部门、30余家分行).
The platform connects six enterprise data marts and more than 30 provincial branches for marketing, customer acquisition, and product recommendation applications (original: 对接全行6大数据集市…30+省级分行接入营销获客和产品推荐应用).
With generative large-model capabilities, the platform incubated AI-native applications such as test-case classification, a currency-trading robot, financial dialogue generation, financial document analysis, and complaint analysis (original: 借助生成式大模型能力，孵化出测试用例分类、货币交易机器人、金融领域对话生成、金融领域辅助文档分析、金融领域投诉分析等AI原生应用).
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