Building

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agents

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financial

services

In financial services, AI agents are moving beyond pilot programs to deliver concrete business value.在金融服务领域，AI 代理正从试点项目迈向提供具体的业务价值。

In banking, wealth management, and insurance, autonomous AI agents are transforming how customers understand spending patterns and find savings opportunities. Among other use cases, these tools spot potential overdraft fees, suggest better savings strategies, and guide financial decisions. 在银行、财富管理和保险领域，自治 AI 代理正在改变客户了解消费模式和发现储蓄机会的方式。除此之外，这些工具还能发现潜在的透支费用、建议更好的储蓄策略，并指导金融决策。

For instance, McKinsey research shows that financial institutions adopting AI agent workflows in fraud detection could generate two hundred to two thousand percent productivity gains, while Norges Bank Investment Management (NBIM) employees save hundreds of cumulative hours per week on analytical and operational tasks using Claude. 例如，麦肯锡的研究显示，金融机构在欺诈检测中采用 AI 代理工作流可实现 200% 到 2000% 的生产力提升，而挪威银行投资管理公司（NBIM）的员工使用 Claude 每周在分析和运营任务上累计节省数百小时。

For most organizations, the real challenge isn't adopting AI agents. It's building systems that navigate complex regulations, manage real-time risk, and protect customer assets while improving business outcomes.对大多数组织而言，真正的挑战并不是采用 AI 代理，而是构建能够在复杂监管环境中运作、实时管理风险并保护客户资产，同时提升业务成果的系统。

Moving beyond analysis: how agents change the game超越分析：代理如何改变游戏规则

Agents represent a fundamental shift in enterprise AI, replacing generative AI tools that depend upon constant human input and oversight with autonomous systems that handle long-running, context-heavy tasks with minimal – if any – intervention. 代理代表了企业 AI 的根本性转变，用能够自主处理长期、上下文密集任务且几乎不需要人为干预的系统取代了依赖持续人工输入和监督的生成式 AI 工具。

This evolution is especially welcome in financial services, where data often lives in fragmented systems that don't talk to each other, making it harder to see the complete picture of a customer's financial health. Agentic systems can understand financial context, ingest information from multiple unrelated sources, process multiple kinds of data (transaction records, market data, regulatory documents), and apply all of these capabilities to taking meaningful actions within your customer’s existing financial workflows.这种演进在金融服务领域尤为受欢迎，因为数据往往散落在彼此不互通的碎片化系统中，难以全面了解客户的财务健康状况。代理系统能够理解金融上下文，摄取来自多个不相关来源的信息，处理多种数据（交易记录、市场数据、监管文件），并将这些能力用于在客户现有的金融工作流中采取有意义的行动。

What does this look like in practice? Instead of an analyst manually pulling data from five different systems, reviewing it, and then updating a risk assessment, an agent can monitor transaction patterns across those systems, recognize concerning trends, draft updated risk recommendations based on current regulations, and route them to the right analyst for approval. The agent handles the coordination and analysis, while the analyst makes the final decision.这在实际操作中是什么样子？与其让分析师手动从五个不同系统中提取数据、审阅并更新风险评估，代理可以跨这些系统监控交易模式，识别异常趋势，依据当前监管要求起草更新的风险建议，并将其路由给合适的分析师批准。代理负责协调和分析，而分析师作出最终决定。

This shift from traditional AI to AI agents is particularly significant for financial services because it tackles the process completion problem. Financial workflows don't just need information, they need actions taken across multiple systems to actually complete transactions and maintain compliance. Agents can bridge those gaps.从传统 AI 向 AI 代理的转变对金融服务尤为重要，因为它解决了流程完成问题。金融工作流不仅需要信息，还需要跨多个系统执行操作以真正完成交易并保持合规。代理可以弥合这些鸿沟。

Real-world results AI agents in finance 真实案例：金融领域的 AI 代理

AI-powered financial agents are already delivering real-world results in areas such as customer service, fraud detection, and workforce empowerment.AI 驱动的金融代理已经在客户服务、欺诈检测和员工赋能等领域交付了真实的成果。

Transforming customer interactions转变客户互动方式

Customer service operations are a natural starting point because this area has already proven successful. Financial institutions implementing AI-powered customer service are seeing measurable improvements:客户服务运营是一个自然的起点，因为该领域已经证明了成功。实施 AI 驱动客户服务的金融机构正看到可衡量的改进：

