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人工智能如何改变金融生态系统
The new physics of financial services
金融服务新物理学
As artificial intelligence (AI) significantly changes the traditional operating models of financial institutions—shifting strategic priorities and upending the competitive dynamics of the financial ecosystem—how can financial institutions better embrace AI and prepare themselves for the future?
随着人工智能（AI）显著改变了金融机构的传统运营模式，改变了战略重点，颠覆了金融生态系统的竞争态势，金融机构如何才能更好地拥抱AI，为未来做好准备？
World Economic Forum (Forum) and Deloitte Global’s latest report studies the strategic, operational, regulatory, and societal implications of AI on the financial services industry to elucidate previously sensationalized debates and help the industry look forward.
世界经济论坛（Forum）和德勤全球（Deloitte Global）的最新报告研究了人工智能对金融服务业的战略、运营、监管和社会影响，以阐明先前耸人听闻的辩论，并帮助该行业向前看。
The report finds that artificial intelligence is changing the physics of financial services, weakening the bonds that have held together the component parts of incumbent financial institutions and opening the door to entirely new operating models.
报告发现，人工智能正在改变金融服务的物理机制，削弱维系现有金融机构组成部分的纽带，并为全新的运营模式打开大门。
The report highlights nine key findings that describe the impact.
该报告强调了描述影响的九个关键结论。
From cost center to profit center: Institutions will turn AI-enabled back-office operations into external services, both accelerating the rate at which these capabilities improve and necessitating others to become consumers of those capabilities to avoid falling behind
从成本中心到利润中心：机构将把支持人工智能的后台业务转变为外部服务，既加快了这些能力的提高速度，又迫使其他人成为这些能力的消费者，以避免落后
A new battlefield for customer loyalty: As past methods of differentiation erode, AI presents an opportunity for institutions to escape a "race to the bottom" in price competition by introducing new ways to distinguish themselves to customers
顾客忠诚的新战场：随着过去的差异化方法逐渐消失，人工智能为机构提供了一个机会，通过引入新的方法来向顾客区分自己，从而避免价格竞争中的“竞争到底部”
Self-driving finance: Future customer experiences will be centered around AI, which automates much of customers’ financial lives and improves their financial outcomes
自驱动金融：未来的客户体验将以人工智能为中心，人工智能将使客户的大部分财务生活自动化，并改善他们的财务成果
Collective solutions for shared problems: Collaborative solutions built on shared datasets will radically increase the accuracy, timeliness, and performance of non-competitive functions, creating mutual efficiencies in operations and improving the safety of the financial system
共享问题的集体解决方案：基于共享数据集的协作解决方案将从根本上提高非竞争性功能的准确性、及时性和性能，在运营中创造相互效率，并提高金融系统的安全性
Bifurcation of market structure: As AI reduces search and comparison costs for customers, firm structures will be pushed to market extremes, amplifying the returns for large-scale players and creating new opportunities for niche and agile innovators
市场结构的分歧：随着人工智能降低了客户的搜索和比较成本，企业结构将被推向市场极端，扩大了大型企业的回报，并为利基和敏捷的创新者创造了新的机会
Uneasy data alliances: In an ecosystem where every institution is vying for diversity of data, managing partnerships with competitors and potential competitors will be critical but fraught with strategic and operational risks
不稳定的数据联盟：在一个每个机构都在争夺数据多样性的生态系统中，管理与竞争对手和潜在竞争对手的合作关系将是至关重要的，但也充满了战略和运营风险
The power of data regulators: Regulations governing the privacy and portability of data will shape the relative ability of financial and non-financial institutions to deploy AI, thus becoming as important as traditional regulations to the competitive positioning of firms
数据监管机构的力量：管理数据隐私性和可移植性的法规将塑造金融机构和非金融机构部署人工智能的相对能力，从而对企业的竞争定位变得与传统法规一样重要
Finding a balanced approach to talent: Talent transformation will be the most challenging speed limit on institutions’ implementations of AI, putting at risk the competitive positioning of firms and geographies that fail to effectively transition talent alongside technology
寻找一种平衡的人才方法：人才转型将是机构实施人工智能最具挑战性的速度限制，这将危及那些未能有效地将人才与技术结合起来的公司和地区的竞争定位
New ethical dilemmas: AI will necessitate a collaborative re-examination of principles and supervisory techniques to address the ethical gray areas and regulatory uncertainties that reduce institutions’ willingness to adopt more transformative AI capabilities
新的道德困境：人工智能将需要对原则和监管技术进行合作性重新审查，以解决道德灰色区域和监管不确定性，这些不确定性降低了机构采用更具变革性人工智能能力的意愿
The report describes key AI-enabled strategies that are substantiated with real world examples, as well as identifies core institutional and broader ecosystem challenges and uncertainties that need to be addressed.
该报告描述了主要的人工智能战略，这些战略得到了现实世界实例的证实，并确定了需要解决的核心机构和更广泛的生态系统挑战和不确定性。
Read the report to learn how AI can transform your business.
阅读报告，了解人工智能如何改变你的业务。
gx-fsi-ai-wef-summary.pdf
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