金融服务中代理型AI的数据就绪状态
内容总结：
金融服务业AI代理面临数据准备关键挑战
近日，由Elastic赞助的一篇行业分析指出，金融服务业在应用代理型人工智能（Agentic AI）时，成败的关键并非取决于模型的复杂程度，而在于其所依赖数据的质量、安全性和可及性。该行业作为高度监管领域，对数据工具的问责性要求极高，同时需要应对每秒更新的外部事件。
报道称，代理型AI能够独立规划并执行任务，相比传统生成式AI具有整合实时数据、优化复杂流程的巨大潜力。Gartner调查显示，超过一半的金融服务团队已部署或计划部署此类系统。然而，引入自主AI会放大底层数据的弱点。Elastic全球搜索AI董事总经理史蒂夫·梅扎克强调："代理型AI放大了数据可用性和质量这一最薄弱环节，系统的可靠性取决于其短板。"
金融服务企业面临的挑战尤为严峻。行业监管要求对数据处理全流程进行可审计、可解释的追溯，而市场对速度和准确性的要求又不容许任何错误。早期AI常见的"幻觉"问题在此毫无容忍空间。梅扎克指出，自然语言数据比结构化数据复杂得多，整理和清洗过程既关键又困难。
此外，数据必须跨系统进行良好索引和整合，避免被锁在不同系统的"数据孤岛"中。否则，AI代理会出现反应滞后、答案不一致、决策难以追溯等问题，削弱监管机构、客户和内部利益相关者的信任。Forrester研究显示，57%的金融机构尚在培养充分运用代理型AI所需的内建能力。
解决方案在于构建有效的搜索平台。通过先进搜索技术，金融机构可以筛选结构化和非结构化数据，确保安全并应用于正确场景。梅扎克表示："搜索技术已成为支持AI革命的权威上下文和记忆库。"在客户风险监控、交易监测、监管报告等应用中，代理型AI能大幅提升效率，同时确保符合审计与合规要求。
对于如何启动，专家建议选择可控的小规模用例逐步推进。"成功可以累积成功。市场有效的做法是一次解决一个问题，等第一步运行成功后再逐步推进。"最终，那些将代理型AI整合到包含安全控制、数据治理和系统性能管理的更广泛生态中的企业，将建立可持续的竞争优势。
中文翻译：
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金融服务领域面向智能体AI的数据准备
智能体AI在金融服务领域的成功不仅取决于更智能的模型，更依赖于一个可访问、可靠且可规模化治理的权威上下文数据存储。
与Elastic合作
金融服务公司在应用商业AI时有着独特的需求。它们运营在监管最为严格的行业之一，同时需要响应每秒都在更新的外部事件。因此，智能体AI在金融服务领域的成功，更多取决于其依赖的数据的质量、安全性和可访问性，而非系统本身的复杂性。
“一切都始于数据，”Elastic全球搜索AI业务董事总经理Steve Mayzak表示。
智能体AI——能够独立规划并采取行动以完成任务，而非仅仅生成回应的系统——因其整合实时数据和优化复杂工作流的能力，在金融服务领域拥有巨大潜力。Gartner发现，超过一半的金融服务团队已经实施或计划实施智能体AI。
然而，将自主AI引入任何组织都会放大其底层数据的优势与劣势。为了快速、自信且可控地部署智能体AI，金融服务公司必须首先能够大规模地搜索、保护和情境化其数据。“智能体AI放大了链条中最薄弱的环节：数据的可用性和质量，”Mayzak表示，“而你的系统，其优劣取决于最薄弱的环节。”
因此，金融服务公司需要一个可信、集中、易于访问、可靠且可规模化管理的的数据存储。
高质量信息的高风险性
金融服务行业的监管要求所有数据工具都具有高度的可问责性。正如Mayzak所说：“你不能仅仅止步于解释数据来自何处以及转换成了什么：‘这是输入的数据，这是输出的结果。’你需要一种可审计、可治理的方式来解释模型发现了哪些信息，以及为何这些数据适合下一步处理的逻辑。”也就是说，你需要能够查看、理解并描述底层过程。
同时，为了满足客户期望并在竞争中保持领先，金融服务公司需要速度和准确性。市场在不断变化，风险和机遇也随之移动。如果AI模型能够解析来自复杂来源的自然语言（非结构化数据）——除了更易分析的电子表格中的结构化数据之外——这将为用户提供更相关的信息。
在这种环境下，对错误绝不容忍，包括困扰早期AI工作的“幻觉”问题。智能体AI系统依赖于快速访问高质量、治理良好、安全且可访问的数据。在金融服务领域，这些数据涵盖交易、客户互动、风险信号、政策以及历史背景。为AI准备数据的任务不容低估。“自然语言比结构化数据混乱得多，这使得组织和清理数据的过程更加重要，也更加困难，”Mayzak说。
数据必须得到良好索引，并在不同位置进行整合，而不能被锁定在组织内各个孤立系统的数据孤岛中。否则，AI智能体会反应迟缓、提供不一致的答案，并产生更难追踪和解释的决策，从而削弱监管机构、客户和内部利益相关者的信心。
正如Mayzak所言：“描述如何在银行执行一笔交易有很多不同的方式。在一个由智能体驱动的世界里，我们需要这些描述是确定性的——每次都能给出相同的结果。然而，我们正建立在强大但非确定性的模型之上。这极其棘手，但并非不可能。”
对于金融服务公司来说，管理这一点可能非常具有挑战性。Forrester的一项研究发现，57%的金融组织仍在开发充分利用智能体AI所需的内部能力。“数据以多种不同格式存在，是在银行发展历程中创建出来的，”Mayzak说，“以任何一家存在了50年的银行为例：他们可能为完全相同的事情准备了60种不同类型的PDF。与此同时，我们希望这些系统的输出是100%准确的。在很多情况下，没有‘足够好’这回事。”也就是说，公司必须一次性做对。