• Multilingual virtual assistants now handle hundreds of millions of interactions annually, serving millions of users across different language groups.多语言虚拟助理现在每年处理数亿次互动，为不同语言群体的数百万用户提供服务。
• Customer service agents automate routine tasks like balance inquiries and card replacements, reducing wait times while delivering 24/7 responses and suggesting relevant products, turning support interactions into opportunities to actually help people manage their finances.客户服务代理自动化常规任务，如余额查询和卡片更换，缩短等待时间，同时提供 24/7 响应并推荐相关产品，将支持互动转化为帮助人们管理财务的机会。

Intuit TurboTax, for example, built an AI financial assistant powered by Claude that generates clear and accurate tax explanations for millions of customers. The agentic implementation was so successful that the AI-powered experiences achieved higher customer ratings compared to non-Claude experiences in the previous tax season. 例如，Intuit TurboTax 构建了一个由 Claude 驱动的 AI 财务助理，为数百万客户生成清晰、准确的税务解释。该代理实现如此成功，以至于 AI 驱动的体验在上一个税季的客户评分上超过了非 Claude 体验。

Combating fraud and financial crime打击欺诈和金融犯罪

In fraud detection and cybercrime prevention, AI agents excel at spotting patterns that may slip past human analysts due to sheer volume. When you consider that financial institutions currently catch only about 2% of global financial crime, you can see why this matters.在欺诈检测和网络犯罪防范方面，AI 代理擅长发现人类分析师因工作量庞大而可能错过的模式。当你考虑到金融机构目前仅捕获约 2% 的全球金融犯罪时，就能理解这有多重要。

AI agents monitor millions of transactions in real-time, working around the clock without the fatigue or cognitive limitations that affect human teams. McKinsey found that a single team member can effectively supervise more than 20 AI agents in financial crime-detection workflows.AI 代理实时监控数百万笔交易，昼夜不间断工作，不受人类团队的疲劳或认知限制。麦肯锡发现，单个团队成员可以有效监管超过 20 个 AI 代理的金融犯罪检测工作流。

For Brex, a modern financial platform, Claude powers their AI anomaly detection, reviewing 100% percent of transactions and providing critical aircover for financial professionals by proactively grouping related expenses, flagging policy concerns, and providing explanations with recommended actions. 对于现代金融平台 Brex，Claude 为其 AI 异常检测提供动力，审查 100% 的交易，并通过主动分组相关费用、标记政策问题以及提供带有推荐行动的解释，为金融专业人士提供关键的安全保障。

Amplifying team capabilities放大团队能力

AI agents deliver tangible benefits right where your teams need them most. When implemented thoughtfully, these tools don't replace your workforce but rather amplify what they can accomplish and let them focus on the high-value work that requires their knowledge and expertise. Here's how financial services organizations like Block and Campfire are doing this:AI 代理在团队最需要的地方提供切实的收益。经过深思熟虑的实施，这些工具并不取代员工，而是放大他们的工作成果，让他们专注于需要专业知识和经验的高价值工作。以下是金融服务组织如 Block 和 Campfire 的做法：

• Design teams turn ideas into working prototypes without coding barriers设计团队在无需编码的情况下将创意转化为可运行的原型
• Operations teams automate case ticket closure运营团队自动化案件工单关闭
• Accounting teams query financial data through natural language, perform flux analyses, and access audit support会计团队通过自然语言查询财务数据，执行流量分析，并获取审计支持

Unique challenges in financial services金融服务的独特挑战

Banks and financial institutions face unique challenges that make AI agent implementation more complex than typical enterprise deployments. When every decision potentially impacts customer finances, regulatory compliance, and institutional risk, the stakes are at a different level altogether.银行和金融机构面临的独特挑战使得 AI 代理的实施比典型企业部署更为复杂。当每个决策都可能影响客户资金、监管合规和机构风险时，风险层级截然不同。

The combination of complex financial contexts, regulatory requirements, and direct impact on customer outcomes creates an implementation environment where thoroughness trumps speed. These are some of the challenges you can expect to encounter.复杂的金融背景、监管要求以及对客户结果的直接影响共同构成了一个需要以彻底性胜过速度的实施环境。以下是你可能遇到的一些挑战。

