Artificial Intelligence in the Financial System
At the 27th Annual Symposium on Building the Financial System of the 21st Century: An Agenda for Japan and the United States, Washington, D.C.
在第 27 届“构建 21 世纪金融体系：日本和美国议程”年度研讨会上，华盛顿特区
Discussions of artificial intelligence (AI) inevitably center on two main points: risks and benefits.
关于人工智能（AI）的讨论不可避免地集中在两个要点：风险和收益。
1 Both of these can be frustratingly vague and amorphous.
1 这两者都可能非常模糊且无定形，令人沮丧。
Proponents of AI project its widespread adoption will be as momentous as the industrial age—radically improving efficiency, increasing labor productivity, and changing the world economy.
人工智能的支持者预计，人工智能的广泛采用将与工业时代一样重要——从根本上提高效率、提高劳动生产率并改变世界经济。
Skeptics largely focus on the risks, noting that it may introduce new and unpredictable variables into the economy and the financial system, including new forms of cyber-risk and fraud.
怀疑论者主要关注风险，指出这可能会给经济和金融体系带来新的、不可预测的变量，包括新形式的网络风险和欺诈。
It would be impossible to predict what the future holds for AI, or how its use and impact will evolve over time.
无法预测人工智能的未来，也无法预测其用途和影响将如何随着时间的推移而演变。
But as the technology continues to mature, as new use cases evolve, and it is rolled out more broadly, we will almost certainly be surprised by how it is ultimately used.
但随着技术的不断成熟、新用例的发展以及更广泛的推广，我们几乎肯定会对它的最终使用方式感到惊讶。
Looking at the financial industry-specific implications of AI, it is helpful to consider not only how it may change the financial system, but also how regulatory frameworks should respond to this emerging technology.
着眼于人工智能对金融行业的特定影响，不仅要考虑它如何改变金融体系，还要考虑监管框架应如何应对这种新兴技术。
Are the existing frameworks sufficient?
现有的框架足够吗？
If not, how can regulators best balance the risks AI may pose to bank safety and soundness and financial stability with the need to allow for continued innovation?
如果不是，监管机构如何才能最好地平衡人工智能可能给银行安全稳健和金融稳定带来的风险与允许持续创新的需要？
Broader availability of generative AI and large language models have created headlines and spiking stock prices, but the financial services sector has been using AI for some time.
生成式人工智能和大型语言模型的广泛应用已经成为头条新闻并飙升股价，但金融服务行业使用人工智能已经有一段时间了。
2 Over time, it has become clear that AI's impact could be far-reaching, particularly as the technology becomes more efficient, new sources of data become available, and as AI technology becomes more affordable.
2 随着时间的推移，人们越来越清楚人工智能的影响可能是深远的，特别是随着技术变得更加高效、新的数据来源变得可用以及人工智能技术变得更加便宜。
Do We Need a Definition of AI?
我们需要人工智能的定义吗？
Before discussing the implications of AI and regulatory policy approaches, we should ask whether we need a definition of AI.
在讨论人工智能和监管政策方法的影响之前，我们应该问我们是否需要人工智能的定义。
As it has advanced, the number and variety of definitions used to define AI have expanded.
随着人工智能的发展，用于定义人工智能的定义的数量和种类也在增加。
3 Some definitions focus on the algorithms—like the use of machines to learn and reason in a way that simulates human intelligence.
3 一些定义侧重于算法，例如使用机器以模拟人类智能的方式进行学习和推理。
Others focus on the outputs—the ability to perform complex tasks normally done by humans.
其他人则关注输出——执行通常由人类完成的复杂任务的能力。
In 2021, an interagency request for information from federal banking regulators, including the Federal Reserve, sought comment on banks' use of AI, but notably avoided using any single definition.
2021 年，包括美联储在内的联邦银行监管机构向机构间索取信息，寻求对银行使用人工智能的评论，但特别避免使用任何单一定义。
Instead, this request listed a few possible use cases, features, and forms.
相反，该请求列出了一些可能的用例、功能和形式。
These included the use of structured and unstructured data; the use of alternative data sources; voice recognition and natural language processing; the algorithmic identification of patterns and correlations in training data to generate predictions or categorizations; and "dynamic updating," where an algorithm has the capacity to update without human intervention.
