Baidu AI Cloud Financial Services China Translated from Chinese

Industrial Bank Co., Ltd. (兴业银行股份有限公司)

Customer Service Automation

Industrial Bank wanted its digital-service channels to deliver better customer experience, broader self-service coverage, and more precise operations as customers moved online, but legacy service models could not scale personalized support or reduce dependence on human agents.

Average daily handled volume reached nearly 100,000 calls.
Customers can complete business handling in 350+ voice scenarios.
During the pandemic, the intelligent service handled 80% of online business.
First-contact resolution reached 88.18%.

Reported impact

Business outcome

Industrial Bank raised automation coverage and service quality, kept banking services running during pandemic restrictions, and turned intelligent service into a broader platform for remote operations and precision marketing.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Industrial Bank and Baidu AI Cloud built a new-generation intelligent customer-service platform spanning voice, outbound service, and online banking interactions. Baidu AI Cloud Intelligent Customer Service serves as the delivery layer for business handling and consultation, while Intelligent Speech and Smart Outbound Calling expand the bank's reach across voice channels. The implementation uses speech recognition, speech synthesis, natural-language processing, multi-turn dialogue management, emotional analysis, and visual transaction handling so simple standardized tasks can be completed by the robot and disputed or complex cases can transfer to agents. The source highlights more than 350 voice-service scenarios and differentiated service strategies for different regions and customer groups, including an elder-friendly 'just say it' mode during the pandemic. The result converts service conversations into reusable assets for remote banking, digital operations, and precision marketing.

Data flow

Customer calls and digital-service requests enter the intelligent customer-service platform, where speech and NLP services interpret requests, route simple tasks through automated handling flows, and transfer disputed cases to human agents. The resulting conversation data is reused to refine scenarios, support remote-banking operations, and expand precision service coverage.

Solution architecture

3 components · 2 layers
  1. Compute
    • Intelligent Speech Provides speech recognition and synthesis for voice banking, intent recognition, and differentiated audio guidance.
  2. Serving
    • Baidu AI Cloud Intelligent Customer Service Runs the bank's self-service handling and consultation workflows across digital customer-service channels.
    • Smart Outbound Calling Extends the bank's service reach with automated outbound contact and guided interactions.

Architecture clues

  • Core capabilities include visual transaction handling, multi-turn dialogue management, and emotion analysis
  • The bank introduced differentiated service strategies for different cities and for older customers through a dedicated voice interaction mode
  • The deployment follows a human-machine collaboration model in which simple standardized tasks are handled by the robot and complex disputed cases transfer to staff

Evidence from the source

By the end of the first quarter of 2022, daily handled volume was nearly 100,000 calls, intent recognition reached 93.87%, speech transcription reached 93.34%, and first-contact resolution reached 88.18% (original: 截至2022年一季度末,兴业银行智能客服业务办理量日均近10万通,意图识别准确率达93.87%,语音转译准确率达93.34%,问题一次解决率达88.18%).
Customers can complete business handling through voice interaction in more than 350 scenarios (original: 客户通过语音交互可完成业务办理的场景超过350个).
During the pandemic, the intelligent customer service handled 80% of online business and provided differentiated services in places such as Shanghai and Changchun (original: 疫情期间,兴业银行智能客服处理了80%的在线业务…在上海和长春,客户拨打服务热线时会听到智能语音的专属问候和业务办理引导).
Industrial Bank and Baidu AI Cloud explored a new-generation intelligent customer-service robot and upgraded the bank's service to proactive, intelligent, and omnichannel capabilities (original: 兴业银行携手百度智能云推出新一代智能客服机器人…实现主动式、智能化、全渠道能力升级).
The first phase introduced visual handling, multi-turn dialogue management, and sentiment analysis under a human-machine collaboration model in which simple standard business is handled by the robot and complex disputes are transferred to people (original: 围绕“简单标准业务智能机器人办理,复杂争议业务转人工处理”的人机协作模式,研发上线了可视化办理、多轮对话管理、情感分析等功能).
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
    4 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

    Industrial Bank Co., Ltd. (兴业银行股份有限公司) customer story

    First-party
    https://cloud.baidu.com/customer/case/voice-robot.html
    Source type
    Vendor-published customer story
    Published
    Jun 30, 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 Jun 30, 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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