Data readiness for agentic AI in financial services
Data readiness for agentic AI in financial services
金融服务领域中代理式 AI 的数据就绪性
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.
金融服务公司在商业 AI 方面有着独特的需求。它们在监管最严格的行业之一中运营，同时必须应对瞬息万变的外部事件。因此，代理式 AI（Agentic AI）在金融服务领域的成功，与其说取决于系统的复杂程度，不如说取决于其所依赖数据的质量、安全性和可访问性。Elastic 公司搜索 AI 全球董事总经理 Steve Mayzak 表示：“一切都始于数据。”
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.
代理式 AI——即能够独立规划并采取行动完成任务，而不仅仅是生成回复的系统——因其能够整合实时数据并优化复杂工作流程，为金融服务业带来了巨大潜力。Gartner 的研究发现，超过半数的金融服务团队已经实施或计划实施代理式 AI。然而，在任何组织中引入自主 AI 都会放大其所用底层数据的优势与劣势。为了快速、自信且可控地部署代理式 AI，金融服务公司首先必须具备大规模搜索、保护数据并赋予其语境的能力。
“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.
“代理式 AI 会放大链条中最薄弱的环节：数据的可用性和质量，”Mayzak 说道，“而你的系统水平取决于其最薄弱的环节。”因此，金融服务公司需要一个值得信赖的集中式数据存储库，它必须易于访问、可靠，并且能够实现规模化管理。
The high stakes of quality information
高质量信息带来的高风险
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.
金融服务行业的监管要求所有数据工具必须具备高度的问责制。正如 Mayzak 所言：“你不能仅仅停留在解释数据来源及其转换过程，即‘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什么信息，以及为什么这些数据对于下一步操作是正确的逻辑。”也就是说，你需要能够查看、理解并描述底层的处理过程。
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.
与此同时，金融服务公司需要速度和准确性，以满足客户期望并保持竞争优势。市场在不断变化，风险和机遇也随之波动。如果 AI 模型除了分析易于处理的电子表格中的结构化数据外，还能解析来自复杂来源的自然语言（非结构化数据），就能为用户提供更相关的信息。在这种环境下，容错率为零，包括困扰早期 AI 尝试的“幻觉”问题。
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.
代理式 AI 系统依赖于对高质量、受控、安全且可访问数据的快速获取。在金融服务领域，这些数据涵盖了交易、客户互动、风险信号、政策和历史背景。为 AI 准备这些数据的任务不容小觑。Mayzak 指出：“自然语言比结构化数据混乱得多，这使得整理和清洗数据的过程变得更加重要，同时也更加困难。”
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.”
数据必须经过良好的索引并跨不同位置进行整合，而不是被锁定在组织内各个独立的系统孤岛中。否则，AI 代理会出现延迟、提供不一致的答案，并产生难以追踪和解释的决策，从而削弱监管机构、客户和内部利益相关者的信心。正如 Mayzak 所说：“银行执行交易的方式有多种描述。在代理驱动的世界中，我们需要这些描述是确定性的——即每次都能给出相同的结果。然而，我们构建的基础是强大但非确定性的模型。这极其棘手，但并非不可能。”
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.
对于金融服务公司而言，管理这些数据极具挑战性。Forrester 的一项研究发现，57% 的金融机构仍在开发充分利用代理式 AI 所需的内部能力。Mayzak 说：“数据以多种不同格式存在，是在银行历史发展过程中产生的。以任何一家拥有 50 年历史的银行为例：它们可能针对同一件事有 60 种不同类型的 PDF 文档。同时，我们要求这些系统的输出必须 100% 准确。在许多情况下，没有‘足够好’这一说。”也就是说，公司必须一次就把事情做对。
Searching and securing results
搜索与保障结果
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.
一个有效的搜索平台是解决数据碎片化、索引不良和无法访问问题的关键。能够轻松筛选结构化和非结构化数据、确保其安全并将其应用于正确语境的金融服务公司，将从代理式 AI 中获得最大价值。这通常要求在设计 AI 系统时就考虑到数据访问和效用，以便它们能够更快地工作、产生更准确的结果，并降低风险。
“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.”
“搜索是使 AI 准确并扎根于真实数据的基石技术，”Mayzak 说，“搜索平台已成为推动这场 AI 革命的权威语境和记忆存储库。”
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 operations.
一旦部署到位，这些 AI 增强型搜索和自主系统可以为金融服务公司提供多种用途。在监控客户风险敞口时，代理式 AI 可以持续扫描交易、市场信号和外部数据以检测潜在风险；平台随后可以实时自动标记或升级问题。在交易监控中，AI 代理可以审查交易工作流程，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 可以从各个系统中收集数据、生成所需报告，并追踪每个输出的产生过程。这些 AI 应用在节省时间的同时，通过可追溯和可解释性支持了审计和合规需求。尽管此类能力已经存在，但它们往往是手动的、碎片化的且难以扩展。代理式 AI 使金融机构能够迈向更加自动化、高效的运营模式。