Ai Agents For Financial Services

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Aug 15, 2025

7 minutes read

Building AI agents in finance: Reshaping financial services

Financial teams are unlocking a new era of compliance, efficiency, and customer trust—powered by AI agents.

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Updated: June 23, 2026

Financial services firms know they need to move fast to stay competitive. Yet time-consuming manual processes remain the norm across many core workflows.

For example, McKinsey analysis found that bank employees at certain organizational levels spend 60% to 70% of their time on internal discussions and updates, leaving only 30% to 40% for work directly tied to clients, decisions, or delivery. Meanwhile, World Economic Forum research suggests that up to 39% of work across capital markets, insurance, and banking has high potential for full automation, and a further 34% to 37% of work has high potential for augmentation.

This is where AI agents in finance and banking come into play. By connecting to firms’ approved tools and data sources, AI agents can help financial professionals summarize information, coordinate workflows, draft outputs, and complete multi-step tasks.

In this article, we’ll explain what AI agents are, explore practical agentic AI applications in finance, and look at how financial institutions can start deploying them securely.

##### What are AI agents in finance?

AI agents in finance help financial professionals and teams automate or streamline specific tasks and workflows. Powered by large language models (LLMs), AI agents can respond to prompts or triggers, access approved tools and data, and produce outputs or actions within defined guardrails.

Each agent can be defined by:

• A specific task or job is designed to be performed
• The tools and data it can access and act on (email, CRMs, and other banking software)
• The guardrails, policies, and approval rules that govern its outputs and actions

In financial services, AI agents broadly fall into two categories — each suited to different needs.

• Conversational agents: These are chat-style copilots for open-ended cognitive tasks like asking questions, exploring data, or drafting an analysis. Given a prompt, they can retrieve information from approved search tools or internal systems, plan a response path, and synthesize a reply for human review.
• Automation agents: Often deployed as part of agentic workflows, these agents are built for specific business tasks where control, consistency, and compliance are essential. They can make bounded decisions or trigger next steps within a predefined workflow, such as Know Your Customer (KYC) evidence gathering.

As agentic AI in finance matures, multi-agent orchestration is emerging as one way to coordinate more complex workflows. In these systems, one agent might retrieve data, another might analyze it, and another might check the result before it reaches a human reviewer. This helps make agent-based systems more modular and scalable, but it also increases the need for routing logic, shared context, and monitoring.

Because AI agents in finance often interact with sensitive customer data and operate within a heavily regulated sector, strong security and governance protocols are crucial. By following secure AI practices, such as deploying AI systems privately, banks and financial institutions can help ensure that AI agents are secure, transparent, and governed by defined guardrails.

##### Top agentic AI applications in finance: Examples and use cases

In our work with leading financial services firms, we've identified several agentic AI use cases in finance that provide a strong starting point for institutions looking to apply AI agents to real-world workflows.

###### AI agents for workflow optimization in financial services

These AI agents assist financial professionals by accessing common business software tools and carrying out actions across them, such as email and shared databases.

Here are two examples of how workflow optimization agents could be deployed.

###### Assisting wealth managers and financial advisors

Despite having access to a range of basic automation tools, wealth managers still spend a significant amount of time reviewing news, interpreting the impact on client portfolios, and crafting personalized updates. This tedious manual work cuts into valuable time they could spend with clients. For example, the average financial advisor spends nine hours a week on administrative tasks alone. Moreover, a looming shortage of wealth managers in the US threatens to put more strain on their ability to give personalized service.

Instead of manually pulling together reports and composing emails, a wealth manager could build and deploy an AI agent using a secure AI workspace, like North, to optimize their workflow. By connecting to market news feeds, email accounts, and client databases, an AI agent — or even a combination of agents, each performing individual tasks — could generate a market or company news summary tailored to a client’s profile, such as risk tolerance, holdings, or geographic exposure. The agent could then turn that summary into a PDF report and draft a client email for the wealth manager to review. The time saved would allow the wealth manager to allocate more time to high-impact tasks, such as personalized check-ins and strategic client sessions.

###### Streamlining sales...