Industry innovations and market developments naturally pressure firms across the financial industry to work faster to gain a competitive edge while maintaining flawless delivery and accuracy at scale. Many firms turned to automation as a solution, but found that few traditional and generative AI solutions could handle the volume, complexity, and accuracy requirements of finance workflows. 

However, the introduction of specialized AI agents in finance is making widespread automation possible. Agents excel at executing mechanical workflows involving similar sets of repetitive tasks, such as running comparables analysis for different companies and industries. When this structural discipline is paired with AI’s ability to reason over large datasets (for example, analyzing public company filings), the result is a powerful augment for finance professionals.

In this article, we’ll break down what AI agents are, how they work, concrete examples of how they benefit the finance industry, and key factors to consider when implementing AI agents to avoid risks and maximize upside.

What Are AI Agents in Finance?

AI agents are one of the most recent developments for AI in finance, used to complete end-to-end workflows in a repeatable manner. Unlike traditional and generative AI, which require extensive human input to complete individual tasks, AI agents can execute complex, multi-step workflows (for example, building a complete investment committee memo) with minimal human input. 

By compressing hours of manual tasks into one click, AI agent systems handle tedious and repetitive drudgery so that professionals can spend more time on the strategic work that makes a difference for their firm, whether that means refining pitches as an investment banker or finding alpha as a hedge fund quant.

Overreliance on automation can introduce risk for firms, so keeping humans in the loop is a non-negotiable across the board. AI agents support human oversight through detailed audit logs that document their work, enabling finance professionals to manually review their reasoning and verify accuracy.

How Do AI Agents Work?

AI agents perceive all context relevant to their task, reason over that context to decide an ideal course of action, act independently on that decision, and learn from the result to improve future performance.

Below is a detailed breakdown of how AI agents work and what it means for finance professionals:

  • Perception: AI agents can ingest and understand vast amounts of structured and unstructured data simultaneously, leveraging a combination of natural language processing and advanced mathematical reasoning. This means they can seamlessly synthesize quantitative data (such as quarterly reports, SEC filings, and financial models) with qualitative information (such as call transcripts, management commentary, and news reports).
  • Reasoning: With all information ingested, an AI agent can make a context-informed decision on how to proceed with a task. They check real-time information across internal and external sources, test assumptions, and determine which tools they need to achieve their goal. For example, agentic AI could uncover hidden patterns across an entire virtual data room (VDR) and cross-reference them with public market data as part of an AI-powered due diligence workflow.
  • Action: Once analysis is complete and the decision is made, finance agents can execute every step of the plan from end to end without needing a human to prompt them along the way. While generative AI platforms often require dozens of detailed prompts (including iterations and refinements on each one) to complete a full workflow, AI agents need limited steering to do it all automatically, saving knowledge professionals hours of time.
  • Learning: AI agents index past outputs, decisions, and feedback to learn how to produce final outputs more efficiently. Where generative AI solutions only change their methodology when prompted, agentic AI implements changes and improvements in real-time. This results in AI systems that can actually conform to the finance industry’s rigid quality standards, with outputs that progressively improve each time they’re generated.

How Are AI Agents Being Used in Finance? 4 Key Use Cases

Firms across the finance industry are equipping teams with AI agent platforms like Hebbia’s Matrix to realize competitive advantages at scale. Below, we explore four areas where finance agents are proving pivotal.

1. Investment Banking

In investment banking, flawless deliverables that stand up to scrutiny and short turnarounds are not just encouraged but expected. Agents are helping investment banking firms accelerate deal timelines by increasing analyst capacity, streamlining reviews, and rapidly identifying strategic opportunities. Agentic AI for investment banking can handle the majority of the repetitive analysis and administrative work inherent to the industry.

