Financial services firms know they need to move fast to stay competitive. Yet time-consuming manual processes are still the industry norm rather than the exception.

From sales follow-up opportunities lost in spreadsheets to wealth managers’ valuable face-to-face time with clients eroded by email and news monitoring demands, the competitive costs add up. Nearly 47% of banking professionals identify manual data collection as their biggest bottleneck in meeting reporting deadlines, for example. And some 42% of banks, credit unions, and other lenders still often rely on manual processes for compliance, exposing them to regulatory risks.

Knowledge workers and professionals at banks and other financial firms need a new productivity leap—and they can now turn to AI agents to provide it. AI agents are no longer abstract prototypes—they are already demonstrating their value across financial roles by interacting with various tools to streamline workflows and eliminate tedious, time-consuming tasks.

We’re seeing three core applications of AI agents in the financial sector: workflow optimization, customer service enhancement, and compliance management.

First, let’s define what AI agents are in the context of financial services.

What are AI agents in financial services?

Enterprise AI agents help financial professionals and teams by automating or streamlining processes to complete specific tasks, based on prompts and triggers. Agents could support relationship management and financial advisory services, or provide banks with more peace of mind and lower costs by helping workers monitor fraud, ensure compliance standards are met, and assess financial risks.

Powered by large language models, AI agents can reason about a goal, select from approved tools, and carry out multi-step tasks for knowledge workers.

Each agent can be defined by:

A specific task or job to be done
The tools and data it can access (email, CRMs, and other banking software)
The guardrails and policies it must follow

Today, we mainly see two types of AI agents—each suited to different needs.

The first are conversational agents. These are chat-style copilots for open-ended cognitive tasks like asking questions, exploring data, or drafting analysis. Given a prompt, they plan a response path, pull data from search tools or internal systems, and synthesize a reply for your review.

The second are automation agents (also known as agentic workflows). These agents are built for specific business tasks where control, consistency, and compliance are essential. They follow predefined workflows to complete jobs like Know Your Customer (KYC) evidence gathering. They can make decisions within the flow but can’t go beyond it.

Multi-agent orchestration is the latest emerging trend. Multi-agent systems split work across small, focused agents that interact with each other. One might plan, another retrieves data, another analyzes or executes, and a checker reviews the result. This applies to both conversational agents and governed agentic workflows: a chat frontend can hide a swarm of helpers, and a strict process can orchestrate specialists step by step. The upside is modularity and resilience. The downside is coordination overhead: you need routing logic, shared context, and monitoring to keep the system aligned and safe.

Because financial AI agents handle sensitive customer data and operate in the heavily regulated world of banking, strong security and governance protocols are crucial. By following secure AI practices, such as private deployment, banks and financial institutions can ensure that enterprise AI agents are safe, transparent, and operate within defined guardrails.

Top AI agent examples in financial services

In our work with leading financial service firms, we've identified several types of AI agents that have garnered momentum and provide a strong starting point for standardizing AI success measures across an institution. If you are wondering where to start. Here's your answer.

AI agents for workflow optimization

These AI agents assist financial professionals by accessing and carrying out actions with common business software tools, such as email and shared databases. Following are two examples of how workforce optimization agents could be deployed.

1. Assisting wealth managers

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 their more valuable time spent with clients. In one survey of over 4,000 financial advisers, nearly a third (28%) said they don’t spend enough time with clients. 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 the company news feed, email account, and client database, an AI agent or even a combination of agents—each performing individual tasks—could be prompted to generate the news summary, tailored to the specific needs of a client (for example based on their profile such as risk tolerance or geographic location). The AI agent can create reports in PDF format and generate email drafts for clients, all under the supervision and direction of the wealth manager. The time saved enables wealth managers to allocate more time to high-impact tasks, such as personalized check-ins and strategic client sessions.

2. Streamlining the sales process

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. Details are inconsistently entered, and opportunities for targeted follow-ups may be missed. A Salesforce survey found that sales reps spend less than 30% of their time actually selling, with the majority of their time spent on tasks such as data entry.

A personalized AI agent can help sales reps both prepare for meetings and process their outcomes more efficiently. Either through conversational agents or as an automated workflow, sales processes can be optimized to individual needs. Agents can 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, it could log notes back into Salesforce and mark the rep’s calendar with follow-up tasks. Financial services firm BNY, for example, has deployed agents that provide clients with detailed product recommendations, freeing up its sales team to answer more complex queries.

AI agents for customer service

These agents support customer-facing employees by retrieving client data, generating responses, and connecting with internal systems, including during live interactions. They go beyond virtual AI assistants by carrying out multi-step actions and operating across multiple tools.