Integrating with legacy infrastructure与遗留基础设施的集成

Financial institutions typically run on decades-old core banking systems that weren't designed for real-time AI integration. Your loan origination system, trading platforms, and compliance databases often use different protocols, data formats, and security models.金融机构通常运行在数十年前的核心银行系统上，这些系统并非为实时 AI 集成而设计。你的贷款发放系统、交易平台和合规数据库往往使用不同的协议、数据格式和安全模型。

Legacy system integration often shows up in:遗留系统集成常表现为：

• Core banking platform incompatibilities across vendors不同供应商的核心银行平台不兼容
• Departmental data silos requiring cross-system orchestration部门数据孤岛需要跨系统编排
• Legacy mainframe integration challenges遗留主机集成挑战
• Real-time synchronization needs for time-sensitive trading decisions对时间敏感的交易决策的实时同步需求

When tackling these challenges, teams need to make practical decisions about integration approaches. The first consideration involves connectivity: does the AI agent have direct integration capabilities with the necessary systems? If not, teams face two practical options: building custom connectors (typically through APIs or MCP approaches) or implementing middleware systems to bridge these communication gaps.在应对这些挑战时，团队需要对集成方式做出务实决策。首要考虑是连通性：AI 代理是否具备与所需系统直接集成的能力？如果没有，团队面临两种务实选项：构建自定义连接器（通常通过 API 或 MCP 方法）或实现中间件系统以弥合通信鸿沟。

For early agentic solutions, look to integrate with modern platforms with APIs and standard protocols. If you decide you need to connect to legacy systems, you'll need to develop middleware that can translate between these systems while maintaining transaction integrity and audit trails.对于早期的代理解决方案，请优先与具备 API 和标准协议的现代平台集成。如果决定需要连接遗留系统，则必须开发能够在保持交易完整性和审计追踪的前提下进行转换的中间件。

Navigating regulatory complexity应对监管复杂性

A single transaction might trigger compliance requirements from multiple regulators, including SEC, FDIC, state banking authorities, and international bodies for cross-border payments. AI agents must understand not just what actions to take, but which regulatory frameworks apply to each decision and how to document actions for different audit requirements.单笔交易可能触发来自多个监管机构的合规要求，包括 SEC、FDIC、州银行监管机构以及跨境支付的国际机构。AI 代理必须不仅了解应采取何种行动，还要知道每个决策适用的监管框架以及如何为不同的审计要求记录行动。

Some regulatory considerations that need to be part of your agent architecture include:需要纳入代理架构的监管考量包括：

• SOC 2 and PCI DSS compliance for AI data processing workflowsAI 数据处理工作流的 SOC 2 与 PCI DSS 合规
• Evidence-based validation of risk assessment accuracy基于证据的风险评估准确性验证
• Documentation requirements for AI decision audit trailsAI 决策审计追踪的文档要求

Ensure you build observability and traceability into the agentic solutions from day one. You'll want it simply from a troubleshooting perspective, but you'll definitely need it from a regulatory one.确保从一开始就在代理解决方案中构建可观测性和可追溯性。你会从故障排查的角度需要它，但更重要的是从监管的角度必须拥有它。

Managing real-time risk实时风险管理

Unlike other industries where decisions can be reviewed later, financial agents often make choices that immediately impact customer accounts, market positions, or regulatory standing. This demands fail-safe architectures where agents can act quickly but always within predefined risk parameters that protect both customers and the institution.与其他行业不同，金融代理的决策往往会立即影响客户账户、市场头寸或监管状态。这要求具备容错架构，使代理能够快速行动，但始终在预定义的风险参数范围内，保护客户和机构的利益。

Implementation requires:实施需要：

• Transparent reasoning that financial professionals can validate and explain to clients金融专业人士能够验证并向客户解释的透明推理
• Clear escalation pathways for complex or ambiguous financial situations针对复杂或模糊金融情形的明确升级路径
• Override capabilities allowing advisors to reject AI recommendations when client circumstances warrant在客户情况需要时，顾问能够拒绝 AI 推荐的覆盖能力
• Fail-safe defaults that prioritize customer protection over operational efficiency优先保护客户而非运营效率的容错默认设置

Identify which actions will require human-in-the-loop authorization, either from a risk or regulatory perspective. For high-risk actions, consider how the system, agentic or otherwise, can fail in a known safe state.识别哪些行动需要人工在环授权，无论是出于风险还是监管考虑。对于高风险行动，考虑系统（无论是代理还是其他）如何在已知的安全状态下失败。