Examples include:

  • Building first-pass deliverables like pitch decks, confidential information memorandums, and strip profiles that conform to your firm’s templates and branding standards.
  • Creating and updating financial models with AI financial modeling technology, including populating historical financials to speed up model creation.
  • Surfacing risks and creating due diligence questions, allowing professionals to identify contract clauses that diminish value or threaten integration, as well as helping them ask the right questions to inform negotiations and deal structuring.
  • Generating buyer lists by screening thousands of potential buyers to build a shortlist complete with rationale for strategic fit and synergistic potential.

2. Private Equity

Private equity teams often have a mountain of administrative work to do and seemingly no time to do it. Agents help reduce that administrative burden, enabling professionals to keep up with tight timelines for analysis and deliverables so firms can find and execute high-quality investments at a greater scale.

Examples include:

  • Automating deal sourcing by synthesizing market research, historical deal data, and proprietary documents to rapidly identify high-probability targets that fit your firm’s investment profile.
  • Extracting key diligence information across multiple data sources, like VDRs with thousands of private documents and public market data, in order to validate a target company’s growth story and trajectory.
  • Reasoning over disparate data sources to find key insights that others might miss, like customer concentrations, compliance risks, and other trends that impact investment theses and portfolio management.
  • Synthesizing takeaways into first-pass deliverables, like investment committee memos and client decks.

3. Public Equity

For public equity investors like hedge funds, alpha is everything. It often comes from unique insights across structured data and unstructured documents, including public macroeconomic and market data, private call transcripts, and SEC filings. The analysis that goes into finding alpha can often take months, but AI agents in finance can dramatically improve speed to insight.

Examples include:

  • Conducting rapid textual analysis across various sources and updating investment theses and financial models where needed.
  • Reasoning across thousands of documents to surface nuanced, context-informed takeaways—from broad patterns like internal sentiment shifts to granular metrics like gross margin—to update forecasts and inform strategies.
  • Monitoring and analyzing real-time market data to detect signals and anomalies that dynamically adjust portfolio positions and risk models.

4. Credit

Credit professionals need to scour contracts, reports, legal documents, and more to identify and mitigate potential downside risks. Often handling numerous simultaneous deals on tight deadlines, these investors may find themselves buried in thousands of documents and struggling to keep up. AI agents in finance remedy this by automating the most time-consuming parts of credit workflows.

Examples include:

  • Extracting, benchmarking, and comparing key covenant terms, contractual obligations, and performance data to build and update risk assessments across an entire portfolio.
  • Analyzing credit agreements to assess leakage and default risk.
  • Building investment committee memos that emphasize potential risks, downside scenarios, and mitigation strategies.

How Do AI Agents Benefit the Finance Industry?

Below are the three main benefits of AI agents in the finance industry.

Increased Scalability

When it comes to scaling workflows, AI agents offer firms a cost-effective alternative to increasing headcount. Firms can position themselves to broaden coverage, adapt to market shifts, and quickly capitalize on opportunities by augmenting their teams rather than expanding them.

Junior-level analysts can offload repetitive, time-consuming administrative work to AI agents, which act as a force multiplier. Meanwhile, senior personnel can lean on agents as on-demand research partners to surface deal opportunities, uncover hidden insights, and scale decision-making. 

Improved Accuracy

AI agents are not bound by the same cognitive limitations that humans are — they can reason over thousands of documents to pull insights and identify hidden patterns ahead of the market without experiencing fatigue, information overload, or qualitative drift.

This makes them ideal for handling painstaking, admin-heavy workflows where fundamental analysis errors that slow down review cycles and formatting gaffs that cost firms credibility are most likely to occur.

Quickened Speed to Insight

AI agents built for financial analysis can compress hours-long tasks into minutes and weeks-long tasks into days, dramatically reducing time-to-insight for firms that rely on their ability to capture alpha before their competitors.

Using agentic AI, high-level patterns (such as sentiment shifts, market trends, and customer retention trajectories) and granular details (such as specific contract clauses, financial performance metrics, and management comments) buried across disparate data sources can be identified and synthesized into first-pass deliverables in just a few clicks.

