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人工智能实践:构建智能、信任驱动型银行的实用指南.pdf

上传人: 芦苇 编号:1146540 2026-02-14 40页 1.50MB

1、1AI IN ACTION:AI IN ACTION:A PRACTICAL GUIDE TO A PRACTICAL GUIDE TO BUILDING INTELLIGENT,BUILDING INTELLIGENT,TRUSTTRUST-DRIVEN BANKINGDRIVEN BANKINGXXWHAT WELL COVER TODAY2REBUILDING TRUST THROUGH INTELLIGENT TRANSFORMATIONTURNING DATA CHAOS INTO AI CLARITYBUILDING RESPONSIBLE AIAI IN ACTIONTHE AI

2、 READINESS FRAMEWORKREBUILDING TRUST THROUGH REBUILDING TRUST THROUGH INTELLIGENT TRANSFORMATIONINTELLIGENT TRANSFORMATION3When trust slips,deliver proof.Create value people feel,beat the challengers and keep pace with a tightening regulatory map.The Trust Gap in BankingProve Value,Deliver ResultsTh

3、e New Stack of RivalsRegulators Are WatchingTHE MARKET IS RESHAPING MODERN BANKING5HOW BANKS LOSE TRUST WITH CUSTOMERS AND PROSPECTSConfusing terms/fine printOverpromising in ads vs whats available at account openingData breaches or fraud mishandlingPromoting products the customer already hasDeliver

4、ing promotions for products the customer wont qualify forPoor problem resolutionHidden fees and teaser ratesHard-to-reach support6While seemingly small,these actions communicate that my bank doesnt really know me or care about me,degrading trust.7W I N N I N G B AC K T R U S T T H R O U G H P R O O

5、Fomnichannel experiences+brandSALES+MARKETINGContext+ConsentSegmentation,triggers,compliance,first-party signal use.DATAAIPersonalized ExperiencesContent generation,offer optimization,journey automationunified,governed,real-time customer datapredictive/LLM intelligence+decisioningTrusted,Personalize

6、d BankingCustomer IntelligenceIdentity resolution,next-best action,risk+propensity models.Trust improves when every interaction proves we understand context,respect consent,and deliver valuethis only happens when Data,AI,and Marketing operate as one system.Turning silos into SystemsWHEN WE UNDERSTAN

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1. **重建信任**:银行因数据混乱、过度承诺、数据泄露等问题失去客户信任,需通过全渠道体验、个性化服务及统一数据系统(数据、AI、营销一体化)重建信任。 2. **AI就绪框架**: - **数据基础**:33%银行领导者认为“无法有效使用数据”是最大技术挑战,需先整理去重数据。 - **领导力**:建立问责制、AI素养及高管反馈机制,确保AI与战略、文化对齐。 3. **负责任AI**:采用模块化设计(可复用组件)、政策即代码,通过可测量信任(伦理前置、持续监控)实现规模化。 4. **实践挑战**:仅5%的生成式AI试点产生商业回报,需“快速失败+迭代”,避免碎片化建设。 5. **未来趋势**:从描述性分析向预测性、指导性分析演进,对话式智能将取代传统应用。
**信任如何重建?** **数据如何变AI?** **AI如何负责?**
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