其中包括使用结构化和非结构化数据；使用替代数据源；语音识别和自然语言处理；对训练数据中的模式和相关性进行算法识别以生成预测或分类；以及“动态更新”，其中算法能够在无需人工干预的情况下进行更新。
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While each definition of AI may serve its own purpose in the context of how it is used, any single narrow definition can be criticized.
虽然人工智能的每个定义都可能在其使用方式方面发挥其自身的作用，但任何单一的狭隘定义都可能受到批评。
A more generic definition runs the risk of oversimplifying the range of activities, use cases, and underlying technology.
更通用的定义存在过度简化活动范围、用例和底层技术的风险。
A definition that captures the variability of AI technology in a more granular way runs the risk of being unwieldy in its length, and obsolete in the short-term as new forms and use cases emerge.
以更精细的方式捕捉人工智能技术的可变性的定义可能会存在冗长且难以操作的风险，并且随着新形式和用例的出现而在短期内过时。
Within this definitional question—of whether and how you define AI—lies a more important policy question: Specifically, for what purpose is a definition required?
在这个定义问题（是否以及如何定义人工智能）中存在一个更重要的政策问题：具体来说，需要定义的目的是什么？
In the context of the financial system, the definition of AI may help to delineate how the regulatory system addresses it and establishes the parameters for how it can be used by regulated institutions.
在金融体系的背景下，人工智能的定义可能有助于描述监管系统如何处理它，并确定受监管机构如何使用人工智能的参数。
Other specific contexts could also be included, like third-party service providers that support banks or other financial services providers, or use by regulators in support of their mandates.
还可以包括其他特定环境，例如支持银行或其他金融服务提供商的第三方服务提供商，或监管机构用于支持其职责的第三方服务提供商。
A definition helps regulators and regulated institutions understand the activities that are subject to rules and requirements by defining the scope.
定义可以通过定义范围来帮助监管机构和受监管机构了解受规则和要求约束的活动。
While this definitional question is important to establish clarity about the scope of what constitutes AI, it can also distract us from a more important point—what is the appropriate policy framework to address the introduction and ongoing use of AI in the financial system?
虽然这个定义性问题对于明确人工智能的范围很重要，但它也可能分散我们对更重要的一点的注意力：解决人工智能在金融系统中的引入和持续使用的适当政策框架是什么？
I have no strong feelings about the ideal or optimal definition of AI, and some version of the many definitions floating around are probably adequate for our purposes.
我对人工智能的理想或最佳定义没有强烈的感觉，并且许多定义中的某些版本可能足以满足我们的目的。
At a minimum though, a definition must establish clear parameters about what types of activities and tools are covered.
但至少，定义必须建立关于涵盖哪些类型的活动和工具的明确参数。
But before leaving the topic, I want to offer a cautionary note.
但在离开这个话题之前，我想提出一个警告。
A broad definition of AI arguably captures a wider range of activity and has a longer "lifespan" before it becomes outmoded, and potentially never becomes outdated.
人工智能的广泛定义可以说涵盖了更广泛的活动，并且在过时之前具有更长的“生命周期”，并且可能永远不会过时。
But a broad definition also carries the risk of a broad—and undifferentiated—policy response.
但宽泛的定义也带来了宽泛且无差别的政策反应的风险。
This vast variability in AI's uses defies a simple, granular definition, but also suggests that we cannot adopt a one-size-fits-all approach as we consider the future role of AI in the financial system.
人工智能用途的巨大变化违背了简单、精细的定义，但也表明我们在考虑人工智能未来在金融体系中的作用时不能采用一刀切的方法。
Innovation and Competitiveness Knowing that the technology and use of AI continues to evolve leads to the question of how it should be viewed by regulators, particularly in light of the need for innovation and the effect on competition.
创新和竞争力 人工智能的技术和使用不断发展，这引发了监管机构应如何看待人工智能的问题，特别是考虑到创新的需求及其对竞争的影响。
Innovation AI tools have the potential to substantially enhance the financial industry.
创新人工智能工具有潜力大幅提升金融业。
In my view, the regulatory system should promote these improvements in a way that is consistent with applicable law and appropriate banking practices.
我认为，监管体系应以符合适用法律和适当银行惯例的方式促进这些改进。
One of the most common current use cases is in reviewing and summarizing unstructured data.