Key Criteria for Implementing AI Agents in Finance Workflows

Implementing AI agents into finance workflows requires a careful approach, ensuring that the agents conform to industry standards and firm-specific expectations. Below are four criteria to consider when evaluating AI agents for your firm:

1. Maintains Accuracy at Scale

High-volume finance workflows leave no room for accuracy drift. To scale effectively, select an agentic AI platform that prioritizes accuracy and reliability with source-linked outputs, in-line citations, and immutable audit logs. These features are critical to avoid compromising on key quality standards.

Many AI agents are prone to frequent hallucinations, faulty reasoning, and outputs without explanation. These can create production bottlenecks by reducing trust, impeding auditability, slowing review cycles, and even presenting compliance risks. Vet agentic AI platforms to ensure that their outputs are as good or better than what a human would normally produce.

Pro tip: When working with AI agents, implement human-in-the-loop checks to vet each of their outputs, especially for higher-stakes workflows and decisions.

Hebbia’s agents use proprietary iterative source decomposition (ISD) technology to link every output to a source, with clickable in-line citations that lead to the precise passage, cell, or clause it was pulled from. Book a demo to see why leading firms trust Hebbia’s agents to automate their workflows.

2. Synthesizes Public and Private Data for Full Context

Across the finance industry, professionals are often tasked with producing unique, actionable insights by synthesizing massive volumes of private documents with real-time public data.

Replicating this depth of analysis requires an agentic AI platform that can ingest and reason over a theoretically infinite number of documents while also integrating with public data sources. This is critical for producing objective outputs that account for present and historical context across time, sectors, and geographies.

Without the full spectrum of data, agents may produce biased or generic analysis that misses the nuance of your firm’s historical data, fails to account for breaking new developments, or misrepresents granular details, leading to outputs that are factually correct but strategically useless.

Pro tip: Look for solutions that integrate with major sources of public financial data, such as FactSet, S&P Capital IQ, and PitchBook, as well as cloud storage platforms like Microsoft SharePoint and Google Drive.

3. Adapts to Proprietary Workflows and Methodologies

Choose an agentic AI platform that lets users customize and create agents to meet the nuances of firm-specific needs and individual workflows. That way, firms can seamlessly automate their existing processes with little-to-no change and avoid disrupting proven methods.

Pre-built, “one-size-fits-all” agents for finance are common and can get production rolling quickly. However, firms often run into issues when they find that their agents can’t be changed to meet their needs. This lack of flexibility means the agent dictates the workflow, not the firm, leading to generic strategies that are easily replicated and may dull your competitive advantages.

Pro tip: Seek out platforms that allow you to create your own proprietary agents from scratch, ideally by uploading a document or through simple natural-language programming.

4. Features Enterprise-Grade Data Security

In finance, world-class data security is non-negotiable. Find an AI agent system that includes enterprise-grade features such as AES-256 and TLS encryption, role-based access controls (RBAC), multi-factor authentication, and isolated deployment options.

These features are necessary for firms handling proprietary institutional knowledge, personally identifiable information (PII), financial performance data, legal contracts, confidential executive commentary, and more. Leaks, breaches, or mishandling can cause loss of trust, reputational damage, and erosion of competitive advantage.

Pro tip: Find a provider with a verifiable zero data retention policy to ensure that large language models (LLMs) aren’t being trained on your proprietary data.

Capture an Edge with Hebbia’s Agentic Workflows

Combining the ability to execute complex, repetitive workflows with in-depth reasoning over massive datasets, AI agents in finance are capable augmentors that yield tangible improvements to speed and accuracy at scale, unlike most traditional and generative AI solutions.

A core part of Hebbia’s platform is customizable AI agents that automate complex finance tasks with unmatched accuracy, integrate with a wide range of data sources to produce context-informed outputs, and seamlessly adapt to firm requirements and individual needs.

Book a demo to see why leading firms across the financial industry choose Hebbia as their platform to harness the power of AI agents.