Let’s look at two examples in financial services.

1. Helping bank staff give personalized advice

Faced with customers calling for advice on their personal finances, customer service staff at banks have to toggle between multiple systems to retrieve relevant details such as account balances, past transactions, and the status of mortgages or other loans. They have limited ability to assess trends, cross-reference data, or provide targeted advice or follow-up actions for clients. Some 42% of financial services employees cite fragmented apps as a key barrier to prioritizing tasks and adhering to compliance rules.

An AI agent works during a call or online meeting by pulling the customer’s relevant data, such as credit card transactions, income, and mortgage details, and summarizing it for the customer service rep. It could then analyze the data to spot trends and surface insights for the rep to discuss with the customer. It can, for example, suggest areas where customers could reduce their spending or save more. Following the call, it could immediately draft an email to the customer with a summary of insights and options. For customer reps, the advantage isn’t just the time saved on tedious tasks—it empowers them to act more like relationship managers and handle more complex cases. Fundamental to successful deployment of these agents is interoperability with legacy systems within secure and private environments.

2. Launching new customer services

Banks can leverage agents to create innovative new customer service offerings that not only provide people with accurate information quickly, but also carry out follow-up actions and connect them with other resources. In one recent survey, 84% of bank customers said they would likely switch to a bank that provides timely, relevant advice to improve their financial health.

Capital One, a major provider of auto loans, for example, launched a new service powered by an AI agent to help customers in the market for a new car purchase. Searching for a vehicle is a time-consuming, often frustrating process for consumers. The bank’s AI agent connects to dealers’ inventories in real time, enabling it to answer detailed queries, such as differences between models and the potential value of a trade-in. It can then schedule appointments with car dealers. It benefits car dealers by finalizing appointments with customers who are further along in their buying journey, while Capital One expects its auto loan business to benefit from the more streamlined and convenient experience.

AI agents for enhancing compliance

As a highly regulated sector, financial institutions must uphold strict compliance standards across their operations. AI agents can be a valuable tool in supporting compliance efforts by scanning for and summarizing regulatory updates, accessing compliance policies, and applying them to various scenarios, as well as guiding employees involved in compliance-sensitive work.

Here are two examples of AI agents in action within financial services compliance.

1. Regulatory compliance and reporting

Relationship managers must navigate complex compliance requirements, ensuring that client transactions align with regulations. This includes includes KYC identity verification requirements to combat money laundering as well as sanctions compliance and data privacy laws. But compliance operations often work in silos, resulting in extra manual steps, delays, and friction both internally and with clients. McKinsey found that 40% of banking customers’ onboarding time is spent on KYC processes and account opening procedures.

Customized AI agents can help by orchestrating compliance teams’ existing tools. For instance, if a bank already uses machine learning models to detect potentially fraudulent transactions, an AI agent embedded in the relationship manager’s dashboard could monitor those alerts in real time. It would review flagged items, summarize the most critical risks, and provide explanations based on the specific rules and policies involved.

The AI agent in this case acts as an intelligent intermediary, enabling managers to respond quickly to red flags and other issues while giving compliance teams a consistent audit trail. The result: faster, more confident decision-making, reduced operational friction, and a stronger overall compliance posture.

2. Compliance-aware client content creation

Personal banking and wealth teams must deliver timely, tailored product guidance, whether it’s a new savings plan, an updated mortgage rate, or a portfolio rebalancing suggestion. A major challenge is ensuring that these communications are compliant with the latest internal policies and external regulations, such as fair-dealing and disclosure requirements. Regulators in both the US and the EU have been tightening their scrutiny of banks’ communications with customers in recent years.

An AI agent, or agentic workflow, can be deployed to interpret up-to-date rules and filter product options according to clients’ profiles, objectives, or location. The agents can draft communications that embed required disclosures and policies. In cases where human judgment is needed, an AI agent can flag issues for manual review. This helps institutions ensure consistent compliance standards in their communications, while enabling them to personalize messages and streamline advisors’ workflows.

Productivity potential is only growing

According to McKinsey, agentic AI offers a 20-fold increase in productivity potential. Leading financial institutions are already deploying advanced workflows where multiple AI agents work together, with every step—from data used and decisions made to QA, compliance, and audit reviews—fully documented for a clear audit trail.

The key to these fast, safe deployments for financial institutions is working with an AI platform like North that provides a secure end-to-end stack to create and manage agents across an organization that delivers data privacy, control, and interoperability for scale.

Whether you begin with simple, high‑impact conversational agents or advance directly to full agentic workflows, the trajectory of financial institutions will be permanently changed. The time to act is now.

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