当前最常见的用例之一是审查和总结非结构化数据。
This can include enlisting AI to summarize a single report or to aggregate information from different sources on the same or related topics.
这可以包括利用人工智能来总结单个报告或汇总来自不同来源的相同或相关主题的信息。
The AI "output" in these cases may not directly produce any real-world action, but it provides information in a more usable way to assist a human.
在这些情况下，人工智能的“输出”可能不会直接产生任何现实世界的动作，但它以更可用的方式提供信息来帮助人类。
AI use cases like this may present opportunities to improve operational efficiency, without introducing substantial new risk into business processes.
像这样的人工智能用例可能会提供提高运营效率的机会，而不会在业务流程中引入大量新风险。
In some ways, the joining of AI outputs with a human acting as a "filter" or "reality check" can capture efficiency gains and control for some AI risks.
在某些方面，将人工智能输出与充当“过滤器”或“现实检查”的人类结合起来可以提高效率并控制某些人工智能风险。
Similarly, AI can act as a "filter" or "reality check" on analysis produced by humans, checking for potential errors or biases.
同样，人工智能可以充当人类分析的“过滤器”或“现实检查”，检查潜在的错误或偏见。
AI tools may also be leveraged to fight fraud.
人工智能工具也可用于打击欺诈。
One such use is in combatting check fraud, which has become more prevalent in the banking industry over the last several years.
其中一种用途是打击支票欺诈，这种欺诈在过去几年中在银行业中变得越来越普遍。
In a recent report, the Financial Crimes Enforcement Network noted that from February to August of 2023, there were over 15,000 reports received related to check fraud, associated with more than $688 million in transactions (including both actual and attempted fraud).
金融犯罪执法网络在最近的一份报告中指出，从 2023 年 2 月到 8 月，收到了超过 15,000 份与支票欺诈相关的报告，涉及超过 6.88 亿美元的交易（包括实际欺诈和企图欺诈）。
5 The growth in check fraud over the past several years has caused significant harm not only to banks and the perceived safety of the banking system but also to consumers who are the victims of fraudulent activity.
5 过去几年支票欺诈的增长不仅对银行和银行系统的安全性造成了重大损害，而且对欺诈活动的受害者消费者也造成了重大损害。
The regulatory response to help address this growing problem has unfortunately been slow, lacking in coordination, and generally ineffective.
不幸的是，帮助解决这一日益严重的问题的监管反应缓慢、缺乏协调，而且普遍无效。
Could AI tools offer a more effective way for banks to fight against this growing fraud trend?
人工智能工具能否为银行提供更有效的方法来应对日益增长的欺诈趋势？
We already have some evidence that AI tools are powerful in fighting fraud.
我们已经有一些证据表明人工智能工具在打击欺诈方面非常强大。
The U.S. Treasury Department recently announced that fraud detection tools, including machine learning AI, had resulted in fraud prevention and recovery totaling over $4 billion in fiscal year 2024, including $1 billion in recovery related to identification of Treasury check fraud.
美国财政部最近宣布，包括机器学习人工智能在内的欺诈检测工具在 2024 财年实现了欺诈预防和追偿总额超过 40 亿美元，其中包括与国库支票欺诈识别相关的 10 亿美元追偿金额。
6 While the nature of the fraud may be different in these cases, we should recognize that AI can be a strong anti-fraud tool and provide significant benefits for affected bank customers.
6 虽然这些情况下的欺诈性质可能有所不同，但我们应该认识到人工智能可以成为强大的反欺诈工具，并为受影响的银行客户提供显着的好处。
If our regulatory environment is not receptive to the use of AI in these circumstances customers are the ones who suffer.
如果我们的监管环境不接受在这些情况下使用人工智能，那么遭受损失的将是客户。
AI will not completely "solve" the problem of fraud—particularly as fraudsters develop more sophisticated ways to exploit this technology.
人工智能不会完全“解决”欺诈问题，特别是当欺诈者开发出更复杂的方法来利用这项技术时。
But it could be important if the regulatory framework provides reasonable parameters for its use.
但如果监管框架为其使用提供合理的参数，则可能很重要。
Another often-discussed use case for AI in financial services is in expanding the availability of credit.
