Alibaba Cloud Financial Services China Translated from Chinese

Rongshan Technology (融山科技)

Customer Service Automation · Risk Management

Rongshan Technology was tackling highly fragmented personal-loan nonperforming assets with labor-intensive manual teams, low customer reach, and growing compliance exposure. The company needed a full-process AI approach that could automate outreach, understand intent, analyze dialogs for risk, and maintain standardized operations from first contact through repayment and dispute resolution.

daily customer reach improved 20x
repayment recovery improved 5x+
serves 2,000+ enterprise customers

Reported impact

Business outcome

Rongshan transformed collections and dispute-handling into a more standardized AI workflow. Compared with manual operations, daily customer reach improved 20x and repayment recovery improved more than 5x, while the cloud-based quality-control layer reduced compliance exposure and let the business scale without matching growth in headcount.

  • Compliance
  • Productivity Improvement
  • Revenue Growth
  • Risk Reduction

Solution

Rongshan Technology built its asset-disposal workflow on Alibaba Cloud around Tongyi Xiaomi's smart outbound robot and intelligent dialog analysis. The architecture combines Rongshan's own asset-management system, large-model outbound-call system, and data-analysis system with cloud-based AI services so the company can automate outreach, online agreement signing, repayment follow-up, and arbitration mediation in one flow. Digital mediators driven by the outbound robot interpret customer intent, perform emotional analysis, and produce compliant settlement options, while intelligent dialog analysis continuously inspects outbound interactions for quality and regulatory risk. Around those business functions, Rongshan uses an elastic cloud-native deployment model for its AI workloads so compute can scale with asset volume without forcing the company to overbuild fixed infrastructure. The result is an AI-first collections and settlement pipeline rather than a manually stitched process.

Data flow

Asset and case data from the management system is fed into the large-model outbound layer, where Tongyi Xiaomi digital mediators contact borrowers, classify intent, and recommend settlement actions. Call transcripts are then analyzed by the dialog-analysis layer to flag risk, support follow-up, and drive the later steps of signing, repayment tracking, and arbitration mediation.

Solution architecture

4 components · 4 layers
  1. Compute
    • Large-model AI outbound system Runs the model-driven conversation layer used for outreach and negotiation tasks.
  2. Storage
    • Asset-management system Provides the structured case and asset context used by the AI workflow.
  3. Serving
    • Tongyi Xiaomi smart outbound robot Automates outreach and drives the digital mediator interactions used in debt-settlement workflows.
  4. Governance
    • Intelligent dialog analysis Inspects outbound-call data for quality and compliance risk and feeds warnings back into operations.

Architecture clues

  • AI models are deployed in a cloud-native elastic mode so compute can scale with asset volume.
  • Customer intent recognition and emotion analysis are embedded directly into the outbound robot rather than handled offline.
  • Dialog analysis is used as a compliance-control layer over operational calls.
  • The workflow spans outreach, signing, repayment, and arbitration instead of limiting AI to only one collections step.

Evidence from the source

Compared with manual handling, daily customer reach improved 20 times and recovery improved by more than 5 times. (original: 相对人工处置模式,日均客户触达率提升 20 倍,回款率提升 5 倍+。)
Digital mediators can identify customer intent and perform emotion analysis to provide compliant personalized settlement options. (original: 调解数字人能准确捕捉客户意图,并进行情绪分析,从而提供符合法规要求的个性化和解方案。)
Rongshan created the first full-process AI intelligent comprehensive solution in personal-loan nonperforming-asset handling, supported by its own asset system, large-model outbound system, and data-analysis system. (original: 以自研的资产管理系统、大模型 AI 外呼系统、智能数据分析系统为技术支撑,创建了个人贷款不良资产处理领域首个全流程 AI 智能综合解决方案。)
The cloud plus AI architecture covers the whole process from outbound mediation to online signing, repayment tracking, and arbitration mediation. (original: 融山科技实现了从外呼调解到线上签约,再到还款跟踪与仲裁调解的全流程智能化运作。)
Transparency Evidence & methodology 100/100 reference value · 95% 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 value100/100
  • 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 specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence95%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency10/10
    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. Alibaba Cloud

    融山科技基于通义晓蜜打造全流程 AI 智能综合解决方案

    First-party
    https://www.aliyun.com/customer-stories/financial-services-2025-rongshantech
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Region
    • Company size
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • Published on source page Feb 21, 2025 · Alibaba Cloud · First-party
    Open date source
Not separately sourced Industry. Treat these normalized attributes as lower-confidence until a dedicated source is added.