搜索与保障成果
一个有效的搜索平台是解决数据碎片化、索引不良、难以访问等问题的关键。能够轻松筛选其结构化和非结构化数据、确保数据安全并在正确情境下应用的金融服务公司，将从智能体AI中获得最大价值。这通常需要从数据访问和实用性的角度设计AI系统，以便它们能更快地工作，产生更准确的结果，并降低风险。“搜索是使AI准确并植根于真实数据的基础技术，”Mayzak说，“搜索平台已成为驱动这场AI革命的权威上下文和记忆存储。”
一旦建立，这些AI增强的搜索和自主系统可以为金融服务公司服务于多种目的。在监控客户风险敞口时，智能体AI可以持续扫描交易、市场信号和外部数据以检测新兴风险；平台随后可以实时自动标记或上报问题。在交易监控中，AI智能体可以审查交易工作流，识别不同格式之间的差异，并以最少的人工干预逐步解决异常情况。在监管报告方面，AI可以从各系统收集数据，生成所需报告，并追踪每个输出结果是如何产生的。这些AI应用不仅节省时间，而且通过可追溯和可解释的特性支持审计和合规需求。
尽管此类能力已经存在，但它们通常是手动的、分散的且难以规模化。智能体AI允许金融组织向更自动化、更高效、更可扩展的流程迈进，同时保持其高度监管环境所需的准确性和透明度。正如Mayzak所说：“这与人类今天的操作方式没有太大不同，只是以更快的速度和规模进行。”
构建智能体AI生态系统
启动智能体AI可能令人生畏，尤其是在其他AI项目在内部停滞不前的情况下。Mayzak的建议是选择一个可控的用例，并让其随着时间的推移而发展。“成功可以建立在成功之上，”他说，“虽然公司可能旨在自动化一个70步的业务流程，但他们发现必须从某处开始。目前市场上行之有效的方法是逐步解决问题。一旦你让第一步成功运行，你就可以迈出下一步，再下一步。”
那些在同行中领先的金融服务组织，将是那些将智能体AI整合到更广泛的生态系统中，包括强大的安全控制、良好的数据治理以及有效的系统性能管理的组织。正如Mayzak所说：“做好这一点将创造一个AI反馈循环，高管们可以从这些系统中获得新信号，以评估其投资的有效性，并生成可靠、可操作的见解。”通过迭代试点和持续改进，企业将构建出可衡量、可管理且可扩展的智能体系统。这将把智能体AI转化为持久的竞争优势。
了解更多关于Elastic如何支持金融服务的信息。
本内容由MIT Technology Review的定制内容部门Insights制作。并非由MIT Technology Review的编辑人员撰写。由人类作者、编辑、分析师和插画师进行研究、设计和撰写。这包括撰写调查问卷和收集调查数据。可能使用的AI工具仅限于经过人工严格审核的次要制作流程。
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Data readiness for agentic AI in financial services
The success of agentic AI in financial services depends not just on smarter models, but on an authoritative context data store—one that is accessible, reliable, and governed at scale.
In partnership withElastic
Financial services companies have unique needs when it comes to business AI. They operate in one of the most highly regulated sectors while responding to external events that are updated by the second. As a result, the success of agentic AI in financial services depends less on the sophistication of the system and more on the quality, security, and accessibility of the data it relies on.
“It all starts with the data,” says Steve Mayzak, global managing director of Search AI at Elastic.
Agentic AI—systems that can independently plan and take actions to complete tasks, rather than simply generate responses—holds enormous potential for financial services due to its ability to incorporate real-time data and optimize complex workflows. Gartner has found that more than half of financial services teams have already implemented or plan to implement agentic AI.