人工智能在金融服务中另一个经常讨论的用例是扩大信贷的可用性。
AI is not the first technology with potential to expand access to credit for the "un-" or "underbanked." We have long viewed alternative data as a potential opportunity for some consumers, like those with poor or no credit history but with sufficient cash flow to support loan repayment.
人工智能并不是第一个有可能扩大“无银行账户”或“银行服务不足”人群获得信贷机会的技术。长期以来，我们一直将替代数据视为某些消费者的潜在机会，例如那些信用记录不佳或没有信用记录但拥有足够现金流支持贷款偿还的消费者。
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AI could be used to further expand this access, as financial entities mine more data sets and refine their understanding of creditworthiness.
随着金融实体挖掘更多数据集并完善对信用度的理解，人工智能可用于进一步扩大这种访问范围。
Of course, we also know that using AI in this context—in a way that has more direct impact on credit decisions affecting individual customers—also presents more substantial legal compliance challenges than other AI use cases.
当然，我们也知道，在这种情况下使用人工智能——以一种对影响个人客户的信用决策产生更直接影响的方式——也比其他人工智能用例带来了更实质性的法律合规挑战。
AI also has promise to improve public sector operations, including in regulatory agencies.
人工智能还有望改善公共部门的运营，包括监管机构的运营。
As I have often noted, the data relied on to inform the Federal Open Market Committee decision-making process often is subject to revisions after-the-fact, requiring caution when relying on the data to inform monetary policy.
正如我经常指出的那样，为联邦公开市场委员会决策过程提供信息的数据往往会在事后进行修改，因此在依赖这些数据为货币政策提供信息时需要谨慎。
8 Perhaps the broader use of AI could act as a check on data reliability, particularly for uncertain or frequently revised economic data, improving the quality of the data that monetary policymakers rely on for decision-making.
8 也许更广泛地使用人工智能可以检查数据的可靠性，特别是对于不确定或经常修改的经济数据，从而提高货币政策制定者决策所依赖的数据质量。
Additional data as a reliability check or expanded data resources informed by AI could improve the FOMC's monetary policymaking by validating and improving the data on which policymakers rely.
作为可靠性检查的额外数据或人工智能提供的扩展数据资源可以通过验证和改进政策制定者所依赖的数据来改善 FOMC 的货币政策制定。
While these use cases present only a subset of the possibilities for the financial system, they illustrate the breadth of potential benefits and risks of adopting an overly cautious approach that chills innovation in the banking system.
虽然这些用例仅代表了金融系统可能性的一小部分，但它们说明了采取过于谨慎的方法会带来的潜在好处和风险的广度，这种方法会抑制银行系统的创新。
Over-regulation of AI can itself present risks by preventing the realization of benefits of improved efficiency, lower operational costs, and better fraud prevention and customer service.
对人工智能的过度监管本身就会带来风险，阻碍实现提高效率、降低运营成本以及更好的欺诈预防和客户服务等效益。
Effect on Competition The regulatory approach and framework can also promote competition in the development and use of AI tools in the financial system.
对竞争的影响 监管方法和框架还可以促进金融系统中人工智能工具的开发和使用的竞争。
An overly conservative regulatory approach can skew the competitive landscape by pushing activities outside of the regulated banking system or preventing the use of AI altogether.
过于保守的监管方法可能会将活动推向受监管的银行系统之外或完全阻止人工智能的使用，从而扭曲竞争格局。
Inertia often causes regulators to reflexively prefer known practices and existing technology over process change and innovation.
惰性常常导致监管机构本能地偏爱已知实践和现有技术，而不是流程变革和创新。
The banking sector often suffers from this regulatory skepticism, which can ultimately harm the competitiveness of the U.S. banking sector.
银行业经常遭受这种监管怀疑，这最终可能损害美国银行业的竞争力。
In the United States, we often think about the financial system based on the regulatory "perimeter." We view institutions within the scope of federal banking regulation (banks and their affiliates) as being "in the perimeter," while entities that operate under other regulatory frameworks (including money transmitters licensed under state law) are "outside the perimeter." Of course, the global financial system includes institutions that operate on a cross-border basis, and tools and approaches often permeate throughout the financial system once they have been deployed successfully in some other part of the system.