Practical deployment strategies务实的部署策略

AI agents are already transforming financial operations for some companies. There are abundant examples of agents currently in production and delivering measurable impact on fraud detection, customer satisfaction, and operational efficiency. So, how do you build and deploy agents that address your specific operational challenges, while making sure they meet regulatory requirements and risk management standards?AI 代理已经在一些公司中改变了金融运营。当前已有大量代理在生产环境中运行，并在欺诈检测、客户满意度和运营效率方面产生可衡量的影响。那么，如何构建和部署能够解决特定运营挑战、同时满足监管要求和风险管理标准的代理？

Finding your starting point寻找起点

The best agent initiatives begin by targeting the things that everyone already agrees need fixing. Clear metrics make all the difference here, because they show whether the solution actually works and help build momentum for wider adoption.最好的代理项目始于针对大家已一致认同需要修复的事项。明确的指标在此至关重要，因为它们显示解决方案是否真正奏效，并帮助为更广泛的采用积累动力。

Choose high-impact, low-risk targets选择高影响、低风险的目标

Target opportunities for adding agents within your organization to areas where the stakes are manageable, but the potential impact is meaningful.在组织内部寻找添加代理的机会，定位那些风险可控但潜在影响显著的领域。

Look for processes where human oversight already exists or where consequences of imperfect automation remain minimal: these are perfect for early adoption without introducing excessive organizational risk. Customer service triage, internal knowledge retrieval, and routine data validation are natural entry points where AI agents can immediately reduce workloads while humans remain in the verification loop.寻找已经有人类监督或不完美自动化后果仍可接受的流程：这些正是早期采用的理想场景，可在不引入过度组织风险的情况下进行。客户服务分流、内部知识检索和常规数据验证都是自然的切入点，AI 代理可以立即减轻工作负荷，同时让人类保持在验证环节。

Beyond immediate productivity gains, these initial agent deployments are extremely valuable learning experiences. Each low-risk implementation helps your teams develop practical understanding of agent capabilities, limitation patterns, and integration requirements without the pressure of mission-critical deadlines. Your technical staff learns to fine-tune prompts and monitoring systems in environments where mistakes are learning opportunities rather than costly errors.除了立竿见影的生产力提升，这些初始代理部署还是极具价值的学习经验。每一次低风险实施都帮助团队在没有关键任务期限压力的环境中，培养对代理能力、局限模式和集成需求的实际认知。技术人员在错误可视为学习机会而非代价高昂错误的环境中，学会微调提示和监控系统。

Start simple, learn fast从简单开始，快速学习

Start with agents that handle one straightforward task, like flagging unusual transaction patterns, monitoring compliance deadlines, or automating document classification.先从处理单一、直接任务的代理入手，例如标记异常交易模式、监控合规截止日期或自动化文档分类。

You'll get tangible operational improvements while keeping human judgment firmly in the loop while building the organizational muscle memory needed for more ambitious agent applications. When you eventually tackle higher-risk use cases, you'll have technical capabilities and confidence built through experience rather than theoretical assumptions about how agents might perform in your specific environment.这样既能获得切实的运营改进，又能在保持人工判断在环的同时，构建组织对更宏大代理应用所需的肌肉记忆。当你最终着手更高风险的用例时，技术能力和信心已经通过经验而非理论假设得以建立。

Scaling across the organization在组织内部规模化

Success with initial implementations opens the door to enterprise-wide capabilities. The key is moving from point solutions to shared infrastructure that serves multiple departments.初始实现的成功为企业级能力打开了大门。关键在于从点解决方案转向为多个部门服务的共享基础设施。

Building reusable foundations构建可复用的基础

Your organization will get better results when you build foundational AI agent capabilities that serve multiple departments rather than deploying one-off solutions for individual problems.当你为多个部门构建基础 AI 代理能力，而不是为单一问题部署一次性解决方案时，组织将获得更好的结果。

For example, you might implement a document processing capability that could be used across your organization. The same AI system that automates bank reconciliations and invoice processing can help compliance teams analyze regulatory documents and support various departments with financial data extraction. Each department contributes use cases that strengthen the core capability while benefiting from improvements driven by other teams.例如，你可以实现一个文档处理能力，供全组织使用。同一套 AI 系统既能自动化银行对账和发票处理，又能帮助合规团队分析监管文件，并为各部门提供财务数据抽取支持。每个部门贡献的用例既强化了核心能力，又受益于其他团队推动的改进。