However, introducing autonomous AI into any organization magnifies both the strengths and weaknesses of the underlying data it uses. To deploy agentic AI with speed, confidence, and control, financial services companies must first be able to search, secure, and contextualize their data at scale. “Agentic AI amplifies the weakest link in the chain: data availability and quality,” says Mayzak. “And your systems are only as good as their weakest link.”
Financial services companies, therefore, require a trusted and centralized data store that is easy to access, dependable, and can be managed at scale.
The high stakes of quality information
Regulation in the financial services sector requires a high degree of accountability for all data tools. As Mayzak says, “You can’t just stop at explaining where the data came from and what it was transformed into: ‘Here's the data that went in, and this is what came out.’ You need an auditable and governable way to explain what information the model found and the logic of why that data was right for the next step.” That is, you need to be able to see, understand, and describe the underlying processes.
At the same time, financial services companies require speed and accuracy in order to meet customer expectations and stay ahead of competition. Markets are continually shifting, and risks and opportunities move along with them. If an AI model can parse natural language (unstructured data) from complex sources—in addition to structured data in spreadsheets that are easier to analyze—this gives users more relevant information.
In this environment, there is no tolerance for error, including the hallucinations that plagued early AI efforts. Agentic AI systems depend on rapid access to high-quality, well-governed data that is secure and accessible. In financial services, that data spans transactions, customer interactions, risk signals, policies, and historical context. The task of preparing that data for AI should not be underestimated. “Natural language is way more messy than structured data, and that makes the process of organizing and cleaning it up that much more important and also that much harder,” says Mayzak.
The data must be well indexed and consolidated across different locations, not locked in the silos of separate systems across the organization. Otherwise, AI agents lag, provide inconsistent answers, and produce decisions that are harder to trace and explain, undermining confidence among regulators, customers, and internal stakeholders.
As Mayzak says, “There are many different ways to describe how to execute a trade at a bank. In an agent-powered world, we need those descriptions to be deterministic—to give the same results every time. Yet we’re building on powerful but non-deterministic models. That’s incredibly tricky, but not impossible.”
For a financial services firm, managing this can be very challenging. A Forrester study found that 57% of financial organizations are still developing the necessary internal capabilities to fully leverage agentic AI. “The data exists in many different formats, created over the course of a bank’s history,” says Mayzak. “Take any bank that's been around for 50 years: They might have 60 different types of PDFs for the exact same thing. And at the same time, we want the output of these systems to be 100% accurate. In many cases, there is no ‘good enough’.” That is, companies need to do it right, and the first time.
Searching and securing results
An effective search platform is key to solving the problem of fragmented, poorly indexed, inaccessible data. Financial services companies that can readily sift through both their structured and unstructured data, keep it secure, and apply it in the right context will get the most value from agentic AI. This often requires designing AI systems with data access and utility in mind so they can work faster and yield more accurate results, as well as reduce risk. “Search is the foundational technology that makes AI accurate and grounded in real data,” Mayzak says. “Search platforms have become the authoritative context and memory stores that will power this AI revolution.”
Once in place, these AI-enhanced searches and autonomous systems can serve financial services companies for a range of purposes. When monitoring client exposure, agentic AI can continuously scan transactions, market signals, and external data to detect emerging risks; platforms can then automatically flag or escalate issues in real time. In trade monitoring, AI agents can review trade workflows, identify discrepancies across different formats, and resolve exceptions step by step with minimal human intervention. In regulatory reporting, AI can gather data from across systems, generate required reports, and track how each output was produced. These applications of AI save time while supporting audit and compliance needs by being traceable and explainable.
Although such capabilities already exist, they are often manual, fragmented, and difficult to scale. Agentic AI allows financial organizations to move toward more automated, efficient, and scalable processes while maintaining the accuracy and transparency required in their highly regulated environment. As Mayzak says, “It’s not that different from how humans operate today, just done at a much faster pace and at scale.”
Building an agentic AI ecosystem
Launching agentic AI can be daunting, especially if other AI ventures have stalled internally. Mayzak’s recommendation is to choose a manageable use case and allow it to grow over time. “Success can build on success,” he says. “While companies may aim to automate a 70-step business process, they are discovering that you have to start somewhere. What is working in the market is tackling the problem one step at a time. Once you get the first step working, then you can take the next step, and the next.”
The financial services organizations that lead among their peers will be those that integrate agentic AI into a broader ecosystem that includes strong security controls, good data governance, and effective management of system performance. As Mayzak says, “Doing this well will create an AI feedback loop, where executives gain new signals from these systems to assess the effectiveness of their investments and generate reliable, actionable insights.” By iterating on pilots and continuously improving, companies will build agentic systems that can be measured, managed, and scaled. This will transform agentic AI into lasting competitive advantage.
Learn more about how Elastic supports financial services.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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