How are AI agents transforming the financial industry?

Sep 2, 2025

The financial industry is undergoing a profound transformation. AI agents are changing how work is done by eliminating tedious manual processes and significantly reducing operational delays.

These intelligent systems can read documents, extract data, and validate information with remarkable accuracy and speed. Fraud detection is faster than ever. KYC procedures are completed in hours instead of days. Invoice reconciliation runs entirely on its own.

So how exactly are these AI agents redefining finance? In this article, we will explore the specific ways these technologies are transforming everything from risk management to compliance reporting, and what this shift means for the future of financial operations.

What is an AI Agent?

What exactly are AI agents? Picture having colleagues who can process information, reason through complex situations, and respond intelligently to changing circumstances. These aren't your typical automation tools that blindly follow commands, they're systems that understand context and evolve with experience.

The finance sector is where these capabilities really shine. Mountains of paperwork get processed effortlessly, regulatory requirements stay current automatically, and questionable transactions get spotted before becoming headaches. Plus, they communicate naturally—no more dealing with clunky, mechanical interfaces.

Here's what makes them game-changers: they function like additional workforce members with unlimited energy. Beyond task completion, they deliver valuable insights, accelerate decision-making processes, and build infrastructure that grows alongside your organization. Your human experts get to concentrate on strategic thinking and the innovative work that requires genuine creativity.

What’s an AI Agent and what is not

Before diving deeper, let's clarify what we mean by AI agents and how they differ from other automation tools.

Type	What it does	Best for	Limits
Chatbot	Answers FAQs from a script or a small knowledge base.	Simple customer queries.	Brittle to change, no reasoning.
RPA script	Replays clicks and keystrokes.	Stable, high-volume tasks on legacy UIs.	Brittle to change, no reasoning.
LLM Q&A	Retrieves and summarizes content.	Research, policy lookups.	Reads but does not act, prone to stale sources.
AI Agent	Plans, retrieves, calls tools, and asks for approval when confidence is low.	Variable workflows with documents and decisions.	Requires grounding, policies, and observability.
Hybrid (RPA + Agent)	RPA handles the backbone, agent handles exceptions and narrative.	Processes with predictable core and messy edges.	More design effort, needs clear handoffs.

Benefits of using AI agents in the financial industry

Why are so many financial companies jumping on the AI agent bandwagon? The answer lies in what they're actually seeing happen. We're not talking about futuristic promises here, these are concrete improvements already transforming entire departments. Let's look at what's really going down:

Main advantages:

Tedious work disappears: Data entry, document reviews, and report generation happen automatically, letting your people tackle more meaningful challenges.
Information gets processed instantly: Massive data volumes turn into actionable insights within seconds, not hours or days.
Suspicious activity gets caught early: Unusual transaction patterns trigger alerts before they become costly problems.
Regulatory headaches become manageable: Compliance checks happen continuously in the background, keeping legal risks at bay.
Communication feels natural: Teams and customers interact through conversational interfaces that actually understand context.
Performance keeps improving: These systems get smarter with every interaction, constantly refining their approach.
Human mistakes become rare: Complex tasks get executed with machine-like consistency, eliminating those costly slip-ups.
Growth doesn't require massive hiring: Companies can handle increased workloads without proportionally expanding their workforce.

What are the top use cases of AI agents in finance?

Where are financial companies actually using AI agents? The applications span across departments, from customer service to compliance.

Let's look at the most popular use cases happening right now.

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.

Industry	Finance
Department	Investment Research Department
Persona	Investment Analyst
Problem	Investment memos take a long time to produce. Analysts must manually sift through documents and perform analysis.
Solution	The Investment Memo Generator automatically writes investment memos for analysts. The agent leverages web and document sources, and uses multiple LLMs to write different sections of the report.
User Interface	Form
LLM	Anthropic - Claude 3.5 Sonnet. 4 instances of Claude are used in a workflow — each has its own unique prompt.
Data Sources	Knowledge Base, web search, LinkedIn, document upload (financials), document upload (pre-diligence)
Actions	Searches the web and user documents. LLMs produce an investment memo based on the data.
Time to Launch	Medium
Benefits	Reduces research time from 8 hours to 15 minutes Analysts can spend more time focusing on valuable tasks Firm can invest in more companies, leading to higher profit margins

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.