Earning trust with stakeholders赢得利益相关者的信任

Agents interact with both your workforce and your customers, and each group will respond differently to AI-assisted processes. Trust-building matters as much as technical deployment.代理与员工和客户都交互，每个群体对 AI 辅助流程的反应不同。建立信任与技术部署同等重要。

For customers, transparency matters. Make it clear when they're interacting with an AI agent versus a human, explain what the agent can and cannot do, and provide straightforward pathways to human specialists when needed. This clarity builds confidence and encourages broader adoption of AI-assisted services.对客户而言，透明度至关重要。明确告知他们是与 AI 代理还是人与人交互，解释代理的能力与局限，并提供直接转向人工专家的简便渠道。这种清晰度能建立信心，促进 AI 辅助服务的更广泛采用。

Internal adoption follows similar principles. Your organization already has change management processes for new systems. Apply them here. Staff need to understand how agents work, when to trust their recommendations, and how to escalate concerns.内部采用遵循相似原则。组织已有的新系统变更管理流程同样适用于此。员工需要了解代理的工作方式、何时信任其建议以及如何上报问题。

Frame the conversation around enhancement rather than replacement. For example,  Block's internal AI agent reached 4,000 active users out of 10,000 employees across 15 different job profiles (sales, design, product, customer success, and operations). Adoption doubled in one month, with user engagement increasing 40-50% weekly as employees found new ways to use it. 围绕“增强”而非“取代”来阐述对话。例如，Block 的内部 AI 代理在 10,000 名员工中拥有 4,000 名活跃用户，覆盖 15 种不同岗位（销售、设计、产品、客户成功和运营）。采用率在一个月内翻倍，用户参与度每周提升 40‑50%，因为员工发现了新的使用方式。

The most successful implementations emphasize how AI enhances human capabilities rather than replacing them.最成功的实施强调 AI 如何提升人类能力，而非取代人类。

Advancing to complex use cases迈向复杂用例

At this stage, your organization has an augmented workforce, refined core capabilities, demonstrated wins, and experienced teams ready for larger challenges. The lessons learned from early implementations provide the foundation for more complex agent deployments.此时，组织已经拥有增强的员工队伍、成熟的核心能力、已验证的成功案例以及准备迎接更大挑战的经验丰富团队。早期实施的经验为更复杂的代理部署奠定了基础。

The observability and human-in-the-loop mechanisms you built for simpler use cases become even more critical as complexity increases. More than ever, your implementation needs:为更复杂的场景提供的可观测性和人工在环机制变得更加关键。你的实现需要：

• Comprehensive audit trails that track every agent decision and data source used, enabling review and regulatory compliance完整的审计追踪，记录每一次代理决策及使用的数据来源，以便审查和合规
• Real-time monitoring systems that detect when agents encounter edge cases or uncertain scenarios requiring human judgment实时监控系统，检测代理遇到边缘案例或需要人工判断的不确定情形
• Escalation protocols that automatically route complex cases to appropriate specialists based on clear criteria升级协议，依据明确标准自动将复杂案例路由至合适的专家
• Performance metrics that measure not just technical accuracy but business outcomes and workflow integration success绩效指标，不仅衡量技术准确性，还评估业务成果和工作流集成成功度

Taking action采取行动

AI agents represent a significant opportunity to address persistent challenges in financial services. Success requires thoughtful implementation that balances technological capability with industry-specific requirements. This approach delivers quick wins that build confidence while establishing the foundation for more sophisticated initiatives.AI 代理为解决金融服务中长期存在的挑战提供了重要机遇。成功需要在技术能力与行业特定需求之间取得平衡的深思熟虑的实施。这种方法既能带来快速胜利，建立信心，又能为更复杂的计划奠定基础。

The path forward demands partnership between technology and business teams. Financial services leaders who prioritize customer protection through robust testing and escalation pathways, and build modular systems that evolve with advancing AI capabilities, will lead the way.前进的道路需要技术团队与业务团队的协作。那些将客户保护置于首位、通过严格测试和升级路径，并构建能够随 AI 能力进步而演进的模块化系统的金融服务领袖，将引领行业前行。