AI Agents: Top 6 Use Cases in Finance

At Stack AI, we’ve worked with hundreds of finance companies around the world to build AI agents. 

We’ve helped companies in finance and many other sectors to develop AI agents that solve their business problems. 

And now we’d like to share these finance AI agents with you!

In the following blog, we’ll detail the top 6 AI agents in finance, including how teams are using them.

What is an AI Agent?

An AI agent is a software program designed to operate independently in pursuit of specific objectives. Unlike conventional programs that adhere to predetermined instructions, AI agents can perceive their surroundings, analyze data, and adjust their actions accordingly.

This ability to adapt enables AI agents to function autonomously, resolving issues and making decisions as they engage with their environment without requiring constant user intervention for guidance.

AI agents, AI chatbots, and AI assistants all utilize LLMs to accomplish tasks. However, AI chatbots, including those like ChatGPT, are primarily designed to respond to explicit user prompts. These chatbots complete tasks based on direct user input, but they cannot operate independently.

AI assistants, like Siri or Alexa, are slightly more advanced. They can perform a range of tasks based on voice or text commands, such as setting reminders. 

But these AI personal assistants rely heavily on user input to perform actions. They do not have the ability to work toward long-term goals.

Compared to other AI systems, AI agents are more autonomous and focused on achieving specific objectives. An AI agent decomposes complex tasks into smaller subtasks and executes them in sequential order. AI agents manage tasks on their own, without needing ongoing user input.

AI agents, AI chatbots, and AI assistants are all intelligent agents that utilize instructions. However, they perform tasks in varied ways. While all three share core technology, they differ in terms of autonomy and decision-making power. Unlike AI chatbots and AI assistants, AI agents can work as independent actors toward long-term goals, in dynamic, fast-changing environments.

How Do AI Agents Work?

AI agents operate through a defined process that allows them to autonomously set and complete goals. At a high level, this process involves determining an objective, gathering relevant information, outlining tasks, and performing actions to achieve the desired outcome. 

Unlike traditional programs that follow static instructions, AI agents can dynamically adapt their approach based on new data and changing circumstances. Let’s take a closer look at what this process might look like.

First, an AI agent determines its goal, which is typically set by a user or an external trigger. This goal could be as simple as categorizing incoming emails or as complex as analyzing a large set of financial data for insights. Once the objective is established, the agent acquires the necessary background information, such as pulling data from a company’s database or performing real-time internet searches. The agent uses this information to make informed decisions on how best to approach its task.

Next, the agent outlines the necessary tasks required to reach its goal. It breaks down the objective into smaller, manageable steps, creating a plan of action. For instance, an AI agent tasked with analyzing financial reports might identify tasks like retrieving specific documents, extracting relevant figures, and running comparisons across multiple data sets. 

Finally, the agent performs these tasks autonomously, following the plan it formulated. As the agent progresses, it continuously monitors its progress and adapts its actions based on new data or changes in the environment, ensuring it remains on track for its goal while optimizing its approach in real-time.

AI agents can be classified based on their architectural complexity and how they interact with their environment. Each category is tailored to handle tasks in distinct ways, ranging from simple, immediate responses to complex behaviors that evolve over time. 

Here’s a breakdown of the primary AI agent types:

• Simple Reflex Agents: These agents react directly to specific inputs using predefined rules, without retaining past data. They are well-suited for straightforward tasks that require immediate responses, such as basic spam filtering.

• Model-Based Reflex Agents: Building on simple reflex agents, these use stored information or environmental models to make decisions based on current conditions and past experiences, enabling more context-sensitive actions.

• Goal-Based Agents: These agents focus on achieving specific objectives by evaluating actions and planning steps to reach a defined goal, such as finding the shortest route in navigation systems.

• Utility-Based Agents: These agents evaluate multiple options using a utility function (e.g., speed, efficiency) to select the most optimal action. They are ideal for scenarios like financial trading, where multiple outcomes are possible.

• Learning Agents: The most advanced type, learning agents adapt their behavior over time by using feedback from their actions. This allows them to improve and adapt in dynamic environments, such as advanced spam detection systems.

Each type of AI agent builds upon the previous one, increasing in complexity and capability. This variety allows developers to choose the most suitable architecture based on the task’s specific needs, whether it involves simple routine tasks or complex, goal-oriented behaviors that require adaptability and learning. 

When developing an AI agent, it’s beneficial to consider these different types and balance the desired outcome with the complexity of the build to achieve the best results for your team’s purposes.

Finance Use Cases: Top 6 AI Agents

Investment Memo Generator

The investment memo generator enables finance analysts to automatically generate research memos based on financial documents. 

Typically, this process takes several hours, as the analysts must sift through and analyze a large volume of financial documents and web information. 

This AI agent automates the investment memo production process down from a few hours to a few minutes. 

Agent Workflow

Buy vs. Sell Side Agent

We’ve seen many of our investor customers create buy versus sell side AI agents to compare the financials of differing sides of a deal.

This agent can identify discrepancies between what the buyer claims about a company, and what the seller does.

When done manually, this process can take hours or days, but this AI agent automates the comparison in a few minutes.

Agent Workflow

Due Diligence Assistant

Before investing in a company, investors need to perform due diligence on the business to ensure its viability.

This AI agent performs due diligence on a company and writes a report with all the pertinent information investors need to make informed decisions.

All you need to do is type in the name of the company and the AI agent will perform the process automatically. 

Agent Workflow

10Q/10K Documents Extraction

10-Q and 10-K forms are popular tax documents that can tell investors much about a company and its financial status.

Typically, financial researchers had to sift through these documents and manually extract information. This took too much time and led to errors and inaccuracies.

This 10-Q/10-K document analyzer is optimized to extract key company information from these tax forms, and present the findings to investors in easy-to-read reports. 

Agent Workflow

Competitive Analysis Assistant

Finance professionals need to perform competitive analysis on a wide range of companies, but the process has typically been time-consuming and mistake-prone.

After working with hundreds of leading financial institutions, we’ve seen best-practices for competitive analysis first hand.

The Competitive Analysis Assistant is an AI agent that puts these best-practices into action. The agent provides financial professionals with the competitive insights they need to assess any company they want. 

Agent Workflow

Spreadsheet AI Assistant

The Spreadsheet AI assistant helps financial professionals get the most out of their spreadsheets.

The tool can summarize and aggregate spreadsheet data quickly and efficiently. It can also help turn the data into structured datasets.

This saves financial professionals time by eliminating the manual copy-and-paste drudgery typical of spreadsheets.  

Agent Workflow

See More Use Cases in Our Free White Paper!

The AI agents we highlighted in this white paper perform complex jobs in finance. We hope you’ll use our list of top 6 use cases to build AI agents that solve common challenges in the financial sector. 

But these are only a sliver of the possible use cases in Stack AI. As more teams adopt AI builder tools, AI agents will emerge for thousands of other use cases, and we’ll be here to document them as we encounter them. 

Download our full white paper for free — AI Agents: Top 25 Use Cases Transforming Industries — to learn about use cases in sectors such as healthcare, operations, and much more. ce.