在美国，我们经常思考基于监管“边界”的金融体系。我们将联邦银行监管范围内的机构（银行及其附属机构）视为“外围”，而在其他监管框架下运营的实体（包括根据州法律许可的货币转移机构）则视为“外围”。当然，全球金融体系包括跨境运作的机构，工具和方法一旦在系统的其他部分成功部署，往往会渗透到整个金融体系。
But we know that the regulatory perimeter is permeable, and there is always the risk that activity pushed outside the perimeter can transmit risk back into the system even as the activities garner less scrutiny and regulation than banks.
但我们知道，监管范围是可渗透的，而且总是存在这样的风险：被推到范围之外的活动可能会将风险传递回系统中，即使这些活动受到的审查和监管少于银行。
Put differently, the overly conservative approach may present only a façade of safety, masking underlying risks to the financial system and those who rely on it.
换句话说，过于保守的做法可能只是表面上的安全，掩盖了金融体系及其依赖者面临的潜在风险。
Of course, there are risks to being overly permissive in the AI regulatory approach.
当然，人工智能监管方式过于宽松也存在风险。
As with any rapidly evolving technology, supervision of its use should be nimble.
与任何快速发展的技术一样，对其使用的监管应该灵活。
Its users must make sufficient risk-management and compliance investments to conduct activities in a safe and sound manner, and in accordance with applicable laws and regulations.
其用户必须进行足够的风险管理和合规投资，以便按照适用的法律法规安全、稳健地开展活动。
While the banking system has generally been cautious and deliberate in its AI development and rollout, others have not.
尽管银行系统在人工智能开发和推广方面普遍持谨慎和深思熟虑的态度，但其他系统却没有。
When left improperly managed and unmonitored, it can result in unintended outcomes and customer harm.
如果管理不当和不受监控，可能会导致意想不到的结果和客户伤害。
For example, certain generative AI models have been known to generate nonsensical or inaccurate outputs, sometimes called "hallucinations." In some cases, AI hallucinations have not involved significant harm, for example when discovered in a testing environment without customer-facing implications.
例如，已知某些生成式人工智能模型会生成无意义或不准确的输出，有时称为“幻觉”。在某些情况下，人工智能幻觉并没有造成重大损害，例如在测试环境中发现时，没有面向客户的影响。
The Sufficiency of Existing Regulatory Tools While helpful to acknowledge the risks of over-regulation and under-regulation, we must understand our currently regulatory stance—what tools do we have to promote AI's benefits while helping to mitigate the risks?
现有监管工具的充分性虽然有助于承认监管过度和监管不足的风险，但我们必须了解我们目前的监管立场——我们拥有哪些工具来促进人工智能的好处，同时帮助减轻风险？
To address this topic, we should widen the lens to consider our approach to innovation broadly.
为了解决这个话题，我们应该拓宽视野，广泛地考虑我们的创新方法。
Supporting innovation in the financial system can and should apply to the introduction and use of AI.
支持金融体系创新可以而且应该适用于人工智能的引入和使用。
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When we consider AI risks, many of these are already well-covered by existing frameworks.
当我们考虑人工智能风险时，现有框架已经很好地涵盖了其中许多风险。
For example, AI often depends on external parties—cloud computing providers, licensed generative AI technologies, and core service providers—to operate.
例如，人工智能通常依赖于外部各方——云计算提供商、获得许可的生成式人工智能技术和核心服务提供商——来运营。
AI can also pose model risks in the banking context, with associated data management and governance concerns.
人工智能还可能在银行业环境中带来模型风险，以及相关的数据管理和治理问题。
AI can also impact a bank's cyber-resiliency as AI-related fraud tools become more widespread and more anti-fraud tools become available.
随着与人工智能相关的欺诈工具变得更加普遍，并且有更多的反欺诈工具可用，人工智能还会影响银行的网络弹性。
While AI may be on the frontier of technology, it does not operate outside the existing legal and regulatory framework.
虽然人工智能可能处于技术前沿，但它不会在现有法律和监管框架之外运作。
AI is not exempt from current legal and regulatory requirements, nor is its use exempt from scrutiny.
人工智能不能免除当前的法律和监管要求，其使用也不能免受审查。
This is particularly true with AI; its use must comply with current laws and regulations, including fair lending, cyber security, data privacy, third-party risk management, and copyright.
对于人工智能来说尤其如此。其使用必须遵守现行法律法规，包括公平借贷、网络安全、数据隐私、第三方风险管理和版权。
And when AI is deployed in a bank, an even broader set of requirements may apply depending on the use case.