Industry	Finance
Department	Investment Research Department
Persona	Investment Analyst
Problem	The buy side investment memo and sell side investment memos often have dissimilarities. Investors need to understand what those differences are before they make a decision.
Solution	This AI agent asks the user to upload the sell and buy side IMs. A prompt instructs the LLM how to compare them.
User Interface	Form
LLM	Azure 1 - GPT - 4o
Data Sources	File upload 1 (buy side investment memo), File upload 2 (sell side investment memo)
Actions	Files are uploaded to LLM. The prompt instructs the LLM how to analyze them. The output is a report that compares the two IMs.
Time to Launch	Medium
Benefits	Reduces comparison time from 4 hours to 5 minutes Analysts can spend more time focusing on mission-critical tasks Firms can make more accurate investment decisions, leading to higher profits

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.

Industry	Finance
Department	Investment Research Department
Persona	Investment Analyst
Problem	Due diligence requires an examination of financial records before entering into a proposed transaction with another party. This process takes a long time when done manually.
Solution	The AI agent performs due diligence with LLMs, with the following inputs and outputs.
User Interface	Form
LLM	Anthropic - Claude 3.5 Sonnet, Open AI - GPT-4o
Data Sources	Web search 1 (Online Market Landscape), Web search 2 (Online Reviews)
Actions	LLMs create web search queries. Queries run through Google Search and results fed into due diligence LLM. Report is written by the LLM.
Time to Launch	Medium
Benefits	Reduces comparison time from 4 hours to 15 minutes Analysts can spend more time focusing on key tasks Firms can avoid making bad investments, saving revenue

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.

Industry	Finance
Persona	Financial analysts
Problem	10-Q/10-K forms hold critical information about a company, but they take too long for investors to analyze.
Solution	This AI agent analyzes a 10-Q or 10-K form that the user uploads and reports on these findings: 1) risk and uncertainties, 2) debts and financing, and 3) performance.
User Interface	Form
LLM	Anthropic - Claude 3.5 Sonnet (x3 instances)
Data Sources	File upload (10-Q or 10-K form)
Actions	Employee uploads a 10-Q or 10-K document. The document is fed into three different LLMs. Each LLM summarizes a different aspect of the report.
Time to Launch	Easy
Benefits	Reduce time spent analyzing 10-Q/10-K forms from 4 hours to 5 minutes Financial analysts can do faster and more accurate assessments of companies Companies can invest in more eligible companies and produce a higher profit margin

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.

Industry	Horizontal
Persona	Research Analyst
Problem	Doing a robust competitive analysis of a company and its competitors is time-consuming, research-intensive, and sometimes error prone.
Solution	The AI agent performs a competitive analysis of a company, including comparisons with its closest rivals.
User Interface	Form
LLM	OpenAI GPT-4o mini (x2)
Data Sources	Google Search + Vector Database
Actions	User enters name of a company. LLM generates questions around the company. Questions are inputted into Google. Results fed into another LLM, which drafts the competitive analysis.
Time to Launch	Easy
Benefits	Perform complex competitor comparisons in minutes rather than hours Learn about competitors and design strategies to outcompete them Analyze multiple competitors in rapid succession and chart out a market strategy

Agent Workflow

Spreadsheet AI Assistant

Building a 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.

Industry	Horizontal
Persona	Business user
Problem	Summarizing complicated spreadsheets is sometimes time-intensive.
Solution	This AI agent summarizes a CSV based on a user’s prompt.
User Interface	Form
LLM	Mistral - Mistral Large 2
Data Sources	File upload (CSV)
Actions	User prompt and spreadsheet are fed into LLM. LLM analyzes and summarizes the spreadsheet based on the user prompt. Summary is shared with the user.
Time to Launch	Easy
Benefits	Empowers business teams to extract insights from spreadsheets without building complicated functions. Saves time by allowing teams to produce summarizations, as opposed to sifting through data. Enables users to retrieve information about spreadsheets without allowing direct access.

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 — 15+ uses cases transforming finance — to learn about more.