Baidu AI Cloud Financial Services China Translated from Chinese

Shanghai Pudong Development Bank

Customer Service Automation

Shanghai Pudong Development Bank needed a scalable, contactless way to deliver personalized wealth-management guidance and everyday banking service to a very large retail customer base without forcing customers to wait for human staff.

If Xiaopu serves 10,000 customers per day for 20 minutes each, it saves 3,333 labor hours per day.
The same scenario saves about 416 person-days per day and nearly 10,000 person-days per month.
Voice-based Xiaopu customer service handled more than 80% of total service volume.
Xiaopu served 80 million customers.

Reported impact

Business outcome

The bank introduced a reusable digital-human service channel that scaled personalized advisory and service interactions, diverted most routine traffic from human agents and created a foundation for broader future-bank automation.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement

Solution

Shanghai Pudong Development Bank and Baidu AI Cloud built Xiaopu as a digital employee that spans the bank app, online banking and service terminals. The implementation starts with a 3D twin of a real wealth manager, using high-precision body capture, voice cloning and text-to-speech so the avatar can speak naturally, while facial-expression recognition and full-duplex speech let customers interrupt and continue multi-turn conversations hands-free. Behind the avatar, Baidu AI Cloud's AI middle-platform and knowledge middle-platform combine user profiles, risk preferences, product knowledge, recommendation logic and accumulated dialogue records into a one-to-one advisory workflow. The system continuously learns from conversation data, improves customer-intent understanding and recommendations over time, and extends the same stack into the bank's broader future-bank concept so digital staff can front sales, service and advisory scenarios across multiple devices.

Data flow

Customer voice and profile inputs from the mobile app, online banking and terminal channels flow into Xiaopu's digital-human interface, where acoustic and semantic analysis call the AI middle platform, user-profile and recommendation services, and the knowledge middle platform returns tailored answers and product suggestions.

Solution architecture

5 components · 4 layers
  1. Compute
    • High-Precision User Profiling Builds individualized customer portraits and preference signals for one-to-one recommendations.
  2. Serving
    • Smart Finance Solution Packages the bank's digital-human service flows for advisory and branch scenarios.
    • Smart Recommendation Engine Matches products and asset-allocation suggestions to each customer's goals and risk tolerance.
  3. Orchestration
    • AI Middle Platform Solution Coordinates speech, semantic analysis and service logic for the digital employee experience.
  4. Governance
    • Knowledge Middle Platform Provides the closed loop from knowledge production to knowledge application for bank service and advisory tasks.

Architecture clues

  • A 3D digital-human twin is built from real-person capture, voice cloning and TTS
  • Recommendation quality improves as conversation data accumulates
  • The knowledge middle platform creates a closed loop from knowledge production to knowledge application
  • The system uses full-duplex voice interaction instead of push-to-talk interaction
  • Xiaopu runs across the bank app, online banking and physical service terminals

Evidence from the source

As the industry's first 3D twin digital human, Xiaopu restores a real wealth manager's appearance, voice, expressions and gestures (original: 作为行业首个 3D 孪生数字人,小浦实际上是真实地还原了一位理财经理的相貌、声音、表情、手势动作).
If Xiaopu serves 10,000 customers a day, the bank can save about 3,333 labor hours per day and nearly 10,000 person-days per month (original: 若小浦服务一位客户的时长为 20 分钟且每天服务 1 万名客户,可每天节约人工 3333 小时……每月节省人力近 1 万人).
The bank's first digital employee, Xiaopu, has already appeared in the app, online banking and various service terminals (original: 目前,浦发银行和百度智能云联合打造的银行业首个数字人员工小浦已经出现在 App、网银和各类服务终端上).
Xiaopu gives users the phone's only full-duplex visual interaction experience and supports multi-turn dialogue after the wake phrase 'Xiaopu, Xiaopu' (original: 小浦提供给用户的是手机端唯一的全双工可视化交互新体验……客户用“小浦小浦”即可唤醒数字人,实现多轮对话).
Xiaopu's voice customer service already accounts for more than 80% of the bank's total customer-service traffic (original: 小浦在全行客服渠道上线,其话务量已占全部话务量八成以上).
Transparency Evidence & methodology 96/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value96/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Baidu AI Cloud

    Shanghai Pudong Development Bank customer story

    First-party
    https://cloud.baidu.com/customer/case/pufa.html
    Source type
    Vendor-published customer story
    Published
    May 16, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page May 16, 2025 · Baidu AI Cloud · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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