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 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 preparation and follow-up

Sales teams at financial institutions spend a significant amount of time tracking sales leads, preparing for meetings with prospects, and consolidating data across various platforms, including CRMs and Excel sheets. In fact, a 2025 McKinsey survey found that excessive admin, low-quality leads, and difficult meeting prep were the top three challenges faced by frontline banking sales teams.

A personalized AI agent can help sales reps both prepare for meetings and process their outcomes more efficiently. Whether used as a conversational assistant or as part of an automated workflow, agents can be tailored to each rep’s sales process. Agents could monitor upcoming meetings on the rep’s calendar, pull the client’s profile from a CRM platform like Salesforce, and summarize any previous interactions. Post-meeting, an agent could log notes back into Salesforce and mark the rep’s calendar with follow-up tasks. Financial services firm BNY, for example, has used agents to help sales teams prepare more relevant client recommendations while reducing manual internal coordination.

AI agents for customer service and client support

These agents support customer-facing employees by retrieving client data, generating responses, and connecting with internal systems, including during live interactions. This can help bank staff respond with more context, spend less time toggling between systems, and follow up more consistently after customer conversations.

Here are two examples of how agents can support customer teams.

Helping bank staff deliver more personalized support

Customer service teams often need to pull information from multiple systems before they can answer customers’ finance questions, from account balances and past transactions to mortgage or loan details. In live customer interactions, that fragmentation can make it difficult to spot patterns, cross-reference details, or identify the most relevant next step for each customer.

During a call, an AI agent could support the rep by pulling together relevant customer data, such as transaction history, income, product holdings, and mortgage or loan details. It could then summarize that information in the rep’s workspace and surface follow-up options that align with the bank’s policies. Following the call, the agent could draft a follow-up email for the rep to review, capturing what was discussed on the call, relevant options, and next steps.

These capabilities don’t just reduce the time service teams spend on tedious tasks; they can also help reps tackle more complex cases and deliver more informed, relationship-oriented support. However, realizing that value depends on AI agents being interoperable with legacy systems within a secure, private environment.

Delivering innovative customer service experiences

Banks can use agents to create new customer-facing services that combine accurate answers with useful next steps. Instead of simply responding to customer questions, these services can help people compare options, take follow-up actions, and connect with the right resources in one streamlined experience.

For example, Capital One, a major provider of auto loans, launched an agent-powered service to help customers shopping for a new car. The agent connects to dealers’ inventories in real time, enabling it to answer detailed questions about available vehicles, model differences, and the potential value of a trade-in. It can then schedule appointments with salespeople. For dealers, the service helps turn customers’ online research into scheduled appointments. For Capital One, it creates a smoother path from car search to auto financing.

AI agents for compliance and regulatory workflows

Financial institutions operate in a highly regulated sector, with strict compliance standards applying across their operations. AI agents can support compliance efforts by retrieving relevant policies, summarizing regulatory updates, checking work against compliance requirements, and escalating issues for review.

Here are two examples of how AI agents can support compliance workflows:

Regulatory compliance and reporting

Relationship managers often sit at the intersection of client service and compliance, especially during onboarding or when client activity requires further review. They may need to coordinate with compliance teams, gather documentation, and respond to questions related to KYC checks and broader anti-money laundering (AML) obligations, as well as requirements around sanctions compliance and data privacy laws. But the teams, tools, and records involved in these processes are often fragmented, creating extra manual steps, internal delays, and a more cumbersome experience for clients.

Customized AI agents can help by connecting existing compliance systems and coordinating the flow of information between teams. For instance, if a bank already uses transaction-monitoring systems to detect potentially suspicious activity, an AI agent connected to the bank’s case-management workflow could help triage incoming alerts for review. It could surface flagged items, summarize key risk signals, and provide context based on applicable policies, monitoring rules, risk thresholds, or escalation criteria.

The AI agent in this case acts as an intelligent intermediary, helping compliance reviewers and relationship managers respond more quickly to alerts, documentation needs, and client follow-ups while supporting a clear audit trail. The result: faster review and escalation, reduced operational friction, and a stronger overall compliance posture.

Compliance-aware client content creation

Personal banking and wealth teams must deliver timely, tailored client communications, whether it’s information about a new savings plan, an updated mortgage rate, or a portfolio review. A major challenge is ensuring that these messages follow the latest internal policies and external regulations, such as fair-dealing and disclosure requirements.

An AI agent, or agentic workflow, could be deployed to retrieve approved policy guidance, draft or check client communications against relevant requirements, and identify which disclosures or review steps apply based on the customer profile, product, and jurisdiction. In cases where human judgment is needed, the agent could also flag issues for manual review. The result is a more consistent, efficient process for personalizing messages while keeping compliance requirements built in.

Build secure, scalable AI agents for finance

As AI tools for finance professionals mature, financial institutions have an opportunity to move beyond isolated copilots and automate more complex, multi-step workflows. AI agents can help teams summarize information, coordinate handoffs, draft outputs, surface risks, and support compliance-heavy processes — but only when they are deployed with the right controls.

That means choosing an enterprise AI platform like Cohere North that can connect to approved data and systems, support clear access controls, preserve auditability, and scale agentic workflows across teams.

Enterprise AI for financial services

Whether firms begin with simple, high‑impact conversational agents or more advanced agentic workflows, the goal is the same: help finance professionals work faster and more consistently while keeping human oversight, compliance, and trust at the center.

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