当人工智能在银行部署时，根据用例，可能会应用更广泛的要求。
Regulators are often playing "catch-up" with banks at the forefront of innovation.
监管机构经常“追赶”处于创新前沿的银行。
As a result, they often suffer from significant disadvantages in terms of understanding how the technology works, understanding the uses of AI within financial institutions, and keeping up-to-date with the latest AI developments.
因此，他们在了解技术的工作原理、了解人工智能在金融机构内的使用以及跟上人工智能的最新发展方面常常处于明显的劣势。
Of course, further compounding this challenge is that much of the work in AI innovation occurs far outside the banking system, including in the development and testing of generative AI models and in compiling the data sources on which to train the models.
当然，进一步加剧这一挑战的是，人工智能创新的大部分工作都发生在银行系统之外，包括生成人工智能模型的开发和测试以及编译用于训练模型的数据源。
Despite these challenges—and the understandable regulatory instinct to limit its use in the financial system—we must avoid this temptation.
尽管存在这些挑战，而且监管本能限制其在金融体系中的使用，这是可以理解的，但我们必须避免这种诱惑。
A few general principles should govern a coherent regulatory approach, which are the same principles that I apply to innovation generally.
一些通用原则应该指导一致的监管方法，这些原则与我通常应用于创新的原则相同。
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First, we must understand AI before we consider whether and how to change our regulatory approach.
首先，我们必须先了解人工智能，然后再考虑是否以及如何改变我们的监管方式。
With respect to various internal use cases, the Board has published a compliance program that governs artificial intelligence.
对于各种内部用例，董事会发布了管理人工智能的合规计划。
11 One of the foundational elements for a successful approach to AI, and one mentioned in this plan, is the development and acquisition of staff expertise.
11 成功实施人工智能的基本要素之一（也是本计划中提到的）是员工专业知识的发展和获取。
Many banks have increased AI adoption to an expanding number of use cases.
许多银行已经增加了人工智能的采用，以适应越来越多的用例。
As this technology becomes more widely adopted throughout the financial system, it is critical that we have a coherent and rational policy approach.
随着这项技术在整个金融体系中得到更广泛的采用，我们拥有连贯而合理的政策方法至关重要。
That starts with our ability to understand the technology, including both the algorithms underlying its use and the possible implications—both good and bad—for banks and their customers.
首先是我们对技术的理解能力，包括其使用背后的算法以及对银行及其客户可能产生的影响（无论好坏）。
In suggesting that we grow our understanding and staff expertise as a baseline, I acknowledge that this has been, and is likely to remain, a challenge.
在建议我们以提高理解和员工专业知识为基准时，我承认这一直是而且很可能仍然是一个挑战。
The Federal Reserve and other banking regulators compete for the same limited pool of talent as private industry.
美联储和其他银行监管机构与私营企业一样争夺有限的人才库。
But we must prioritize improving our understanding and capacity as this technology continues to become more widely adopted.
但随着这项技术继续得到更广泛的采用，我们必须优先考虑提高我们的理解和能力。
Second, we must have an openness to the adoption of AI.
其次，我们必须对人工智能的采用持开放态度。
We need to have a receptivity to the use of this technology and know that successful adoption requires communication and transparency between regulated firms and regulators.
我们需要接受这项技术的使用，并知道成功采用需要受监管的公司和监管机构之间的沟通和透明度。
One approach regulators can use to reframe questions around AI (and innovation generally) is to adopt a posture that I think of as technology agnosticism.
监管机构可以用来重新构建有关人工智能（以及一般创新）问题的一种方法是采取一种我认为是技术不可知论的态度。
We should avoid fixating on the technology, and instead focus on the risks presented by different use cases.
我们应该避免专注于技术，而应关注不同用例带来的风险。
These risks may be influenced by a number of factors, including the scope and consequences of the use case, the underlying data relied on, and the capability of a firm to appropriately manage these risks.
这些风险可能受到许多因素的影响，包括用例的范围和后果、所依赖的基础数据以及公司适当管理这些风险的能力。
Putting activities together may be a helpful way to get a sense of broad trends (for example, the speed of AI adoption in the industry), but is inefficient as a way to address regulatory concerns (like safety and soundness, and financial stability).
将活动放在一起可能是了解广泛趋势（例如，行业中人工智能采用的速度）的有用方法，但作为解决监管问题（例如安全性和稳健性以及财务稳定性）的方法效率低下。
This may seem like an obvious point, but at times regulators have fallen prey to overbroad categorizations, treating a diverse set of activities as uniformly and equally risky.
这似乎是一个显而易见的观点，但有时监管机构会陷入过于宽泛的分类，将各种不同的活动视为统一且同等风险的活动。
This approach allows us to be risk-focused, much like we try to do with other forms of supervision, moderating intensity for low-risk activities, and increasing the intensity for higher-risk ones.
这种方法使我们能够以风险为中心，就像我们尝试其他形式的监督一样，调节低风险活动的强度，并增加高风险活动的强度。
Of course, regulatory agencies do not operate in a vacuum, so we must also ask what type of coordination we need to ensure that we promote safe and sound adoption of AI, and address broader financial stability risks, both domestically and internationally.
当然，监管机构并不是在真空中运作，因此我们还必须问，我们需要什么样的协调来确保我们促进人工智能的安全、稳健的采用，并解决更广泛的国内和国际金融稳定风险。
As a threshold matter, we need coordination both within each agency and among domestic regulators that play a role in the supervision and regulation of the financial system, which requires an environment of open sharing of information.
首先，我们需要各个机构内部以及国内监管机构之间的协调，这些监管机构在金融体系的监督和管理中发挥着作用，这需要一个开放共享的信息环境。
A posture of openness to AI requires caution when adding to the body of regulation.
在增加监管体系时需要谨慎对待人工智能的开放态度。
Specifically, I think we need a gap analysis to determine if there are regulatory gaps or blind spots that could require additional regulation and whether the current framework is fit for purpose.
具体来说，我认为我们需要进行差距分析，以确定是否存在可能需要额外监管的监管差距或盲点，以及当前框架是否适合目的。
Fundamentally though, the variability in the technology will almost certainly require a degree of flexibility in regulatory approach.
但从根本上说，技术的可变性几乎肯定需要监管方法具有一定程度的灵活性。
Closing Thoughts Before closing, I want to thank the organizers of this event for the invitation to address you this evening, and to thank the many speakers and participants who have contributed to the symposium.
结束语 在结束之前，我要感谢本次活动的组织者邀请我今晚向大家发表讲话，并感谢为本次研讨会做出贡献的许多发言者和与会者。
Artificial intelligence has tremendous potential to reshape the financial services industry and the broader world economy.
人工智能具有重塑金融服务业和更广泛的世界经济的巨大潜力。
While I have suggested in my remarks that we need not rush to regulate, it is important that we continue to monitor developments in AI and their real-world effects.
虽然我在讲话中建议我们不必急于监管，但重要的是我们要继续监控人工智能的发展及其对现实世界的影响。
In the long run, AI has the potential to impact many aspects of the Fed's work, from our role in supervising the payment system, to the important work we do promoting the safe and sound operation of banks and financial stability.
从长远来看，人工智能有可能影响美联储工作的许多方面，从我们在监督支付系统方面的作用，到我们为促进银行安全稳健运营和金融稳定所做的重要工作。
AI may also play a growing role in monetary policy discussions, as the introduction of AI tools alter labor markets, affecting productivity and potentially the natural rate of unemployment and the natural rate of interest.
人工智能也可能在货币政策讨论中发挥越来越重要的作用，因为人工智能工具的引入改变了劳动力市场，影响生产率，并可能影响自然失业率和自然利率。
But as we engage in ongoing monitoring—and expand our understanding of AI technology and how it fits within the bank regulatory framework—I think it is important to preserve the ability of banks to innovate and allow the banking system to realize the benefits of this new technology.
但当我们进行持续监控并扩大我们对人工智能技术及其如何适应银行监管框架的理解时，我认为保持银行创新能力并让银行系统实现这项新技术的好处非常重要。
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The views expressed here are my own and not necessarily those of my colleagues on the Federal Reserve Board or the Federal Open Market Committee.
这里表达的观点是我自己的观点，并不一定是我在联邦储备委员会或联邦公开市场委员会的同事的观点。
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In 2017, the Financial Stability Board was already considering the financial stability implications of AI and machine learning in financial services.
2017 年，金融稳定委员会已经在考虑金融服务中人工智能和机器学习对金融稳定的影响。
See Financial Stability Board, "Artificial intelligence and machine learning in financial services: Market developments and financial stability implications" (PDF) (Basel: Financial Stability Board, November 2017).
请参阅金融稳定委员会，“金融服务中的人工智能和机器学习：市场发展和金融稳定影响”(PDF)（巴塞尔：金融稳定委员会，2017 年 11 月）。
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The National Artificial Intelligence Initiative Act of 2020, 15 U.S.C.
2020 年国家人工智能倡议法案，15 U.S.C.
§ 9401(3) defines artificial intelligence as "…
§ 9401(3) 将人工智能定义为“……
a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments.
一种基于机器的系统，可以针对一组给定的人类定义的目标，做出影响真实或虚拟环境的预测、建议或决策。
Artificial intelligence systems use machine and human-based inputs to-(A) perceive real and virtual environments; (B) abstract such perceptions into models through analysis in an automated manner; and (C) use model inference to formulate options for information or action." Return to text
人工智能系统使用基于机器和人类的输入来： (A) 感知真实和虚拟环境； (B) 通过自动分析将这些感知抽象为模型； (C) 使用模型推理来制定信息或行动的选项。”返回正文
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See Request for Information and Comment on Financial Institutions' Use of Artificial Intelligence, Including Machine Learning, 86 Fed.
请参阅关于金融机构使用人工智能（包括机器学习）的信息请求和评论，86 Fed。
Reg.
注册。
16837 (March 31, 2021) (PDF).
16837（2021 年 3 月 31 日）（PDF）。
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Financial Crimes Enforcement Network, Financial Trend Analysis: Mail Theft-Related Check Fraud: Threat Pattern & Trend Information, February to August 2023 (PDF) (Vienna: Financial Crimes Enforcement Network, September 2024).
金融犯罪执法网络，金融趋势分析：与邮件盗窃相关的支票欺诈：威胁模式和趋势信息，2023 年 2 月至 8 月 (PDF)（维也纳：金融犯罪执法网络，2024 年 9 月）。
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See U.S. Department of the Treasury, "Treasury Announces Enhanced Fraud Detection Processes, Including Machine Learning AI, Prevented and Recovered Over $4 Billion in Fiscal Year 2024," news release, October 17, 2024.
请参阅美国财政部，“财政部宣布加强欺诈检测流程，包括机器学习人工智能，在 2024 财年预防和追回超过 40 亿美元”，新闻稿，2024 年 10 月 17 日。
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See Board of Governors of the Federal Reserve System, Consumer Financial Protection Bureau, Federal Deposit Insurance Corporation, National Credit Union Administration, and Office of the Comptroller of the Currency, "Interagency Statement on the Use of Alternative Data in Credit Underwriting," (PDF) news release, December 12, 2019.
请参阅美联储理事会、消费者金融保护局、联邦存款保险公司、国家信用合作社管理局和货币监理署，“关于在信贷承保中使用替代数据的机构间声明”，(PDF) 新闻稿，2019 年 12 月 12 日。
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See Michelle W.
参见米歇尔·W.
Bowman, "Perspectives on U.S. Monetary Policy and Bank Capital Reform," (PDF) (speech at Policy Exchange, London, England, June 25, 2024).
Bowman，“美国货币政策和银行资本改革的观点”(PDF)（英国伦敦政策交易所演讲，2024 年 6 月 25 日）。
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See Michelle W.
参见米歇尔·W.
Bowman, "Innovation and the Evolving Financial Landscape" (PDF) (speech at The Digital Chamber DC Blockchain Summit 2024, Washington, D.C., May 15, 2024).
Bowman，“创新与不断变化的金融格局”(PDF)（2024 年 5 月 15 日华盛顿特区数字商会区块链峰会上的演讲）。
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See Bowman, "Innovation and the Evolving Financial Landscape." Return to text
请参阅鲍曼，“创新与不断变化的金融格局”。返回正文
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See Board of Governors of the Federal Reserve System, Compliance Plan for OMB Memorandum M-24-10 (PDF) (Washington: Board of Governors, September 2024).
请参阅联邦储备系统理事会，OMB 备忘录 M-24-10 合规计划 (PDF)（华盛顿：理事会，2024 年 9 月）。
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